Amazon has heard FinOps practitioners' cries asking for new AI tools, and the answer is Titan and AWS Bedrock.  These new tools provide the same generative AI abilities of generating images like expected from DALL-E, operating like a Large Language Model (LLM) like ChatGPT, and even transcribing audio to text.  But how do these new tools compare to pre-existing ones like Azure's OpenAI? Most importantly, which of these tools is the best financial investment for your organization?  Take it from the cloud experts. Here's your complete guide to AWS Bedrock vs Azure OpenAI so you know the strengths and weaknesses of each tool.  What is AWS Bedrock?   [caption id="attachment_16658" align="aligncenter" width="540"] Source: Amazon[/caption] First, let’s define some terms.  AWS Bedrock is one of the newer AI tools on the block. Bedrock is a fully managed severless offering. Developers can use third-party provider foundation models (FMs) through Amazon's API and personalize accordingly, building out the custom tool of their dreams.  Common Bedrock support models include: Model Family Functionality Max Requests Embeddings (Ada, Text Embedding 3 (Small, Large)) Identifying anomalies, classifying tasks, clustering data, generating recommendations, and conducting searches. 2k-8k tokens DALL-E (2 and 3) Create, modify, or refresh images based on text. 1k-4k characters GPT-3.5 Turbo Sophisticated reasoning and conversation, comprehension and generation of code, and conventional completion tasks. 4k and 16k tokens GPT-4 Advanced reasoning and dialogue, intricate problem-solving, code comprehension and generation, and standard completion tasks. 8k and 32k tokens GPT-4o GPT-4 Turbo with Vision capabilities. Improved responses. 128k tokens Whisper Convert audio to text. 25 MB audio size   What is Azure OpenAI? Azure OpenAI is Azure's solution to keep up with the booming AI market. This partnership between Microsoft and OpenAI means Azure users can use their Azure cloud account to access OpenAI with either an API, a web-based interface, or Python SDK. Azure OpenAI offers slightly different features than OpenAI, including private networking, better security, and co-developed APIs. Model Family Functionality Max Requests Embeddings (Ada, Text Embedding 3 (Small, Large)) Identifying anomalies, classifying tasks, clustering data, generating recommendations, and conducting searches. 2k-8k tokens DALL-E (2 and 3) Create, modify, or refresh images based on text. 1k-4k characters GPT-3.5 Turbo Sophisticated reasoning and conversation, comprehension and generation of code, and conventional completion tasks. 4k and 16k tokens GPT-4 Advanced reasoning and dialogue, intricate problem-solving, code comprehension and generation, and standard completion tasks. 8k and 32k tokens GPT-4o GPT-4 Turbo with Vision capabilities. Improved responses. 128k tokens Whisper Convert audio to text. 25 MB audio size Azure OpenAI vs AWS Bedrock   So,  now that you now what Azure OpenAI and AWS Bedrock are now – let’s take a look at the real question: which is better?  Obvious differences like OpenAI (besides being a much bigger name), the biggest gaps between the two are:  Services offered Accessible models Pricing That said, as of 2024, many brands have endorsed GPT-4o as the leader in terms of quality. But that doesn’t mean it's the clear winner. It all depends on the company and what it is trying to accomplish.  AWS Bedrock vs Azure OpenAI: Services   When we take a look at services, we want to keep in mind four things:  Ease of use: How easy is it for customers to accomplish what they're trying to do? Help documents: How does a customer get help if they have questions? Ability to create: What kind of content can you create? How easy is it to create useful content? Security: Will that data be kept safe? Customers need to give a lot of proprietary data to AI tools. Ease of use Bedrock makes things as simple as possible for users. It’s accessible via API or SDK, and also has plenty of no-code playgrounds, providing a very low barrier to entry. Bedrock is a fully managed service, which means you can count on Amazon to handle the technical details. You'll have more time to focus on fine-tuning and working on Retrieval Augmented Generation (RAG) techniques.  OpenAI is also accessible via SDK and API and offers similar no-code playgrounds to Bedrock. However, since OpenAI is not a managed service, users must be a bit more hands-on.  Help documents Customers flourish in cloud environments where they can easily find the tools they need and access help forums or customer support if they get stuck. A good tool should provide ample documentation to pre-empt questions and a community for users to turn to their peers if they get confused. Customer support lines are a must-have, too, of course!  AWS Bedrock has a growing community and backlog of help documents. It's a pretty new service, so this makes sense, but there's still room for growth.  OpenAI is similar to AWS Bedrock in terms of community and help documents. Since it is an older service, there are more places to ask for assistance, but its still not as robust as our cloud professionals would like.  Ability to create OpenAI provides natural language processing, copy, and code generation, and can also help make other creative assets like images by using its DALL-E integration. It's for copy, chat, and code features, are very similar toGPT-3 and GPT-4 Bedrock, on the other hand, is capable of creating the same but at a larger scale. You can access DALL-E 2 and other models for image creation. For text and code generation and chat,  users can use Jurassic-1, Jumbo, and Bloom.  Security Bedrock prides itself on user data protection. Data is maintained by a service-team escrowed accounts.  Customers have access to tools like Amazon DataZone, a cloud platform management tool that allows managing data across AWS. There’s also Guardrails, a safeguard solution that will redact or block personally identifiable information (PII) and prevent it from being shared with others.  OpenAI, is not quite as robust with its data security offerings. User data is kept secure, but it’s worth looking into 30-day storage policies for those who are especially private. What do we mean by that? Any data you provide to Azure OpenAI is stored for up to 30 days to detect and prevent behavior that violates its code of conduct. If pre-approved or elected to turn off abuse monitoring, exemption from this policy applies.   [CTA id="dcd803e2-efe9-4b57-92d5-1fca2e47b892"][/CTA] AWS Bedrock vs Azure OpenAI: Models   Keep in mind model offerings for tools when picking which is the best for the organization. Make sure to note the following: Supported languages  Supported regions  Max tokens  Training data  Supported languages Bedrock's language varies from model to model. Jurassic supports seven languages. Most other models typically only support English. OpenAI has better language support by far. Speech-to-text has support for over 100 languages, from Spanish to French, to German.  Supported regions Bedrock's supported regions fall short of OpenAI. Here are the regions to access Bedrock: Asia Pacific (Singapore, Tokyo)  Europe  US West  US East  The regions that access OpenAI vary by model, but in general users can access OpenAI as a whole if they're based in:  Australia East  Canada East  France Central  Japan East  Norway East  South India  Sweden Central  Switzerland North  UK South  West Europe  US Max tokens AWS Bedrock's max token number ranges depending on the model type and category. Take LLMs, for instance. Bedrock's models start with 4k to 8k and can increase to as high as 128k tokens, which amounts to about 300 pages of information. This number isn't anything to write home about though – Claude models can go up to 200k tokens, or provide 500 pages of information.  Whereas OpenAI is limited to 4,096 tokens, which is much smaller in comparison.  Training data  All of OpenAI's models were trained until September 2021 except for GPT-4 Turbo, which was trained until April 2023.  Bedrock's Claude model was trained until December 2022, and Jurassic about mid-2022.  AWS Bedrock vs Azure OpenAI: Pricing   The ideal pricing plan should offer more flexibility for those willing to accept higher prices and better rates for those ready to commit. Companies should be offered multiple plans designed to meet their needs depending on where they are in their AI adaptation process. Overall, Bedrock wins out when it comes to pricing. OpenAI costs a bit more, and it also has far less flexible plans.  But we’re getting ahead of ourselves – take a look at the payment plan breakdowns for AWS Bedrock and OpenAI below.  Amazon Bedrock pricing Bedrock pricing rises and falls depending:  Model interference  Customization  Region  Not all models can be customized and only certain models can be purchased with specific plans.  There are two plans to pick from: On-Demand or Provisioned Throughput. On-Demand On-demand is a high-cost, high-flexibility payment plan option. You pay for each usage, with your charges varying depending on your model of choice.  For example, an image generation model charges for each image you produce, whereas a text generation model charges for each input token processed and each output token created.  Here are some of the most common models and the cost per 1000 input and output tokens to give an idea of the numbers you'll be dealing with: Model Price per 1000 Input Tokens Price per 1000 Output Tokens Claude 3.5 Sonnet $0 $0 Claude 3 Opus $0 $0 Command R+ $0 $0 Command $0 $0 Embed – English $0.00 N/A Embed – Multilingual $0.00 N/A Jamba-Instruct $0.00 $0.00 Jurassic-2 Ultra $0.02 $0.02 Llama 3 70B $0.00 $0.00 Llama 3 8B $0.00 $0.00 Llama 2 70B $0.00 $0.00 Mistral Small $0.00 $0.00 Mistral Large $0.00 $0.01 Titan Text Embeddings $0.00 N/A   In comparison, here’s how much it’ll cost you to work with image generation models:  Model Image resolution Cost Per Image - Standard Quality (<51 steps) Cost Per Image - Premium Quality (>51 steps) SDXL 0.8 (Stable Diffusion) 512X512 or smaller $0 $0.04 Larger than 512X512 $0 $0 SDXL 1.0 (Stable Diffusion) 1024X1024 or smaller $0 $0 Titan Image Generator (Standard) 512X512 $0 $0 1024X1024 $0 $0 Titan Image Generator (Custom Models) 512X512 $0.02 $0.02 1024X1024 $0 $0   Provisioned Throughput Provisioned throughput requires commitment to multiple model units. You'll need to know what kind of content you're going to generate. You'll be charged on an hourly basis. Prepare to commit to either a one- or six-month contract. If you're ready to sign up for a much larger workload, this will get you a lower cost. Here's a list of the models you'll typically be dealing with and the associated costs:  Model Price per Hour per Model Unit With No Commitment (Max One Custom Model Unit Inference) Price per Hour per Model Unit With a One Month Commitment (Includes Inference) Price per Hour per Model Unit With a Six Month Commitment (Includes Inference) Claude 2.0/2.1 $70 $63 $35.00 Command $50 $40 $24 Embed - English $7 $7 $6 Embed - Multilingual $7 $7 $6 Llama 2 Pre-Trained and Chat (13B) N/A $21 $13 Llama 2 Pre-Trained (70B) N/A $21.18 $13.08 SDXL1.0 (Stable Diffusion) N/A $50 $46 Titan Embeddings N/A $6.40 $5.10 Titan Image Generator (Standard) N/A $16.20 $13.00 Titan Multimodal Embeddings $9.38 $8.45 $6.75 Titan Text Lite $7.10 $6.40 $5.10 OpenAI pricing OpenAI has only one pricing open: pay-as-you-go. There is ease in simplicity, but this also means no access to those lower prices by committing to more long-term usage. To customize models, extra costs apply. Prices vary depending on the region. Pay-as-you-go pricing OpenAI's pay-as-you-go pricing varies depending on the model. Payment is per prompt token and completion token for text generation, per token for embeddings, and per 100 images generated for image models. Here’s a preview of what OpenAI’s pricing looks like per model:  Model Price for 1000 Input Tokens Price for 1000 Output Tokens Ada $0 N/A Text Embedding 3 Large $0 N/A Text Embedding 3 Small $0 N/A Babbage-002 (GPT Base) $0 $0 Davinci-002 (GPT Base) $0 $0 GPT-3.5 Turbo $0.00 $0.00 GPT-4 $0 $0 GPT-4 Turbo $0.01 $0.03 GPT-4o $0.01 $0.02 GPT-3.5 Turbo $0.00 $0.00 GPT-4 $0.03 $0.06 GPT-4 Turbo $0.01 $0.03 GPT-4o $0.01 $0.02   Here’s the cost for image for the most common image generation models: Models Resolution Price (per 100 Images) Dall-E-3 1024X1024 $4 Dall-E-3 1024X1024 $8 Dall-E-2 1024X1024 $2   AWS Bedrock vs OpenAI: Summary   Here’s a table that breaks down each feature and how AWS Bedrock and OpenAI compare:  Feature AWS Bedrock Rating Azure OpenAI Rating Winner Ease of use API & SDK access 7/10 API & SDK access 6/10 Bedrock Help documents Some community & documents 7/10 Some community & documents 6/10 Bedrock Ability to create Access to wide range of creative models 8/10 More limited access to creative models 6/10 Bedrock Security High security 8/10 Medium security 6/10 Bedrock Supported languages Varies. Jurassic supports 7 languages 5/10 OpenAI supports 100+ languages 7/10 OpenAI Supported regions Asia Pacific, Europe, US West & East 5/10 Varies by model but widely available 8/10 OpenAI Max tokens <=200k tokens 6/10 4,096 tokens 4/10 Bedrock Training data Latest trained until 12/2022 7/10 Latest trained until 4/2023 8/10 OpenAI Pricing On-demand and Provisioned Throughput 9/10 Pay-as-you-go 4/10 Bedrock   Overall, AWS Bedrock wins, though it depends on what you're is trying to accomplish. Azure OpenAI offers support for more languages and more regions, and its models have been updated most recently.  But Bedrock can't be beaten for its pricing plans, tokens, and wide range of models to pick from though.  Our final verdict: For a flexible, robust tool that works with all kinds of machine learning (ML) frameworks, choose AWS Bedrock. For enterprises looking to connect an AI tool to other Azure services, Azure OpenAI is likely the best option. [CTA id="82139892-d185-43ce-88b9-adc780676f66"][/CTA] Get 100% visibility into your new cloud AI tools   If you’re worried about keeping your data secure while you open up a new Bedrock or OpenAI account – or, worse, you’re unsure how you’ll manage your budget with these new additions, we have just the solution for you. We have some experience managing budgets during OpenAI to Bedrock migrations, so know you’ll be in expert hands.  The good news is that it’s not necessary to commit to AWS Bedrock’s full cost… not with the right tools. Cloud prices don’t need to be a surprise with the right cloud cost management tool. Getting 100% visibility into your cloud spending can help enterprises and MSPs reduce costs. Tools like Anodot capture minute data changes down to the hour for up to a two-year period for an entire multi-cloud environment. With a dashboard that shows all of the cloud spending in one place, with AI-powered recommendations and forecasting. That’s Anodot.  And it gets better. Anodot has been demystifying cloud costs since day one. We’ve made it our mission to address poor cloud spend visibility, and by partnering with Automat-it, we’ve created the perfect aid to identify hidden prices, and poor cost monitoring and reporting: CostGPT. This new tool uses AI to address your cloud price fluctuations and can help you save up to 30% on spend.  Want a proof of concept? Talk to us to learn how much you can save with Anodot’s tools.  

Imagine standing on stage at AWS re:Invent, surrounded by the industry’s best, as Anodot is named the 2024 Rising Star Technology Partner. It’s a proud moment for our team and a testament to the innovation driving our platform. [caption id="attachment_16811" align="aligncenter" width="548"] CEO David Drai celebrating the win with the Anodot team at AWS Partner Awards[/caption] Our team already had an incredible week at the event, highlighted by: Hosting an exclusive dinner at Toca Madera, reconnecting with colleagues over delicious Mexican food and drinks. Connecting with many partners, prospects, and clients who share our passion for innovation in cloud cost management. Participating in the FinOps panel discussion led by Senior Technical Account Manager at AWS, Judith Lehner, where ideas were shared about the current state of cloud FinOps. These highlights alone made it an unforgettable week, but imagine our excitement when our CEO, David Drai, and Cloud Alliance Manager, Moran Onger Ben-Baruch, were called onstage to accept the AWS Rising Star Partner Award! This recognition from AWS is a proud moment for the entire Anodot team. Using AI and ML, we’re transforming cloud financial management (CFM) with automated tools like savings recommendations, cost allocation, and complete cloud cost visibility. These solutions enable MSPs and enterprises to innovate, thrive, and maximize the value of the AWS ecosystem. But how did our CEO and Cloud Alliance Manager find themselves onstage, applauded by FinOps leaders for Anodot’s game-changing approach to cloud financial management? This is how our platform's innovation in optimizing multi-cloud environments with AI-powered solutions earned us an award for providing AWS customers with a new level of efficiency and insight in cloud cost management. Anodot Refresher: Who We Are and How We Got Here   Founded in 2014, we successfully built a powerful BI platform empowering anomaly detection for hundreds of prominent customers.  Fast forward to 2022, we introduced an integrated cloud cost management solution packed with enterprise-grade features, allowing  MSPs and businesses to take control of their cloud spend. And now, in 2024, we’re celebrating a major milestone as recipients of the AWS Partner Awards. From client-specific rebilling to 100% cloud cost visibility and showback/chargeback features, we deliver unmatched precision in AWS cloud cost management. Strategic partnerships have played a vital role in our journey, extending our capabilities and bringing added value to our customers. Key partnerships include: UBTECH and Anodot: Together, we enhance UBTECH's client-partner model with financial visibility and cost optimization for Azure clients. Anodot and YäRKEN: Through this strategic partnership, we offer seamless management of on-premises and private cloud costs via a unified platform. Anodot and Greenpixie: By integrating Greenpixie’s ISO-verified cloud emissions measurement, our platform now delivers insights into both cloud costs and carbon emissions, all in one place. Today, we’re setting the standard for AI-driven automation in cloud cost optimization. Our mission is to push boundaries, innovate relentlessly, and ensure enterprises and MSPs achieve unparalleled efficiency, scalability, and savings with cloud investments. The 2024 EMEA AWS Partner Awards and Rising Star Technology Partner Category   The 2024 EMEA AWS Partner Awards celebrate the achievements of AWS Partners across Europe, the Middle East, and Africa (EMEA). These awards recognize exceptional performance, innovation, and dedication to helping customers succeed with AWS technologies. Spanning a region with diverse industries and unique cloud adoption challenges, the awards highlight partners excelling in technology, consulting, and customer solutions. They showcase contributions to the AWS ecosystem and the advancement of cloud technology adoption across a wide array of markets and use cases. Winners are selected through a rigorous evaluation process conducted by Canalys, an independent analyst firm. This process leverages data-driven metrics from the past year to assess partner performance and impact. Being named a Rising Star Technology Partner reflects our growing influence in the EMEA region, where we help MSPs and enterprises optimize cloud costs, enhance spending management, and achieve greater operational efficiency within the AWS ecosystem. The AWS Award Adds to Anodot’s 2024 Accolades and Success   [caption id="attachment_16810" align="aligncenter" width="333"] The 2024 AWS Rising Star Partner of the Year (Technology) trophy awarded to Anodot[/caption] This recognition adds to a year of milestones for Anodot, underscoring our platform's innovation and impact on driving success for some of the world’s leading enterprises and cloud solution providers. Named Visionary in the 2024 Gartner® Magic Quadrant™ for CFM Tools   Gartner recognized Anodot for its advanced capabilities in cloud cost management, tailored specifically for MSPs and enterprises. The report highlighted our strengths in precise, real-time cost control, client-specific rebilling, and actionable insights across complex, multi-cloud environments. We’re especially proud of innovative features like Cost Incident Detection and Showback/Chargeback, which are helping MSPs and enterprises save both now and in the future. See how Gartner recognizes Anodot’s leadership in cloud cost management. Chosen by monday.com to Boost FinOps Efficiency   monday.com, renowned for its premier work management platform, relies heavily on AWS to power its global operations with high scalability. By partnering with Anodot, monday.com transitioned from manual FinOps processes to a fully automated solution, leveraging our automated recommendations to optimize cloud cost management. "After more than two years with Anodot, their ability to quickly adapt and consistently implement innovative features like automated recommendations has been a game-changer for us. This long-term partnership continues to be exactly what we were looking for." — Yariv Shoshany, TechOps Team Lead at monday.com Discover how Anodot helped monday.com transform their FinOps strategy. What’s Next for Anodot   Being recognized as an AWS Partner is an honor, and we’re proud that our technology continues to deliver precise and impactful results in cloud cost optimization. As we look ahead to 2025, our focus remains on innovating FinOps practices, automating processes, and reducing manual workloads to help our customers thrive in the cloud. "Receiving the 2024 AWS Rising Star Partner Award in Technology underscores the growing importance of FinOps and advanced cost management tools in today's cloud landscape." — David Drai, CEO of Anodot [caption id="attachment_16809" align="aligncenter" width="288"] Anodot's 2024 AWS Rising Star Partner of the Year (Technology) Award for Israel.[/caption] Learn more about our AWS win.

The future is AI. That's a fact, and all the major cloud corporations are taking notice and investing in generative AI offerings to serve their customers better.  Microsoft Azure has invested in OpenAI's ChatGPT, Google has Vertex AI, and Amazon has created Bedrock.  But what exactly is AWS Bedrock? And, most importantly, how much will it cost? Will this generative AI be an easy investment, or will you have to break the budget to squeeze it in?  What is AWS Bedrock?   [caption id="attachment_16793" align="aligncenter" width="512"] Source: Amazon[/caption]   To get started, let’s define some basic terms. AWS Bedrock is a fully managed service designed for developers to create generative AI applications.  Since AWS Bedrock comes with foundation models sourced from other leading AI companies like Anthropic, Meta, and Cohere, developers don't have to worry about the learning process for your new generative AI application. Depending on your chosen foundation model, you can access different tools and infrastructure to build new models.  And no, you don't need to worry about data security. AWS Bedrock prides itself on its integrity and user privacy; all data is kept 100% secure 100% of the time.  How does Amazon Bedrock work?   AWS Bedrock works by supplying dev teams with a wide range of foundation models to create generative AI tools that can generate anything from images to code to copy and more.  All you need to do to get started is:  Pick the foundation model that best fits your need (ex: if you’re looking to build a custom model to train bank customer onboarding, you’ll probably want to go with an Anthropic model like Claude 3 Sonnet or Titan). Send an API request to ensure your data has been sent to the model Ensure the model has received your input. Wait for your model to generate your next blog copy, line of JavaScript, or branding image.  Yes, it’s that easy!  AWS Bedrock pricing   The harder part is AWS Bedrock pricing.  Like most cloud providers, AWS Bedrock offers two pricing models: on-demand, a pay-as-you-go structure, and provisioned throughput, a pricing structure where you pay for a one-month or six-month commitment for a set amount of services.  We'll go into more detail about these different structures below, but before we get into that, know that the following are all factors that might negatively influence your bill:  Foundation model provider: Different FMs charge different amounts for input/output tokens. For example, image FMs typically cost a bit more.  Input and output token usage: Every time you send a request into an FM, you’ll be charged an input token, and everytime you receive that output in the form of copy, an image, or a chatbot conversation, you’ll be charged an ouput token.  Model usage and customization: Model customization prices are based on the number of processed tokens and overall model storage.  [CTA id="dcd803e2-efe9-4b57-92d5-1fca2e47b892"][/CTA] On-demand  On-demand prices look the same as your typical pay-as-you-go plan: you’re paying more for the freedom to stop anytime. Your prices will vary depending on your model of choice and the content you're trying to generate.  The following typically costs more per token:  Text generation  Image generation  Embedding models With that said, here's a breakdown of how much you should expect to pay for common models and services based on 2024 U.S. prices: Model Price per 1000 Input Tokens Price per 1000 Output Tokens Claude 3.5 Sonnet $0 $0.02 Claude 2.0 $0 $0.02 Command R+ $0 $0.02 Embed – English $0 n/a Jamba-Instruct $0 $0.00 Jurassic-2 Ultra $0.02 $0.02 Llama 3 70B $0 $0.00 Mistral 7B $0.00 $0.00 Titan Text Lite $0.00 $0.00   Here’s how much you should expect to pay for image-generation AI assets:  Model Image resolution Cost Per Image - Standard Quality (<51 steps) Cost Per Image - Premium Quality (>51 steps) SDXL 0.8 (Stable Diffusion) 512X512 or smaller $0.02 $0.04 Larger than 512X512 $0.04 $0 SDXL 1.0 (Stable Diffusion) 1024X1024 or smaller $0.04 $0 Titan Image Generator (Standard) 512X512 $0.01 $0 1024X1024 $0.01 $0 Titan Image Generator (Custom Models) 512X512 $0.02 $0.02 1024X1024 $0.02 $0 Provisioned Throughput Buying via provisioned throughput is the best option if you can commit to a one—or six-month period.  There are options to buy certain model units and specific throughput amounts. These will be measured based on the maximum number of input/out tokens for each minute and charged on an hourly basis. This plan is best for those with consistent workloads and who can commit to longer periods of use.  Here’s roughly what your costs should look like:  Model Price per Hour per Model Unit With No Commitment (Max One Custom Model Unit Inference) Price per Hour per Model Unit With a One Month Commitment (Includes Inference) Price per Hour per Model Unit With a Six Month Commitment (Includes Inference) Claude 2.0/2.1 $70 $63.00 $35.00 Command $50 $39.60 $24 Command - Light $9 $6.85 $4 Embed - English $7 $6.76 $6 Embed - Multilingual $7 $6.76 $6 Llama 2 Pre-Trained and Chat (13B) N/A $21.18 $13.08 SDXL1.0 (Stable Diffusion) N/A $49.86 $46 Titan Embeddings N/A $6.40 $5.10 Titan Image Generator (Standard) N/A $16.20 $13.00 Model customization Model customization is one of the factors that influences you AWS Bedrock costs.  Your model customization cost is based on amount of model storage and number of processed tokens you use.  Pro tip: you can only get interference on multiple models if you use Provisioned Throughput.  Model Price to Train 1000 Tokens or Price per Image Seen Price for Storage per Custom Model per Month Price to Infer from a Custom Model for One Model Unit per Hour with No Commitment Command $0 $1.95 $49.50 Command Light $0 $1.95 $9 Llama 2 Pre-trained (13B) $0 $1.95 $24 Llama 2 Pre-trained (70B) $0 $1.95 $24 Titan Image Generator $0 $1.95 $23 Titan Multimodal Embeddings $0.00 $1.95 $9.38 Titan Text Lite $0 $1.95 $7 Titan Text Express $0.01 $1.95 $20.50 How to optimize AWS Bedrock pricing   The pricing for Amazon Bedrock might be the best option for developers compared to other choices on the market, such as AWS Bedrock; now the question is how it fits in the 2025 budget for cloud cost?  Cloud cost management tools like Anodot are designed optimize all of your cloud spend of MSPs and enterprises. We do this by giving you 100% visibility into your entire mulitcloud environment capturing spend down to the hour across all of your cloud platforms, with up to a two year retention period.  Other Anodot features include:  AI-powered cloud management, forecasting, and recommendations. Start saving with the click of a button. Customizable multicloud dashboards that capture your cloud spend across any and all cloud devices.  24/7 automated budget monitoring that alerts you when your cloud spend exceeds certain threshholds.  Easy integration with your other cloud services.  Since 2014, Anodot has worked with FinOps organizations, MSPs, and enterprises of all sizes and worldwide, demystifying cloud costs.  Want a proof of concept? Talk to us to learn how much you can save on cloud cost with Anodot’s tools.  

Azure IoT Hub enables you to monitor on-prem devices down to the smallest temperature change and react accordingly from cloud device commands. But how does it work? What other features does Azure IoT have? And, most importantly, how much does it cost? Learn all you need to know about Azure IoT Hub from our expert guide. In this article: What is Microsoft Azure IoT Hub? Who should use Azure IoT Hub? How much does Azure IoT Hub cost? How to get started with Azure IoT Hub How to save on your Microsoft Azure IoT Hub spend What is Microsoft Azure IoT Hub?   Before we define Microsoft Azure's IoT Hub, let's talk about IoT. IoT, or internet of all things, allows physical objects to connect to the web. This communication empowers you to look up "what is Azure IoT Hub", and explains the best answer. IoT exists in every corner of your life, be it in your fitness watch, smart thermostat, Alexa, or any other device that can receive and control data and connect to the internet. Azure IoT Hub, on the other hand, is one of the many Microsoft cloud services, like Azure Backup or Azure Machine Learning, that help companies store their data with AI-powered technology. It’s a service that lets organizations manage, monitor, and control their IoT devices securely. Examples of Azure IoT involve developers sending and receiving messages via IoT devices in real-time or a company pulling information from Azure SQL databases and turning that information into an informed strategy.   [caption id="attachment_16774" align="aligncenter" width="512"] Source: Microsoft[/caption] Azure IoT Hub features   The following are the three key features you can expect from Azure IoT Hub: Device-to-cloud telemetry Device-to-cloud telemetry means you can use your IoT devices to send telemetry data, such as machine performance or temperature readings, to the cloud for storage, analysis, or processing. Why is this data so useful? If you're analyzing a manufacturing plant, high temperatures cause extended downtime due to equipment failures. Telemetry can quickly capture this insight and send it to the cloud. Birdirectional communication This feature allows your cloud and IoT devices to send and receive messages. Your IoT device can funnel data into the cloud, and the cloud can respond in turn. Since this communication works both ways, you can also issue cloud-to-device commands and use Direct Methods, which issue cloud commands to an IoT device like resetting sensors or rebooting the device. This can be especially useful if you're a small team managing a large array of IoT devices. Why is this data so useful? Think real-time interactions between your devices and the cloud. If you need to make an immediate change—like fixing a problem with a cloud device—you can do so with the click of a button. Device Twins A Device Twin is a cloud copy of your IoT device that stores relevant information, such as metadata and the device's current state. This copy can be used to track and control IoT devices, ensuring they don't overheat, waste energy, and operate at maximum capacity. Why is this data so useful? Device twins make it easier to report on a device's state and control it, protecting you from variables like temperate. It also makes it possible for yout manage devices at scale by showing you all relevant device configurations and metadata in the cloud. Who should use Azure IoT Hub?   Azure IoT Hub is appealing to any organization that requires real-time communication and manages maintenance needs on the fly. In particular, Azure IoT Hub should appeal to: FinOps organizations FinOps organizations often run hundreds to thousands of Azure functions, quickly leading to overheating devices. As we’ve discussed before, overheating devices can result in a machine shutdown, and downtime can mean a severe loss of revenue. Azure IoT Hub can monitor operational temperatures so each device operates at its optimal threshold. Healthcare organizations Since Azure IoT is HIPAA compliant, you can communicate all forms of PHI (personal health information), allowing you to provide real-time patient monitoring and services. Healthcare organizations require the ability to communicate and securely store endless amounts of patient data. For health organizations, hospitals, and clinics, being able to react to changes to a patient’s condition in real-time means, at the least, a massive increase in patient comfort and quality of care—and at the most, it means you could be saving a life. Eco-friendly organizations Energy-efficient processes are useful for meeting budgets, but they're also vital for GreenOps and protecting the environment. Azure IoT Hub is ideal for environmentally conscious organizations because it allows you to track the energy usage of each device, making it easy to reduce its carbon footprint. Industrial automation organizations Azure IoT Hub is known for its real-time automation abilities, which are especially important for industrial automation companies. For this type of organization, it's vital to constantly monitor operations and maintenance to ensure worker safety and maximize profitability. The easiest way to have a continuous influx of data from on-site devices is to utilize Azure IoT Hub so that cloud devices can respond immediately should an anomaly arise. How much does Azure IoT Hub cost?   Good news—the Azure pricing model for IoT Hub is super flexible, with the lowest tier being free! If you're looking just to sample services, you can start with up to 8,000 0.5KB messages a day, though note the free tier doesn't support cloud-to-device communications. And if you’re missing out on cloud-to-device communications, you’re also missing out on advanced analytics on device performance, device authentication, and even AI or machine learning automation. Here’s a breakdown of many basic tier costs: Edition Type Price per IoT Hub unit (per month) Total number of messages/day per IoT Hub unit Message meter size B1 $10 400,000 4KB B2 $50 $6,000,000 4KB B3 $500 $300,000,000 4KB   Here’s how much standard tier costs: Edition Type Price per IoT Hub unit (per month) Total number of messages/day per IoT Hub unit Message meter size S1 $25 400,000 4KB S2 $250 $6,000,000 4KB S3 $2,500 $300,000,000 4KB *Please note that all these prices are pulled assuming the user is based in the East U.S. region.   Here’s how features differ from basic to standard tiers: Feature Basic Tier Standard Tier Device-to-cloud telemetry ✅ ✅ Per-device identity ✅ ✅ Message Routing, Event Grid Integration ✅ ✅ HTTP, AMQP, MQTT Protocols ✅ ✅ DPS Support ✅ ✅ Monitoring and diagnostics ✅ ✅ Device Streams ✅ Cloud-to-device messaging ✅ Device Management, Device Twin, Module Twin ✅ IoT Edge ✅   What about the costs for Azure IoT Hub device provisioning services? At the S1 tier, you’ll only pay $0.10 for every 1,000 operations. You'll also be paying $70 per tenant and $0.70 per device for every over-the-air update (OTA) you deploy to keep your IoT devices up to date. How to get started with Azure IoT Hub   Setting up an Azure IoT Hub account is very similar to setting up an Azure Storage or DevOps account—in other words, it's easy! You'll need a free account if you don't already have an Azure subscription. From there, perform the following: From the Azure portal, click on the IoT Hub section's "+ Create" button. Provide details about what kind of subscription you want, your resource group, IoT Hub name, and region. Select "Review + Create". Congrats! You've made your first IoT Hub. Register your devices with your new IoT Hub to connect them to your new IoT Hub. To do this, click on the "+ Add button" and name the device. Add an IoT Hub SDK to your device application to send a message from your device to your Azure IoT Hub. Test newly connected devices by sending messages to the Hub. [caption id="attachment_16776" align="aligncenter" width="361"] Source: Microsoft[/caption]   How to save on your Microsoft Azure IoT Hub spend Azure IoT Hub enables users to constantly monitor device changes and send real-time communications and commands to those devices from the cloud. The one place it falls short is cloud cost management. Azure does offer a cost management feature, but these tools provide a quarter as much detail as a third-party cloud cost management service. According to a 2023 study from Zesty, 63% of tech executives agree that cloud cost optimization is vital in maximizing cloud ROI. What is the best way to ensure that your Azure IoT Hub spend does its best possible work and meets your budget? Anodot. Anodot’s purpose is to give you 100% visibility into your cloud spend—and that means your entire cloud spend. You can pull up your whole multi-cloud environment and track data down to the hour for up to a two-year retention period. If you use Azure, Google, or AWS simultaneously, Anodot can give you all that data in one dashboard. Not only that, but you’ll get AI-powered budgeting and management recommendations. All that data is available to you at the click of a button that can help you save up to 40% on your annual cloud spend. Worrying about your Azure spend – your multicloud spend – can become a thing of the past. Why go with Anodot? We demystify cloud costs. Since 2014, we have made it our mission to ensure FinOps organizations have the proper data to save on cloud spend. Want a proof of concept? Talk to us to learn how much you can save with Anodot’s tools.

So you're an MSP supporting tens, hundreds or maybe even thousands of clients. Then there’s a good chance that you're offering services for more than one cloud vendor. You may even have clients who are Multi-Cloud (using more than one cloud vendor). The Billing Challenge: Managing Complex Invoices   Now the first question is, How do I handle all these clients and multicloud deployments and what exactly am I managing? So while every MSP is going to have its distinct offering for customers, ranging from on-demand professional services to fully outsourced management of an environment, nearly all MSPs are going to be cloud resellers, offering customers flexible plans to consume public cloud services and spend via the MSP. Also, most MSPs will offer their clients some form of Finops / Cost optimization service. Based on this, you're going to need a solution that can: Help you with billing & invoicing  Assist you with all Finops activities, including but not limited to Cost reduction, usage analyst, reporting & budgeting. Let's dive in a bit here. You're thinking to yourself, surely the Cloud vendors will provide me with a detailed bill that I can pass on to my clients. Wrong!  You will often be supplied with a massive invoice containing all of your clients' billing in bulk. This invoice needs processing and breaking down for each client. The invoice may also contain special discounts for certain clients, credits, and refunds. All of these also need to be carefully dealt with to ensure that customers receive all the benefits they are entitled to. [CTA id="dcd803e2-efe9-4b57-92d5-1fca2e47b892"][/CTA] Addressing Diverse Client Needs: Customization Is Key   Certain clients may also have specific requests. They may want an invoice split up into multiple invoices based on departments within their organizations, or in other words they want to perform internal chargebacks. So this is something else that you want to look out for. As an MSP, you may also be receiving certain incentives from the vendor that you are entitled to, which are usually meshed up within the invoice and may also require processing accordingly.  FinOps: Delivering Cost Optimization at Scale   From a cost optimization perspective, the list is never-ending and basically expanding every day. This is a major factor in your decision-making process. First, you need to define the extent of Finops and optimization service that you will be providing to your clients. Larger clients will most likely have their own Finops teams and be less reliant on the MSP for this function. They may want to consult or request insights, but they will handle the day-to-day internally. However, smaller and mid-size clients will be more dependent on the MSP for the handling of Finops. While these may be smaller environments, they still need addressing and each environment will have its own specific challenges. You're going to need to build out your methodology for supplying these customers with Finops and cost optimization services.  As this will most likely be  based on weekly, monthly, or quarterly review meetings, based on the client size, you are most likely going to want to focus on the following areas: Waste control and cleanup, rightsizing of resources & implementation of reservations (RI’s) and saving plans (SP’s). Focusing on these three main areas will usually supply a good level of cost reduction that you as an external vendor will be able to supply the client and present the savings achieved accordingly.  ​​What Is Multi-Tenant, and Why Do MSPs Need It?   But wait, there’s more. While many tools will offer this functionality in one form or another as an MSP, you will also need a Multi-Tenant tool. What exactly does this mean? Well, on the one hand, you will want a single pane of glass to view and manage all your clients. You're also going to want to be able to achieve this with a single login and not have to login separately to each client’s environment.  However, you’ll most likely also want your clients to be able to login to the tool, and in this case, they need to be limited to the scope of their own environments. This is the meaning of multi-tenant, allowing you a central management console to view all of your customers while supplying each customer a slice and dice view of his own environment, of course, all with central user management. A big plus if the tool supports SSO and allows both you and your customer to login using your own and their own existing credentials such as Okta, Entra ID, Google etc. And of course, let's remember that if you're supporting multiple cloud vendors, then you're going to want a tool that supports these vendors. The last thing you want is to be using multiple tools to achieve the same goals but on different clouds. [CTA id="574cb89f-f2c3-4cc5-b4f5-a7c98f7f436a"][/CTA] Key Features Every MSP Should Look For in a Cost Management Tool   So to summarize the main points you want to be looking at: Billing & Invoicing Finops & Cost Reduction SSO with existing IDM’s  MultiCloud support  All of this should be wrapped up in a Multi-tenant supported solution to allow you centralized management and views with slice & dice capabilities and end-user customer access, allowing them views of their own environments. Let's Talk About Your Needs   Looking to transform your MSP cost management and FinOps strategy? Talk to us today to discover how our multi-tenant solution can help you streamline operations, maximize cost efficiency, and deliver unparalleled value to your clients.

Here's how Anodot became a leader in the Visionary category with its game-changing vision in FinOps and cloud-saving insights. What a journey it’s been! As the Marketing Director at Anodot, I had the honor of managing our entire process with Gartner - from start to finish - to achieve what we’ve worked so hard for: Visionary status in the 2024 Magic Quadrant for Cloud Financial Management. This recognition wasn’t handed to us. It’s the result of months of showcasing how our AI-powered innovations, like anomaly detection, forecasting, and CostGPT, are transforming FinOps for enterprises and MSPs.     It wasn't just recognition from the leading authority in the technology industry but the culmination of a journey fueled by our incredible team’s dedication to providing our customers with FinOps-centric innovation and AI proprietary data. This wasn't just any victory but an acclaimed win forged by industry-led expert leaders at our company. It wasn’t easy, but our challenges led us to be an influential leader in the Cloud FInancial management space. Here’s our road to success in becoming a Gartner leader in the Visionary category in Gartner’s Magic Quadrant Report. The Road to the Gartner Magic Quadrant   The path to Gartner recognition was a marathon, not a sprint. We know the process would be rigorous, demanding meticulous preparation and a deep understanding of Gartner's Magic Quadrant evaluation criteria for completeness of vision and ability to execute. To say it wasn’t easy would be an understatement. There were countless conversations, moments of self-reflection, and late nights ensuring we didn’t just meet Gartner’s expectations—we exceeded them. But what made this journey so meaningful was knowing that every step reinforced our commitment to helping our customers succeed. For me, this milestone is personal. It validates not just our product but also the teamwork, passion, and resilience that make Anodot what it is. Team Effort, Preparation, and Aligning Our Vision Our team rallied behind the goal, contributing their unique expertise and insights to highlight how Anodot is a FinOps partner constantly innovating to reduce cloud cost for its clientele. From product to engineering, data science to customer success, everyone contributed to Gartnet’s recognition of us. We recognized the importance of aligning our company's vision with the Gartner evaluation criteria for technology providers throughout the process, including how well a provider delivers on its current vision, meets market needs, and understands future potential and market trends. Our responses showcased our commitment to innovation and customer-centricity while reflecting the vision we aim for. Reflecting FinOps Innovation on Our Website As we prepared for the Gartner submission, we understood that the impact of innovation on our products is reflected well on our homepage. We underwent a comprehensive overhaul, reimagining our homepage to communicate our core differentiators effectively. This alignment of a refreshed brand style and a focus on innovation has solidified our position as a leader in all-in-one FinOps solutions for cloud cost management platforms that effectively communicate our commitment to quality in the industry. Why We Were Chosen as a Visionary Anodot's Visionary status is a testament to our data-driven approach and customer focus. Our advanced anomaly detection, precise forecasting, real-time savings tracking, and innovative CostGPT technology empower clients to achieve tangible value. This commitment to innovation ensures Anodot's continued leadership in cloud financial management. Advanced Anomaly Detection: Our industry-leading anomaly detection tool accurately identifies cost deviations and anomalies. Accurate Forecasting: Our advanced ML models enable precise forecasting with 98.5% accuracy. Real-Time Savings Tracking: The Saving Tracker provides real-time visibility into cost-saving recommendations and their impact. CostGPT Innovation: Our proprietary CostGPT technology offers unique insights and automation capabilities. Challenges and Overcoming Obstacles   One of the biggest hurdles was the sheer volume of data required for the submission. Gathering, analyzing, and engagingly presenting this data took a lot of research and attention to detail to clearly show that Anodot is an influential player in cloud cost management. We crafted our responses to align with the Gartner Magic Quadrant and ensured they were of the utmost quality. Additionally, we set up a dedicated team to gather and analyze the necessary data, and we held regular check-ins to keep our responses on track and in line with Gartner's qualifications. Navigating The Gartner And Getting To The Finish Line The Gartner journey for Anodot was a well-researched and thoughtfully put-together plan highlighting our expertise as a cloud financial management tool. It shows that we're not just about what we offer now; we're also continuously testing, updating, and rolling out new products to maintain our reputation as a top-tier FinOps-certified platform. Receiving this recognition from Gartner as a top competitor in the cloud cost space gives us the credibility to continue exploring how our intelligent cost optimization platform can benefit MSPs and enterprises. This would not have been possible without the incredible Anodot team, especially our dedicated product, CSM, R&D, and data science teams. Their expertise, innovation, and unwavering support were instrumental in our success. This journey would not have been possible without their hard work and dedication. This achievement isn’t just a win for Anodot - it’s validation for our customers. It means the solutions we provide help businesses optimize cloud spend, eliminate waste, and drive real impact. And we’re just getting started. Download the 2024 Magic Quadrant™ report to compare and analyze the listed vendors.

We’ve all been hearing about Amazon Bedrock – and the exclusive few who could access the full scope of AWS’ new product. But what exactly is AWS Bedrock? What can it help you accomplish? And, most importantly, when can you get full access to it?  Learn all you need to know about AWS’ new tool from our cloud experts.  In this article: What is AWS Bedrock? What can AWS Bedrock do? How does Amazon Bedrock work? How much does AWS Bedrock cost? Who can use Amazon Bedrock? How to manage your AWS Bedrock spend What is AWS Bedrock?   Amazon Bedrock will be your next go-to tool for building generative AI applications in AWS. Developers can use the tools to make applications like ChatGPT and other gen AI programs. In other words, it enables you to create a tool that can generate anything from copy to images to even music.  We’ll explain how AWS Bedrock does this below, but for now, know that this tool gives developers full access to customizable foundational models.  The best part? You don't need to worry about building independent infrastructures or training the AI. You can use your pre-existing AWS cloud environment for that.  [caption id="attachment_16656" align="aligncenter" width="512"] Source: AWS[/caption] AWS Bedrock vs AWS Sagemaker Let’s clarify one thing: though AWS Bedrock and AWS Sagemaker might both handle AI, they are two very different tools.  Amazon Bedrock is ideal for creating and generating AI applications that help with everything from content creation to security and privacy. Amazon SageMaker is designed to develop, train, and deploy machine learning models, such as debuggers, profilers, and notebooks.  These two AWS products can be used in tandem to develop machine learning (ML) and generative AI applications. However, Amazon Bedrock was made with developers in mind, with pay-as-you-go pricing and pre-trained models to help you get started. SageMaker was made for data scientists and ML engineers, allowing full creative access to customize models as needed.  Amazon Bedrock vs Microsoft vs Google Amazon Bedrock might be the newest developer tool on the market, but how does it compare to the other generative AI tool powerhouses? AWS isn't the only company developing generative AI tools like this. Microsoft has Azure OpenAI, and though Google doesn't have an exact 1:1 competitor, it is working on a similar option called Google Vertex AI.   But what are the differences between these tools?  Azure OpenAI is more accessible than Bedrock. It supports more regions and languages but is limited in creation compared to Bedrock. Google Vertex AI lets you build custom models, providing the most freedom with features like deploying from an on-premise or hybrid environment, not just from the cloud. However, the learning curve might be higher than that of another alternative because of that added freedom and lack of managed service. Amazon Bedrock offers managed services, so you’ll have much more support to build and grow. Plus, you'll get access to Amazon's still-growing AI model, Titan. The tool also offers features that can help you save money on computing and reduce overall workloads, making AWS more cost-effective in the long run. What can AWS Bedrock do?   Amazon Bedrock allows developers to create a wide range of generative AI tools. Here are a few common examples:  Chatbots. Like ChatGPT and Bard, developers can create a conversational bot or your next virtual assistant. Content simplification. Like Google's Search Generative Experience, you can also create an AI that condenses data sets and essays into easy-to-understand paragraphs. Text generation. As mentioned above, Amazon Bedrock can be your go-to for copy and content generation, helping you get started with anything from blogs to emails. Image generation. You can also use AWS Bedrock to create images for said blogs or even for marketing campaigns or branding. Personalization recommendations. Improve your user recommendations by feeding customer personas into Amazon Bedrock to ensure your offer aligns with your customers' needs.  How does Amazon Bedrock work?    AWS Bedrock enables your developers to create powerful generative AI tools by allowing your team access to already-established large language models (LLMs) from companies like Anthropic, Stability AI, and more. Developers can add their flare by attaching custom code to an established foundation model. Deployment will always take place in AWS’ cloud environment.  But what exactly is a foundation model? A foundation model is a tool that can understand natural language requests for generating new text or images. Still, a developer shouldn’t replace it, as it cannot complete complex tasks without human guidance. Amazon Bedrock provides foundational models developers can use to build applications such as chatbots or content generators. To leverage Bedrock effectively, developers can connect these foundation models with specific data sets or fine-tune them for tasks relevant to your organization.  How much does AWS Bedrock cost?   Amazon Bedrock charges you for model interference and customization, which makes projecting your AWS budget a bit tricky.  You can either pay for these services with: On-demand plan, which is a pay-as-you-go plan. This means you can avoid long-term commitments, though you’ll be eating steeper costs. Provisioned Throughput plan that ensures you'll have the same monthly bill but will want to meet specific performance thresholds to ensure you aren't wasting money. AWS Bedrock pricing varies depending on:  The foundation you use to build your generative AI. The assets you choose to generate. Images will cost more than copy, for example. Model customization can also inflate your price depending on how many tokens you use and how much model storage you need.  Who can use Amazon Bedrock?   AWS Bedrock was released for the general public in September 2023, so anyone can use it now! Companies like have already started migrating from OpenAI and have seen a 30% classification improvement.  In other words, you don’t even necessarily need to make room in your budget for AWS Bedrock. If you’re investing in a similar tool and want to see how much you can save, you should take the plunge.  How to manage your AWS Bedrock spend   Amazon Bedrock sounds like an obvious addition to your AWS tool suite, but are you ready for its cost? Sure, you might be able to save big by transitioning from Azure OpenAI to AWS Bedrock, but such migrations are always a risk – and always cost a little more than you might expect.  The good news is that you don’t have to commit to AWS Bedrock’s full cost… you might only have to commit to 70% of that cloud cost. And those hidden migration prices? They don’t need to creep up on you—not if you have a cloud cost management tool.  Tools like Anodot have been designed to help you save big on your annual cloud spend—all by giving you 100% visibility into your cloud spend. And by cloud spend, we mean all of your cloud spend, as in full views and dashboards of your entire multi-cloud environment, with graphs capturing spend down to the hour up to a two-year period.  Why use Anodot? Demystifying cloud costs is our area of expertise, and we’ve created the AI tools you need to address cloud price fluctuations. By partnering with Automat-it, our CostGPT is now enhanced with Amazon Bedrock, proving the perfect aid to identify hidden prices, poor cost monitoring and reporting, all with you asking a simple search about your cloud spend.  Want a proof of concept?  Talk to us to learn how much you can save with Anodot’s tools.  

With Anodot's partnership, monday.com's vision for FinOps innovation has become a reality.

Microsoft offers several cost-saving opportunities with Azure pricing. Azure Reserved Instances, Azure Savings Plan, Azure Hybrid Benefit... all can help you save a pretty penny if you know what you're doing. If you're looking to cut costs with your Azure VMs, Spot VMs can be the way to go – but the risks can be just as high as the rewards.  How do you help your FinOps organization save big – and improve your chances of avoiding a complete resource shutdown? Learn all the ins and outs of Azure Spot VMs below.  In this article: What are Azure Spot VMs Why use Spot VMs? How much do Spot VMs cost Who should use Spot VMs? How do Azure Spot VMs work How to deploy Azure Spot VMs Azure Spot VMs vs. Reserved Instances Get complete visibility into your Azure Spot VM spend What are Azure Spot VMs   [caption id="attachment_16623" align="aligncenter" width="512"] Source: Microsoft[/caption] A Spot VM is a cloud server instance available for up to a 90% discount. Of course, with big discounts come big drawbacks. In this case, Azure has the right to evict – basically, it can make the VM unavailable and disrupt your Spot VMs with little to no notice.  So, knowing that Spot VMs can be interrupted anytime, why would you ever want to use them?  Applications that best run Spot VMs   Spot VMs can appeal if you're running certain types of jobs that can stand to be interrupted. If you're doing any of the following tasks, you might want to look into this opportunity:  Test and development environments Already using Azure DevOps? Then your test and dev enivronments are a great place to start. These are places where software developers can code and test new applications in a product environment that won't impact the current status quo. Since these environments aren't cheap, Spot VMs can be highly appealing.  For example, let's consider a hypothetical FinOps organization. Say this company wants to test new security measures that should offer extra comfort to clients, but this might introduce a horde of new bugs. The best way to ensure a smooth, glitch-free experience is through rigorous testing in a dev environment. Since the testing isn't customer-facing, it can be done in a Spot VM space.  Batch jobs Batch jobs are automatic tasks typically processed in large groups – a.k.a. "batches". Since these tasks usually have flexible start and end times, it doesn't hurt the workload to be randomly paused or started.  Retake our FinOps organization example. Say you’re running transactions toward the close of business. You might load up thousands to millions of transactions to validate and update an account. Spot VM instances can be a great place to do this.  Fault-tolerant applications Fault-tolerant applications can continue even after certain components fail due to special redundancy measures and failover mechanisms.  Let’s say our FinOps company has multiple Azure SQL servers established through Azure Functions that focus on handling customer support. This means that requests will be automatically routed through the next available server, even if one server fails.  [CTA id="cad4d1a1-3990-4d6b-bb21-ccdcbb6949db"][/CTA] Why use Spot VMs?   Spot VMs can be wildly unpredictable. We’re talking like a 30-second eviction notice.  Why does Azure do this?  Like most major cloud providers, Azure has a lot of unused compute capacity on its platform. During low demand, it wants to sell that space to make a little extra cash. However, when the demand for compute resources increases, the first thing Azure is looking to cut is Spot VM instances.  So, why risk it?  The most apparent advantage of Spot VMs is the cost savings they offer. A 90% discount means you can put those dollars elsewhere. You can scale up workloads on your new Spot VMs. You can invest more elsewhere in your cloud environment, like beefing up your cloud forecasting tools or transitioning into GreenOps to save the real-world environment, not just the cloud environment.  When your VM inevitably evicts you, you can handle that easily enough by having a metaphorical eviction policy that quickly and easily handles the lost VM, restarting your systems once you have available bandwidth again while maintaining your data and workloads.   How much do Spot VMs cost   Spot VM prices vary depending on the following factors:  OS/software type  Region  VM series type  You can also sort Spot VM options by a minimum number of vCPUs/cores and RAM.  Below is a sample table to give you an idea of the costs. The price breakdown shows a possible scenario for purchasing a Red Hat Enterprise Linux OS in the Eastern US region. Instance vCPU(s)/Core(s) RAM Temporary storage Pay as you go Spot A1 v2 1 2 GB 10 GB $41.90/month $14.31/month 66% savings F1 1 2 GB 16 GB $46.79/month $14.90/month 68% savings F1s $1 2 GB 4 GB $46.79/month $14.90/month 68% savings DS1 v2 $1 3 GB 7 GB $63.80/month $16.96/month 73% savings D1 v2 $1 3 GiB 50 GB $63.80/month $16.96/month 73% savings DC1s v2 $1 4 GB 50 GB $80.59/month $18.99/month 76% savings DC1s v3 1 8 GB N/A $80.59/month $18.99/month 76% savings DC1ds v3 1 8 GiB 75 GiB $93.00/month $20.49/month 78% savings B2pts v2 2 1 GiB N/A $27.16/month $22.88/month 16% savings B2ats v2 2 1 GiB N/A $27.89/month $23.10/month 17% savings Azure also has a Spot VM-specific pricing calculator that can help you weigh risk vs reward scenarios.  Who should use Spot VMs? Spot VMs are usually a solid choice if you're an enterprise organization. You'll typically have big testing environments or large workloads that can withstand the sudden start/stop pattern of Azure Spot VMs.  But you can still use and benefit from Spot VMs even if you're not handling enterprise-sized workloads. Organizations that manage experimental or testing environments are also ideal for Spot VMs since they have a high tolerance for random interruptions.  Who shouldn’t use Spot VMs? The real question is, who shouldn’t use Spot VMs?  If your company purely runs monitoring software or web applications, Spot VMs aren't the best choice since the risk of a sudden downtime ruining everything is too high.  How do Azure Spot VMs work   As mentioned above, Spot VMs occur when Azure needs extra compute space to sell, so they’ll offer it for a steep discount. One key thing to remember (besides that Spot VMs can stop working anytime someone wants to pay full price) is that not all Spot VMs come with those 90% discounts. Remember: Spot VMs have such steep discounts because Azure is trying to sell that space quickly. That means the more spare compute capacity Azure has for a certain VM, the bigger the savings (since they'll want to sell that unused space faster). Savings can also vary depending on the VM configuration, compute capacity, or cloud region you're shopping in. Different regions might have steeper discounts.  Typical rates range between 75% to 90%, though sometimes savings can be as low as 30% or 40%. At that point, you're better off using Reserved Instance types like we mentioned above. How to deploy Azure Spot VMs   You have two options for deploying Spot VMS: a single Spot VM or a Spot VM Scale Set, also known as a group of Spot VMs. And don’t worry—Azure Spot VMs are as easy to deploy as Azure Machine Learning.  Our guide below details how to do this from the Azure portal, but you can also use Azure CLI or Azure PowerShell or another method to start your Spot VM. You can refer to the Azure guidelines to learn more.  Deploying Single Spot VMs Start with creating VMs from the Azure portal since that's the easiest way. You can do this by:  Go to "Virtual Machines" listed under "Services." Select "Create".  Fill out all necessary details in the "Create a virtual machine page". Don't forget to check the "Run the Azure Spot discount" box.  Select the "Eviction type" and "Eviction Policy" that works best for you.  For "Eviction type," you can pick either "Capacity only" or "Pricing or capacity". The former means your VM will only get evicted when Azure VMs are in high demand, forcing you to set a maximum price to pay for the Spot VM since that number can fluctuate. The latter lets you reduce that maximum price, and you'll then get evicted either when Azure runs out of room, or the cost exceeds your set price point.  For "Eviction Policy," you can either select "Deallocate" or "Delete." Deleted means everything is permanently lost. Deallocated means the VM pauses but can be resumed. You'll typically want to pick deallocated so you can pick up where you've left off, but if you're worried about spiking Azure storage costs, deletion might be best.  Deploying Virtual Machine Scale Sets (VMSS) Creating Spot Virtual Machine Scale Sets (VMSS) is very similar to creating a single Spot VM.  Find Virtual Machine Scale Sets.  Follow the same steps as the Spot VM creation process. Make sure to know if what kind of Eviction type and Eviction Policy you want to go with!  With VMSS, you can also automate the kind of deployment and management types you want, so it's easier to scale. For example, you can pick CPU usage or network traffic to help cap or bolster scaling or stick with the same maximum price and availability requirements offered by the single Spot VM setup.  Azure Spot VMs vs. Reserved Instances   Spot VMs aren’t the only way you can spend on Azure spend. As mentioned above, Reserved Instances can sometimes be more appealing despite lower deals. Let's explain why you might want to choose one deal over another.  Spot VMs You already know by this point that Spot VMs can help you save up to 90%. But Spot VMs also come with the steep drawback of a 30 second warning before eviction, and a fluctuating savings offering depending on availability.  If you're dealing with a flexible workload, Spot VMs will usually offer the better savings deal—so long as you're prepared for unpredictability.  Reserved instances On the other hand, Reserved Instaces require a one—or three-year commitment. You can get a VM for a much lower cost than the on-demand Azure pricing, but you'll have to be ready to use it for either one or three years. You don't need to worry about eviction, but you're offered no flexibility.  If you know that you'll have a steady amount of work for the next one or three years—and you don't want to deal with the unpredictability of Spot VMS—Reserved Instances are probably the better option for you.  How to optimize Azure Spot VMs   Here are our best practices to reduce the risk and increase the rewards of using Spot VMs:  Set maximum VM prices [caption id="attachment_16624" align="aligncenter" width="426"] Source: Microsoft[/caption] As we mentioned above, setting a maximum price can prevent Azure from pulling a fast one on you by changing the Spot VM price when availability becomes less limited. You'll get evicted from your VM, yes, but you'll have much better control over your budget.  Confirm available capacity Since capacity can change depending on Azure customer demand, you'll want to make sure the region and VM size you need are available for purchase.  Get an interruption strategy  Spot VMs can be interrupted at ainy time, so make sure you're implementing strategies to address this, not just using workloads that can withstand frequent pauses. Consider using checkpointing to save work often done so you can pick up where you left off if abruptly stopped.  Review previous evictions Make sure to review historical eviction rates. While there is not guarantee of eviction rates matching past trends, this still gives you can idea of the interruptions you'll have to work around so you know if you have workloads that are a good fit or not.  Consider using Spot Priority Mix Spot Priority Mix is a unique Azure feature that lets you combine standard and Spot VMs. This set up will allow you to automatically move Spot VM workloads when you get evicted to a standard VM. If you're looking to run hundreds of VMs, you can use Spot Priority Mix, so you don't have to worry about the risks of relying heavily on Spot VMs.  Use Azure Backup You can also keep your Azure Spot VM data secure by using Azure Backup. This means your data will remain 100% secure and recoverable no matter the eviction.  Monitor, monitor, monitor Our final pro tip for savings with Azure Spot VMs is that Spot pricing can change even after you've deployed a server. So even if you've captured a 90% discount rate, you'll want to monitor accordingly because Azure can and will pull the rug out from under you and change that fee.  Azure provides some tools to help you keep a pulse point on your fluctuating costs, but they can be limited in the amount of information provided as far as cost optimizations. If you're really looking for the best way to save on Spot VMs, you'll want to consider going third-party.  Get complete visibility into your Azure Spot VM spend   To truly master the savings Azure Spot VMs offer, you’ll need far more than the monitoring tools Azure provides you. Going third-party is the only way to ensure your cloud resources are performing at maximum capacity at all times and not a penny has gone to waste.  And the best news? You can stack up to 90% savings from Azure Spot VMs with up to 40% annual cloud spend savings offered by a cloud optimization tool like Anodot.  Why go with Anodot? We’ll make sure a Spot VM eviction never catches you by surprise. Our AI-powered anomaly detection means you’ll be ready for anything.  Anodot was made for demystifying cloud costs for FinOps organizations. Our real-time dashboards and customized alerts mean you can keep a 24/7 pulse on your spend not only with Azure and Spot VMs but also your entire multi-cloud experience. Our tools offer AI-powered feedback, which means you can start savings without ever having to dig into the data.  Anodot’s dashboards capture all of your multicloud spend, with a specific focus on opportunities where you can save. So if you want to be thrifty with Azure Spot VMs, we’ve got your back. We’re here to ensure that you get the most lift for each dollar spent and get some of those dollars back to reconfigure your monthly budget.  Want a proof of concept? Talk to us to learn how much you can save with Anodot’s tools.