Token Limit Exceeded? What's Actually Going On and What to Do About It ?
Hi All, Based on some recent experience across the organisation with token limit issues, I wanted to put my thoughts down and actually dig into what's happening under the hood, rather than just chalking it up to "we need a bigger plan." If you work anywhere near the Microsoft ecosystem these days, you're probably touching more AI tools than you realize. Copilot in Word and Excel, GitHub Copilot while you code, Copilot Studio if you're building agents, maybe Security Copilot or Copilot for Sales depending on your role, and increasingly Azure AI Foundry if your team is building anything custom. I work across a good chunk of this stack day to day, and at some point, almost everyone runs into the same wall: "Token limit exceeded." "You've reached your usage limit." "Upgrade to continue." The first instinct is usually to assume you did something wrong wrote too much, uploaded too big a file, or just need a fatter subscription. Sometimes that's the actual story. But honestly, often, that error message is standing in for three completely different problems that all happen to look identical from the outside. One is about how much text a model can physically process at once. One is about your license or credits running dry. And one has nothing to do with size at all it's just about how fast you're sending requests. Once you know which of these three, you're dealing with, the fix becomes obvious. Until then, "upgrade your plan" feels like the only lever you've got even when it isn't. This post walks through what a token is, why Microsoft's various Copilots each handle this differently, and what habits genuinely cut down on these interruptions instead of just throwing money at the problem. Part 1: So What Is a Token, Really? A token isn't a word, and it isn't a character it's somewhere in between. It's the small chunk of text a model's tokenizer breaks your input into before it can do anything with it. Take a word like "unbelievable." A tokenizer might split it into three pieces something like "un," "believ," and "able." Short, everyday words usually come out as a single token. But code, technical jargon, acronyms, and non-English text tend to fragment into a lot more tokens than you'd guess just by looking at the word count. This is why every AI tool has a ceiling on how much it can handle in one go, and that ceiling isn't measured in words or characters it's measured in tokens. Your prompt, any documents or emails it pulls in as context, the back-and-forth history of your conversation, and the response itself all draw from the same pool. Once that pool runs dry, something has to give: the tool truncates, rejects the request outright, or quietly summarizes older context to make room. The part that trips people up: token count doesn't map cleanly to word count. A short, dense paragraph full of code or acronyms can eat up more tokens than a much longer plain-English message. Part 2: Three Different Limits, One Confusing Error Message This isn't always obvious upfront, even to a lot of admins managing these tools: "token limit exceeded" is really a stand-in phrase for three separate limits, and they don't behave the same way. This isn't unique to Microsoft either every major AI platform bundles these same three things behind similarly vague error messages. Microsoft's stack just makes a good case study because so many of us touch multiple pieces of it in the same week. The context window is the ceiling on how much text a specific model can process in a single request everything from your prompt to retrieved documents to chat history. This is tied to the model itself, not your subscription. Swap from one model to another inside the same tool, and this ceiling can move without you doing anything differently. Your license, credits, or feature allowance is a completely separate thing. This is what Microsoft 365 Copilot plans track through AI credits and feature limits, and it's what Copilot Studio measures through Copilot credits at the environment level. A single action summarizing an inbox, generating an agent response, running an analysis deducts from this pool regardless of how small your actual prompt felt. Run out, and you get blocked, even if you're nowhere near any context window limit. The rate limit is about speed, not size. Copilot Studio, for instance, enforces quotas measured in requests per minute or per hour to keep the system stable under load. Send messages too quickly, which happens easily with automations, flows, or bots, and you can get throttled even with a tiny prompt and plenty of credits left. The reason this matters: a plan upgrade only ever fixes the second one. If you're actually running into the model's context window or getting rate-limited, paying for a bigger license won't change anything, and that mismatch is exactly where most of the frustration comes from. Part 3: How This Plays Out Across the Microsoft AI Stack The Microsoft ecosystem isn't one AI tool wearing different outfits it's genuinely several different systems, each handling tokens and limits in its own way. Here's a tour of the ones people run into most. Microsoft 365 Copilot (the one living inside Word, Excel, Outlook, Teams) doesn't work off a single published token number the way a developer tool would. Instead, it dynamically pulls together your prompt, recent chat history, and relevant snippets retrieved from Microsoft Graph your files, emails, and messages and quietly summarizes or drops older material to stay within bounds. Where this usually breaks isn't the context window at all; it's the AI credit and feature-limit system running out, often without much warning until you're mid-task. GitHub Copilot Chat is more like a traditional developer tool. It has a fixed, published token window tied to whichever model you've selected, and that limit applies consistently whether you're in the browser, VS Code, or the CLI. The failure mode here is usually a long conversation or a big multi-file context quietly creeping past that ceiling. Copilot Studio, where a lot of custom agent-building happens, runs on Copilot credits per interaction, plus its own requests-per-minute and requests-per-hour quotas at the environment level. If you're grounding an agent in SharePoint content, there's also a separate file-size ceiling to watch content over a certain size can get silently excluded from generative answers depending on your tenant's licensing. Azure AI Foundry (recently renamed to Microsoft Foundry, in case you've seen both names floating around) is where this gets more directly in your control. If your team is building custom applications on top of Azure OpenAI or other models in the Foundry catalog, which now includes everything from GPT to Phi to Claude to Llama, you're working with explicit, published context windows per model, and you're billed per token rather than per credit. It's a different mental model entirely: less "you hit a wall," more "you're paying by the word, so design accordingly." Security Copilot, if your org uses it for threat analysis and incident response, runs on its own capacity model pooled compute units at the tenant level rather than a simple per-user cap. It's easy to assume this behaves like M365 Copilot license limits; it doesn't. Copilot for Sales, embedded in Outlook and Teams for CRM-connected work, and Copilot in Power BI, which now goes beyond generating summaries to actually helping build and refine semantic models, both draw from their own feature-specific allowances layered on top of whatever base Microsoft 365 or Power Platform license you're on. And then there's the multi-model wrinkle that trips up teams the most: because tools like Copilot Studio and GitHub Copilot let you choose between GPT-based models, Claude, and others, the exact same prompt can have a different effective context window and a different token cost purely based on which model handled it that day. This is a big, underrated reason behind the "it worked fine yesterday, why not now" complaint. Part 4: What Actually Helps ? Some of this is genuinely outside your control, but a fair amount isn't. If you're just using these tools day to day, the single biggest habit shift is not letting conversations run forever. Long threads in Copilot Chat or Copilot Studio keep accumulating history, and that history eats into the same budget as whatever you're asking right now. Starting fresh periodically costs you nothing and buys back a lot of headroom. Large documents are worth splitting up before you feed them in, especially for SharePoint-grounded agents, where oversized files can get quietly excluded rather than cleanly rejected you won't necessarily know it happened unless you're looking for it. And it's worth resisting the urge to default to the heaviest, most capable model for every single task. Lighter models are usually faster, cheaper, and often sit under a more generous limit than the flagship ones, and most everyday tasks genuinely don't need the biggest model available. Before you go asking IT for a license upgrade, it's worth a quick sanity check on which limit you actually hit. If it's a rate limit, waiting a minute and retrying usually solves it outright. If it's a context window problem, trimming your prompt or starting a new session fixes it. An upgrade only helps if you've genuinely run out of credits or feature allowance, and that's worth confirming before you file the request. If you're on the building side Copilot Studio agents, Foundry applications, anything with RAG-style grounding a couple of things pay off quickly. Keep an eye on credit or token consumption proactively rather than discovering it's gone when the agent goes down mid-conversation. Be deliberate about what goes into system prompts and orchestration instructions, since those draw from the same budget as the end user's actual message, often invisibly to whoever's chatting with the agent. And spend real time getting chunk size right for knowledge sources too large and you're burning budget on irrelevant context, too small and the agent loses the thread. Part 5: Quick Checklist Before You Escalate Is this actually a context window problem -prompt, history, and attachments too big for the model in use? Have you genuinely run out of credits or feature allowance on your plan? Could this be a rate limit -too many requests too fast, especially from a flow or automation? Did the underlying model change since last time, quietly shifting the effective window? For Studio or Foundry work, is this a tenant or environment-level limit rather than something tied to you personally? Closing Thoughts Tokenization is one of those things that stays completely invisible right up until it isn't. Across a stack as sprawling as Microsoft's M365 Copilot, GitHub Copilot, Copilot Studio, Foundry, Security Copilot, and everything layered on top "token limit exceeded" almost never means one single thing. It means you've hit one of three very different walls, and each one needs a different response. If your team builds or maintains any of these tools, this is genuinely worth putting in front of people early. Most of the "why did this break" tickets in this space aren't about tokens at all. They're about nobody knowing which limit actually got hit, or where in this increasingly large ecosystem it happened. I'm curious how this shows up for others has your team standardized on one model across these tools, or are you juggling several depending on the task? I'd love to hear what patterns you've run into. Cheers, and happy reading. - By Surya Vennapusa, MCT
New Blog Post | Automated Detection and Response for Azure WAF with Sentinel
Full article: Automated Detection and Response for Azure WAF with Sentinel - Microsoft Community Hub Web applications are increasingly targeted by malicious attacks that exploit commonly known vulnerabilities. SQL injection and Cross-site scripting are among the most common attacks. Preventing such attacks in application code is challenging. It can require rigorous maintenance, patching, and monitoring at multiple layers of the application topology. A WAF solution can react to a security threat faster by centrally patching a known vulnerability, instead of securing each individual web application. Azure Web Application Firewall (WAF) is a cloud-native service that protects web apps from common web-hacking techniques. This service can be deployed in a matter of minutes to get complete visibility into the web application traffic and block malicious web attacks. Integrating Azure WAF with Microsoft Sentinel (Cloud Native SIEM/SOAR solution) for automated detection and response to threats/incidents/alerts would be an added advantage and reduces the manual intervention needed to update the WAF policy. In this blog, we will discuss about WAF detection templates in Sentinel, deploying a Playbook, and configuring the detection and response in Sentinel using these templates and the Playbook. Original Post: New Blog Post | Automated Detection and Response for Azure WAF with Sentinel - Microsoft Community Hub
Sentinel Cost Optimization Series - Part 1 - Data prioritization
* There are graphs in this post, but I can't seem to upload/insert them; please visit the link in each part to see the picture. Problem statement Data prioritization is an issue that any SIEM or data gathering and analysis solution must consider. The log that we collect to SIEM is typically security-related and capable of directly creating alerts based on the event of that log, such as EDR alerts. However, not all logs are equally weighted. For example, the proxy log only contains the connections of internal users, which is very useful for investigation, but it does not directly create alerts and has a very high volume. To demonstrate this, we categorize the log into the primary log and secondary log based on its security value and volume. https://i.ibb.co/d4CzxCH/sentinel-cost-optimize-p1-1.png The metadata and context of what was discovered are frequently contained in the primary log sources used for detection. However, secondary log sources are sometimes required to present a complete picture of a security incident or breach. Unfortunately, many of these secondary log sources are high-volume verbose logs with little relevance for security detection. They aren’t useful unless a security issue or threat search requires them. On the current traditional on-premise solution, we will use SIEM alongside a data lake to store secondary logs for later use. https://i.ibb.co/kyc96Dx/sentinel-cost-optimize-p1-architect-onpremise.png Because we have complete control over everything, we can use any technology or solution, making it simple to set up (Eg. Qradar for SIEM and ELK for data lake). However, for cloud-naive SIEM, this becomes more difficult, particularly with https://azure.microsoft.com/en-gb/products/microsoft-sentinel/. Microsoft Sentinel is a cloud-native security information and event manager (SIEM) platform that includes artificial intelligence (AI) to help with data analysis across an enterprise. To store and analyze everything for Sentinel, we typically use Log Analytics with the Analytics Logs data plan. However, this is prohibitively expensive, costing between $2.00 and $2.50 per GB ingested per day depending on the Azure region used. Current Solution Storage Account (Blob Storage) To store these secondary data, the present approach uses https://docs.microsoft.com/en-us/azure/storage/blobs/storage-blobs-introduction. Blob storage is designed to hold large volumes of unstructured data, which implies it does not follow a certain data model or specification, such as text or binary data. This is a low-cost option for storing large amounts of data. The architecture for this solution is as follows: https://i.ibb.co/YhDJvJ8/sentinel-cost-optimize-p1-architect-blob.png However, Blob Storage has a limitation that is hard to ignore. The data in Blob Storage is not searchable. We can circumvent this by using as demonstrated in https://docs.microsoft.com/en-us/azure/search/search-blob-storage-integration, but this adds another layer of complexity and pricing that we would prefer to avoid. The alternative option is to use https://docs.microsoft.com/en-us/azure/data-explorer/kusto/query/externaldata-operator?pivots=azuredataexplorer, but this is designed to obtain small amounts of data (up to 100 MB) from an external storage device, not massive amounts of data. Our Solution High-Level Architecture Our solution used Basic Logs to tackle this problem. https://docs.microsoft.com/en-us/azure/azure-monitor/logs/log-analytics-workspace-overview#log-data-plans-preview is a less expensive option for importing large amounts of verbose log data into your Log Analytics workspace. The Basic log also supports a subset of KQL, making it searchable. To get the log into the Basic Log, We need to use a Custom table generated with the Data Collection Rule (DCR)-based logs ingestion API. The structure is as follows: https://i.ibb.co/2hZ6Gpx/sentinel-cost-optimize-p1-architect.png Our Experiment In our experiment, we use the following component for the architecture: Component Solution Description Source Data VMware Carbon Black EDR Carbon Black EDR is an endpoint activity data capture and retention solution that allows security professionals to chase attacks in real-time and observe the whole attack kill chain. This means that it captures not only data for alerting, but also data that is informative, such as binary or host information. Data Processor Cribl Stream Cribl helps process machine data in real-time - logs, instrumentation data, application data, metrics, and so on - and delivers it to a preferred analysis platform. It supports sending logs to Log Analytics, but only with the Analytics plan. To send the log to the Basic plan, we need to set up a data collection endpoint and rule, please see Logs ingestion API in https://learn.microsoft.com/en-us/azure/azure-monitor/logs/logs-ingestion-api-overview for additional information on how to set this up. And we also use a Logic App as a webhook to collect the log and send it to the Data collection endpoint. The environment we use for log generation is as follows: Number of hosts: 2 Operation System: Windows Server 2019 Number of days demo: 7 The number of logs we collected for our test environment are: Basic Log generated: 30.2 MB Alerts generated: 16.6 MB https://i.ibb.co/HYXyDYL/sentinel-cost-optimize-p1-2.png The cost is based on the East US region, the currency is the USD, and the Pay-As-You-Go Tier was used to determine the number saved using the generated data with 1000 hosts and 30 days retention period. The calculation using only Analytic Log Table Ingestion Volume (GB) Cost per GB (USD) Total cost per day (USD) Total cost per retention period (USD) Host number Retention (Days) Cb_logs_Raw_CL 2.16 2.3 4.96 148.84 1000 30 Cb_logs_alert_CL 1.19 2.3 2.73 81.81 1000 30 Total 7.69 230.66 If we use Analytic Log with Storage Account Table Ingestion Volume (GB) Cost per GB (USD) Total cost per day (USD) Total cost per retention period (USD) Host number Retention (Days) Cb_logs_Raw_CL 2.16 0.02 0.04 1.29 1000 30 Cb_logs_alert_CL 1.19 2.3 2.73 81.81 1000 30 Total 2.77 83.11 If we use Analytic Log with Basic Log Table Ingestion Volume (GB) Cost per GB (USD) Total cost per day (USD) Total cost per retention period (USD) Host number Retention (Days) Cb_logs_Raw_CL 2.16 0.5 1.08 32.36 1000 30 Cb_logs_alert_CL 1.19 2.3 2.73 81.81 1000 30 Total 3.81 114.17 Now let’s compare these 3 solutions together and get an overall look altogether. Only Analytic Log Analytic Log with Storage Account Analytic Log with Basic Log Cost calculated $230.66 $83.11 $114.17 Searchable Yes No Yes but cost $0.005 per GB Retention Up to 2,556 days (7 years) 146,000 days (400 years) Up to 2,556 days (7 years) Limitation Even though the Basic Log is an excellent choice for ingesting hot data, it does have some limitations that are difficult to overlook: The retention period is only 8 days, and this retention can’t be increased, after that, it will either be deleted or archived KQL language access is limited, for a list of what operators can be used, please see https://learn.microsoft.com/en-us/azure/azure-monitor/logs/basic-logs-query?tabs=portal-1#kql-language-limits There is a charge for interactive queries ($0.005/GB-scanned) This is the first post in this Sentinel Cost Optimization series. I hope this helps you have another choice to consider when setting up and sending your custom log to Sentinel.
