Customer Offerings: Solution Optimization for GitHub Copilot
Introduction As Microsoft Cloud Solution Architects, we are increasingly asked by engineering leaders, platform teams, finance stakeholders, and GitHub administrators how they can scale GitHub Copilot while keeping usage visible and costs predictable. The question is no longer simply how many licenses have been assigned. Agentic experiences, model choice, context size, reasoning level, and task complexity can all influence consumption. To address this challenge, Microsoft customers can engage their account team to request a Solution Optimization for GitHub Copilot engagement. The engagement is designed to help customers create visibility, establish practical cost guardrails, and improve the quality and efficiency of GitHub Copilot usage. The objective is not to minimize every token; it is to help each interaction produce useful outcomes with the right level of capability and control. What changed with GitHub Copilot billing? As of June 1, 2026, GitHub Copilot moved from premium request units to usage-based billing with GitHub AI Credits. When a user interacts with an AI-powered Copilot feature, the interaction can consume input tokens, output tokens, and cached tokens. The model used and the number of tokens processed determine the AI credit consumption, where one AI credit represents $0.01 USD. Area What customers should know Included usage Copilot Business includes 1,900 AI credits per licensed user per month, while Copilot Enterprise includes 3,900. These credits are pooled at the billing entity level. Promotional period At the time of writing, existing Business and Enterprise customers receive higher promotional allowances through August 2026. Validate the current allowance before publishing or making financial decisions. Included experiences Code completions and next edit suggestions remain included on paid plans and do not consume AI credits. Metered experiences AI-powered features such as Copilot Chat, Copilot CLI, Copilot cloud agent, Copilot Spaces, Spark, and supported third-party coding agents consume AI credits. Additional consumption Some agentic experiences, including Copilot code review and cloud agent scenarios, can also consume GitHub Actions minutes in addition to AI credits. Important: Pricing, allowances, supported models, and product behavior can change. The official GitHub documentation and the customer’s commercial agreement remain the source of truth. Why optimization is more than cost reduction Token consumption is only the visible part of the problem. A poorly scoped task, unnecessary context, or the wrong model can cause an agent to misunderstand the request, make excessive changes, or require repeated attempts. In that situation, reducing the price of an individual request does not solve the underlying quality problem. A better optimization strategy starts with agent quality. The right model, clear instructions, focused context, good repository guidance, and deterministic validation can help Copilot complete work in fewer attempts. Better outcomes and lower consumption frequently reinforce each other. Introducing Solution Optimization for GitHub Copilot The Solution Optimization for GitHub Copilot engagement provides customers with an opportunity to work with a Microsoft Cloud Solution Architect to review how GitHub Copilot is being adopted, consumed, governed, and measured. The final scope should be agreed with the Microsoft account team and may vary according to the customer’s licensing model, environment, maturity, and priorities. A typical engagement can focus on the following areas: Usage visibility: Establish a current-state view of adoption, AI credit consumption, model activity, license allocation, and the users or workflows driving demand. Billing readiness: Review exported usage data, compare scenarios, and identify where the move to usage-based billing changes planning assumptions. Cost guardrails: Design user-level, cost-center, organization, and enterprise budget controls that protect the shared pool without unnecessarily blocking productive users. Agent quality and token optimization: Identify improvements to model selection, prompt structure, context management, reasoning levels, repository instructions, and validation steps. Operating model: Define ownership, review cadence, escalation paths, reporting responsibilities, and a prioritized backlog of recommended actions. Potential customer outcomes Depending on the agreed scope, the engagement can help the customer develop: A baseline of GitHub Copilot adoption, AI credit consumption, and cost drivers. A view of heavy users, underused licenses, high-consumption models, and agentic workflows that need closer review. A budget and guardrail design that balances shared-pool flexibility with predictable financial control. Practical recommendations for improving agent quality and reducing avoidable retries or context overhead. A prioritized optimization plan with owners, next actions, and measurable follow-up points. A practical engagement approach Discover: Review the customer’s goals, GitHub billing entity, Copilot plans, license assignment model, existing policies, cost centers, and available usage data. Analyze: Use native GitHub reporting and approved accelerators to examine adoption, AI credit usage, model activity, user patterns, and potential cost drivers. Optimize: Map the right model and reasoning level to each task, improve prompts and context, reduce unnecessary tool or repository context, preserve reusable cache, and plan before executing complex changes. Govern: Define user-level budgets, power-user overrides, cost-center or organization controls, enterprise spending limits, alerts, and ownership responsibilities. Measure: Document the baseline, agree on key indicators, and establish a regular review cycle to measure adoption, value, quality, and consumption over time. Tools and accelerators GitHub AI usage, billing exports, and budget controls GitHub’s native AI usage pages and billing reports should be the starting point for understanding consumption. They provide the information required to identify model usage, users, features, and cost patterns. Native budget controls can then be applied at the appropriate user, cost-center, organization, or enterprise scope. GitHub AI usage dashboard, exported usage report, and budget controls GitHub Copilot Billing Preview The GitHub Copilot Billing Preview is an open-source web application for analyzing Copilot billing CSV reports, comparing request-based and usage-based billing signals, and exploring usage and cost trends by user, organization, model, product, and cost center. CSV processing occurs locally in the browser. The application is a preview and planning tool, not the billing source of record. GitHub Copilot Billing Preview showing usage and cost trends Copilot Insights dashboard The community-developed Copilot Insights dashboard provides centralized views of adoption, license allocation, AI credit consumption, model activity, productivity indicators, and team-level reporting. It can complement native GitHub data when a customer needs richer visualization or executive reporting. Customers should review the project’s security, deployment, support, and governance model before production use. Copilot Insights adoption, license, and AI credit views Agent quality and token optimization principles GitHub’s guidance emphasizes that the most sustainable way to reduce AI credit consumption is to improve the quality and efficiency of each interaction. The following principles provide a useful starting point: Choose the right model for the task. Reserve powerful reasoning models for complex architecture, debugging, and design work. Use mid-tier or lighter models for well-scoped implementation, documentation, formatting, and routine refactoring. Provide clear guidance. State the goal, constraints, expected output, relevant files, and validation criteria. Ambiguous prompts often lead to exploration and rework. Keep context lean. Supply the information needed for the task and avoid loading unrelated repositories, files, tools, or instructions into the context window. Preserve reusable context. Stable instructions and cached context can reduce repeated processing, provided they remain relevant and accurate. Research and plan before implementation. For complex work, separate discovery and planning from execution so the agent does not repeatedly rediscover the same information. Add deterministic guardrails. Tests, linting, build validation, explicit stop conditions, and session limits help prevent long-running or low-quality loops. Measure value, not only consumption. AI credit data should be considered together with adoption, developer experience, quality, delivery outcomes, and business impact. Overview video The on-demand session GitHub Copilot - Token Optimization [AMER/EMEA] explains the relationship between token usage and agent quality. The session covers how large language models, agent harnesses, context windows, and available controls influence agent behavior, quality, consumption, and cost. https://www.youtube.com/live/LeALSSsbzHU?si=4RgxflItkQFAs9Qk Helpful inputs before the engagement The following inputs can help the Microsoft team and customer make the best use of the engagement: A GitHub enterprise or organization owner, billing manager, and relevant engineering or platform stakeholders. Current Copilot plan and license counts, including the billing entity and cost-center structure. A representative GitHub AI usage or billing export, handled according to the customer’s data policies. Existing budgets, spending policies, model policies, and reporting processes. Examples of high-volume agent sessions, code review workflows, or teams reporting unexpected consumption. The business outcomes the customer wants to improve, such as adoption, developer experience, delivery speed, quality, or cost predictability. Conclusion GitHub Copilot usage-based billing changes the conversation from license assignment alone to the broader discipline of operating AI-assisted software development. Customers need visibility into how AI credits are consumed, controls that prevent unexpected spend, and engineering practices that help agents complete work accurately and efficiently. The Solution Optimization for GitHub Copilot engagement can help customers connect usage data to practical decisions, improve agent quality, establish appropriate financial guardrails, and create a repeatable approach for measuring and optimizing GitHub Copilot over time. How do I book this engagement? Microsoft Unified Support customers can contact their Customer Success Account Manager (CSAM) or Microsoft account team and ask about Solution Optimization for GitHub Copilot. The Microsoft team can confirm availability, eligibility, scope, prerequisites, and scheduling for the customer’s environment. Resources GitHub Copilot usage-based billing for organizations and enterprises Getting started with GitHub Copilot budget controls Optimizing AI usage to maximize efficiency and reduce cost Models and pricing for GitHub Copilot GitHub Copilot Billing Preview Copilot Insights dashboard GitHub Copilot - Token Optimization [AMER/EMEA] Disclaimer Pricing, plan entitlements, AI credit allowances, supported models, and product behavior are subject to change. Always verify current information in the official GitHub documentation and the customer’s commercial agreement before making purchasing, budgeting, or technical decisions. The sample applications, dashboards, and scripts referenced in this article are provided AS IS without warranty of any kind and may not be supported under a Microsoft or GitHub standard support program unless explicitly stated. Customers are responsible for reviewing security, privacy, compliance, deployment, and operational requirements before using community or open-source solutions in production. This blog post was drafted with the assistance of generative AI and should be reviewed and approved by the author and relevant Microsoft stakeholders before publication.Building an Azure Storage Data Integrity Checker with .NET
Overview of the Application The Azure Storage Manager is a console application that handles two main tasks: verifying data integrity and ensuring secure authentication. Here's a summary of its features: Managed Identity Authentication: Eliminates the need to manage keys or secrets by using Azure's identity framework. MD5 Hash Verification: Calculates MD5 hashes for local files and compares them against Azure metadata to ensure data consistency. CSV Report Generation: Outputs a comprehensive CSV report that shows file integrity status, with Match or Mismatch results for every file processed. Standalone Deployment: The application can be built into a single executable file, making it easy to run without any additional dependencies. Setting Up Managed Identity for Authentication A major design principle for this application was to avoid storing credentials directly in the codebase. Instead, I enabled Managed Identity on the Azure Virtual Machine running this application. Here's how to do that: Enable Managed Identity: Go to your VM in the Azure Portal, navigate to Identity, and enable the System-assigned managed identity. Assign Permissions: Assign the Storage Blob Data Contributor role to this identity for your Azure Storage Account under Access Control (IAM). This will allow the application to perform read and write operations securely. Use Azure SDK: In the .NET application, the Azure SDK's DefaultAzureCredential allows the Managed Identity to be used for authentication seamlessly. Building the .NET Console Application Here are the key parts of the Azure Storage Manager application: Project Structure: The application is a .NET 9 console app, consisting of service classes (BlobStorageService, FileShareService) and utilities for tasks like MD5 hash calculation and CSV export. Authentication Code: Using DefaultAzureCredential, we authenticate to Azure Storage securely: var blobServiceClient = new BlobServiceClient(new Uri($"https://{storageAccountName}.blob.core.windows.net"), new DefaultAzureCredential()); This allows the application to request tokens from Azure's Managed Identity endpoint without having to manage sensitive information manually. File Integrity Verification: The application generates MD5 hashes for local files, compares these with the hashes stored in Azure, and updates the metadata if it detects a mismatch. Here's a snippet showing how we update metadata when necessary: if (blobHash == null || blobHash != localHash) { await blobClient.SetMetadataAsync(new Dictionary<string, string> { { "md5", localHash } }); blobProperties = await blobClient.GetPropertiesAsync(); // Confirm the update } Generating the CSV Report Once the integrity check is complete, the application generates a CSV report (BlobStorageReport.csv) with details for each file, including: File Name Local MD5 Hash Remote MD5 Hash (from Azure Storage) Status (Match or Mismatch) This provides a comprehensive overview of which files have been verified successfully and helps quickly identify any discrepancies. Publishing the Application as a Standalone Executable For deployment convenience, the application can be published as a single executable file. This application was built using .NET 9, providing the latest features and performance improvements. The following command builds the app into a single file with all necessary dependencies included: dotnet publish -c Release -r win-x64 --self-contained true /p:PublishSingleFile=true /p:IncludeAllContentForSelfExtract=true This ensures the app is easy to run on any target machine without needing a pre-installed .NET runtime. Challenges and Lessons Learned Metadata Verification: Initially, I found that metadata updates were not reflected in the first run. I modified the code to immediately re-query the blob properties after writing the metadata, ensuring the changes were correctly applied. Data Integrity Accuracy: During testing, I also learned that removing local file deletions allowed better verification without losing any data locally, which simplified troubleshooting. Managed Identity: Setting up Managed Identity and ensuring the appropriate roles were assigned to the VM was a crucial part of making the authentication seamless. Conclusion This Azure Storage Manager application demonstrates the power of combining Managed Identity for secure authentication with data integrity checks to ensure your files in Azure are always consistent with their on-premises versions. It removes the hassle of managing credentials and makes data integrity verification simple and efficient. If you want to explore the full code or adapt it to your own use case, head over to my GitHub repository (link here). Feel free to leave comments or ask questions—I’d love to hear your thoughts on how this could be improved or extended further. Call to Action Try It Out: Download the code, set up your own Azure VM, and see how it works for you. GitHub: WernerRall147/AzureStorageManager: A tool that helps generate MD5 hashes to compare copied files Leave Feedback: Let me know if you have ideas for improvement or other scenarios where you could see this approach being helpful.How to Find Azure Resources with Public IP Addresses
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