Best Ways to Use Startup Credits

Microsoft for Startups equips thousands of startups worldwide with startup credits, which can be used to build fast on a unified developer-to-cloud path, scale smart with enterprise-ready AI and governance, and prepare to sell more through the Microsoft ecosystem. This article expands the Azure credits baseline into practical ways to spend credits across cloud foundations, GitHub, and eligible Microsoft Foundry model usage.

In this article you’ll learn

• Which Microsoft for Startups credit uses are automatic, which require setup, and which have eligibility limits.
• How to map startup credit spend to cloud foundation categories: compute, containers, data, storage, networking, AI, observability, and security.
• How to use GitHub, Microsoft Foundry, Fireworks models, and Azure Kubernetes Service in a credit-aware architecture.
• How to set review checkpoints so credits increase build velocity instead of masking waste.

Understand what startup credits cover

Startup credits apply to eligible Azure services used through your sponsored Azure subscription. Follow this practical rule: if the service is an Azure product billed as standard Azure consumption, it will draw down credits while they are available on your sponsored subscription. If the service is a non-Azure product, a Microsoft Support Plan, or an Azure Marketplace purchase, it might not be covered.

Using credits for your cloud foundation

The highest-return credit spend for most startups is a secure, observable Azure foundation. The following categories and services in the Azure stack (including their use cases) are noted in the table below. Read about considerations and decision frameworks for platform foundations and scaling workloads.

Cloud foundation category	Common credit-backed services	Best first use
Compute	Azure App Service, Azure Functions, Azure Container Apps, Azure Virtual Machines	Run the product, APIs, background jobs, and experiments without committing to complex infrastructure too early.
Containers	Azure Container Apps, Azure Container Registry, Azure Kubernetes Service	Package services consistently, deploy from GitHub, and graduate to AKS when orchestration needs are concrete.
Data	Azure Database for PostgreSQL Flexible Server, Azure SQL Database, Azure Cosmos DB, Azure AI Search	Store product data, tenant state, search indexes, vectors, and operational metadata.
Storage	Azure Blob Storage, Azure Files, managed disks	Store uploads, model artifacts, generated reports, logs, backups, and document corpora.
Networking	Azure Front Door, Application Gateway, NAT Gateway, private endpoints, virtual networks	Add routing, TLS, static outbound IPs, and private access when customer or workload requirements justify them.
Security and identity	Microsoft Entra ID, Azure Key Vault, Defender for Cloud, Azure Policy	Protect secrets, identities, subscriptions, and deployment guardrails before customer data grows.
Observability	Azure Monitor, Application Insights, Log Analytics	Measure latency, failures, token usage, queue depth, and customer-impacting errors.
Cost control	Azure Cost Management budgets, alerts, exports, tags	Create the measurement loop that keeps credits from becoming unobserved burn.

Tip

Azure Kubernetes Service (AKS) usage on a sponsored subscription is automatically covered while startup credits are available—no configuration or billing setup is required. If you create AKS (including AKS Automatic) resources on your sponsored Azure subscription, usage is automatically covered by startup credits, including compute, storage, and networking costs associated with the AKS clusters provisioned.

Use credits for developer velocity with GitHub

GitHub usage is not covered by startup credits out-of-the-box. You must connect your Azure subscription as a payment method in GitHub before you can apply credits to eligible usage-based GitHub services, noted in the table below.

GitHub service	How startups use it	Credit-aware setup
GitHub Copilot	Accelerate coding, tests, refactors, documentation, and onboarding for small engineering teams.	Confirm your GitHub billing model supports metered billing through Azure, then connect the Azure subscription.
GitHub Codespaces	Create repeatable development environments for contractors, founders, and new hires without local setup drift.	Use prebuilds and spending limits so environments do not run unattended.
GitHub Actions	Build, test, scan, and deploy to Azure from the same repo where product work happens.	Connect billing through Azure for eligible metered usage and keep workflows short, cached, and scoped.
GitHub to Azure deployment	Deploy App Service, Container Apps, Functions, AKS, and infrastructure templates from pull requests.	Use workload identity federation instead of long-lived secrets where possible.

How to connect your Azure subscription to GitHub

1. Sign in to your GitHub account.
2. Go to Billing and Payments in account settings.
3. Select Add payment method and choose Azure subscription.
4. Authenticate with your Azure credentials.
5. Grant permission to validate subscription ownership.
6. Select the Azure subscription you want to use.
7. Confirm the connection.

Once connected, eligible GitHub usage will be billed against your Azure credits.

Build AI products with Microsoft Foundry

Microsoft Foundry is where credits become AI product velocity. Use it to test model quality, build retrieval-augmented generation, evaluate safety, deploy agents, and connect AI workloads to the Azure services that store data, secrets, telemetry, and application logic.

Caution

Models sold directly by Azure and billed through your Azure subscription are covered with your startup credits. Models from partners and community will have separate billing and invoicing. See Microsoft for Startups Sponsorship Coverage for Foundry Models.

AI workload	Credit-backed Azure pattern	What to measure
Prototype a model-powered feature	Deploy Foundry Models sold by Azure, connect from App Service or Container Apps, and store secrets in Key Vault.	Cost per request, latency, successful task completion, and user feedback.
Retrieval-augmented generation	Use Azure AI Search for retrieval, Azure Blob Storage for source documents, and a Foundry model for generation.	Grounded answer rate, retrieval latency, token usage, and source freshness.
Embeddings and search	Use embeddings from eligible Azure-billed models with Azure AI Search vector indexes.	Index cost, query latency, relevance, and storage growth.
Agent workflows	Use Foundry projects, managed identity, Key Vault, and Azure Monitor to govern tools and observe behavior.	Tool call success, error categories, task completion, safety events, and cost per workflow.
Multimodal workloads	Use Azure-billed vision, audio, image, or video models when eligibility and region availability fit.	Input size, output quality, response time, and per-customer cost.

Use Fireworks models on Foundry

Fireworks on Microsoft Foundry gives startups access to selected open-weight and custom model options inside a Foundry project, with Azure governance, access controls, and project management. Because Fireworks models are billed and sold through Azure, startup credits will apply to usage of models.

Use Fireworks on Foundry when	Why it helps	Check first
You need to evaluate open-weight models alongside Azure OpenAI models.	You can compare quality, latency, and cost per task from the same Foundry project.	Confirm the selected Fireworks model, deployment type, region, and credit eligibility.
You need an OpenAI-compatible endpoint for an existing app.	You can test alternative models with fewer application changes.	Verify that model behavior, retirement notice, and preview terms fit your use case.
You have custom model weights that match supported architectures.	Bring your own model can reduce switching costs when a proprietary or fine-tuned model is core to the product.	Review supported architectures, data handling, and compliance limitations.
You need provisioned throughput for predictable high-volume inference.	Provisioned capacity can make cost and latency more predictable for known production traffic.	Use it after you have measured real traffic, not during early experiments.

Track credit spend confidently

Credit consumption can feel safe because it does not hit the credit card immediately. Treat that as a temporary buffer, not a substitute for cost control. Credits eventually run out, and the subscription transitions to pay-as-you-go so applications can keep running.

Control	Why it matters	Weekend setup
Budgets and alerts	Azure budgets notify you when actual or forecasted spend crosses thresholds.	Create monthly subscription budgets at 50 percent, 80 percent, and 100 percent of expected credit burn.
Required tags	Tags let you answer which environment, team, customer, or feature caused spend.	Require environment, owner, workload, and stage tags by policy or deployment templates.
Service-level review	Early startups often discover spend concentration in AI, databases, storage, or idle compute.	Review top services monthly and connect each large line item to a product decision.
Model usage metrics	AI spend scales with tokens, context, retrieval, retries, and user growth.	Track cost per successful task, not only cost per token.
Idle cleanup	Credits are often lost to nonproduction resources, stale disks, unused public IPs, and test clusters.	Add a recurring cleanup issue in GitHub and assign it to the engineer rotating on releases.

Tip

Don’t wait until credits are nearly gone to understand unit economics. The best time to measure cost per active customer, cost per workflow, or cost per million tokens is while credits still give you room to change architecture.

Common Questions

What happens when my Azure credits run out?
Your Azure subscription automatically transitions to pay-as-you-go, ensuring your applications continue running without interruption.

Why do I need to add a credit card?
A credit card is required for identity verification, services not covered by credits, and automatic transition to pay-as-you-go when credits expire.