For most AI startups, Google for Startups Cloud Program offers the highest ceiling (up to $350,000 for AI-first companies), Microsoft for Startups is the simplest path for teams building on OpenAI models through Azure (up to $150,000), and AWS Activate (up to $100,000) suits teams that want Amazon Bedrock, SageMaker and the widest choice of managed services. The best choice depends less on the headline number and more on which models, GPUs and services your product actually needs.
Compute is usually the second biggest cost for an AI company after salaries. Inference bills grow with every user, GPU instances for fine-tuning are priced by the hour, and storage for training data keeps growing in the background. Startup cloud credits exist to absorb these costs during the period when a young company is still testing its product and pricing. This guide compares the three largest programs side by side so founders can apply to the right one first instead of spreading thin across all three.
What are startup cloud credits?
Startup cloud credits are prepaid balances that a cloud provider applies to your account to cover usage of its services. They work like a gift card tied to one platform. You still receive a normal bill, but the credit balance pays it until the credits run out or expire.
Providers offer them because switching clouds is expensive. A startup that builds its data pipelines, model hosting and authentication on one platform tends to stay there as it grows. Giving away credits early is a customer acquisition cost for the provider, and a runway extension for the founder.
Credits normally come with three conditions: an expiry date, a list of eligible services, and eligibility rules based on company age, funding stage or partner affiliation. Reading those three conditions before applying matters more than the headline figure.
How the three programs compare at a glance
| Program | Maximum credits (2026) | Typical path to the top tier | AI services covered |
| AWS Activate | Up to $100,000 | Referral through an Activate Provider such as an accelerator, incubator or VC firm | Amazon Bedrock, SageMaker, EC2 GPU instances |
| Google for Startups Cloud Program | Up to $350,000 for AI-first startups | Funded startups, with the largest packages reserved for AI-focused companies | Vertex AI, Gemini models, GPU and TPU instances |
| Microsoft for Startups | Up to $150,000 | Startups backed by investors in Microsoft’s partner network | Azure OpenAI Service, Azure AI Foundry, GPU virtual machines |
Figures reflect the published maximums at the time of writing. Each provider adjusts amounts and rules regularly, so confirm the current terms on the official program page before you apply.
AWS Activate: best for breadth of services
AWS Activate has two broad routes. Self-funded founders can apply directly for a smaller credit package, while startups referred by an Activate Provider (a partner accelerator, incubator or venture firm) can receive packages of up to $100,000.
The strength of AWS for AI companies is choice. Amazon Bedrock gives access to foundation models from several companies, including Anthropic’s Claude models, through one API. SageMaker covers training and deployment for teams running their own models, and EC2 offers a wide range of GPU instance types. If your product mixes AI features with conventional backend work such as queues, databases, search and file storage, AWS lets you run all of it on one bill that your credits cover.
The trade-off is complexity. AWS has hundreds of services and pricing models, and new teams can burn credits quickly on resources they forgot were running. Budget alerts in AWS Billing should be set up on the first day.
Google for Startups Cloud Program: best for AI-first companies
Google’s program has the highest ceiling of the three. Standard packages cover early-stage and funded startups, and companies that are AI-first can qualify for up to $350,000 in credits spread across the program period.
For AI teams, the main draw is Vertex AI, Google’s platform for building with Gemini models and other foundation models, plus access to both GPUs and Google’s own TPUs. TPUs can be cost-effective for certain large training jobs, which matters if you plan to train or fine-tune at scale rather than only calling APIs.
Google also tends to pair credits with technical support and access to startup-focused events and training. The catch is eligibility: the largest packages are aimed at funded companies with a clear AI product, so a two-person team with a prototype will usually start at a lower tier.
Microsoft for Startups: best for teams building on OpenAI models
Microsoft for Startups offers Azure credits of up to $150,000 for startups connected to its investor network, with a smaller self-serve tier for earlier companies. The program has changed shape several times since it launched as Founders Hub, so the current tiers are worth checking directly.
Its biggest advantage for AI founders is Azure OpenAI Service. Teams that already prototype with OpenAI’s models can move to Azure and pay for usage with credits, while gaining enterprise features such as private networking and regional data controls that larger customers often ask for. Microsoft also bundles developer tools like GitHub and Microsoft 365 benefits into the startup offer.
If your roadmap involves selling to enterprises that already run on Microsoft, building on Azure can also shorten security reviews later, which is an advantage that has nothing to do with the credit amount.
Which program should you apply to first?
Start with the cloud that matches your technical stack, not the biggest number. Credits on a platform you will not use are worth nothing, and migrating a half-built product to chase a larger package costs engineering time you probably cannot spare.
A practical way to decide is to look at your model dependency. Teams built around Claude models often find AWS Bedrock the most natural fit. Teams built around Gemini or planning heavy training on TPUs will get more from Google. Teams built around OpenAI models usually save the most integration work on Azure.
Your funding stage is the second filter. Without institutional investors or an accelerator, you will qualify for entry tiers almost everywhere, so apply to the one that matches your stack and plan to upgrade after you raise or join a program. If you are already in an accelerator, ask which cloud partnerships it has, because a referral is often the only way to reach the top tier.
Founders who want a running comparison of tiers, eligibility rules and current amounts can compare startup cloud credit programs side by side before applying, then confirm the final terms with the provider.
Can you stack credits from more than one provider?
Yes. Nothing stops a startup from holding AWS, Google Cloud and Azure credits at the same time, and many AI companies do. A common pattern is running the main application on one cloud while using a second provider’s credits for a specific workload, such as GPU-heavy fine-tuning or a model that is only available on that platform.
Stacking makes sense once your architecture is stable. Early on, splitting infrastructure across clouds adds networking costs, duplicated tooling and more places for credits to leak. Most teams are better served by committing to one primary cloud for the first year and adding a second only when there is a clear technical reason.
Smaller programs can sit alongside the big three without adding complexity. Oracle for Startups and the Oracle Cloud Free Tier provide Arm-based compute that is free permanently within limits. AI API companies such as Deepgram and ElevenLabs offer starter credits or grants for speech and voice products, and NVIDIA Inception gives startups access to technical resources and partner offers.
How to make cloud credits last longer
The most common way startups lose credit value is idle GPU time. A single high-end GPU instance left running over a weekend can consume thousands of dollars of credits. Automating shutdown of development instances outside working hours is one of the cheapest controls you can put in place.
The second is model choice. Many product features work well with smaller, cheaper models, and routing only complex requests to large models can cut inference spend substantially. Caching repeated prompts and responses helps for the same reason.
The third is planning for the end of the credits. Credits usually expire within one to two years, and the month they run out is when many startups first see their real infrastructure cost. Tracking your projected monthly bill at full price from the start keeps that moment from becoming a crisis.
Finally, apply only through official program pages. Sellers offering “pre-approved” or pre-loaded cloud accounts operate outside provider terms, and those accounts can be suspended without notice, along with any data and workloads running on them.
Frequently asked questions
Do I need venture funding to get startup cloud credits?
No. AWS, Google and Microsoft all have entry tiers that bootstrapped founders can apply for with a company website and a clear product description. Funding or an accelerator referral is usually needed only for the largest packages.
Do startup cloud credits cover GPU instances?
In most cases, yes. Credits generally apply to GPU virtual machines and managed AI services on each platform, although some third-party marketplace products and certain reserved purchases may be excluded. Check the eligible services list in your program terms.
How long do cloud credits last?
Expiry periods vary by provider and package, but most startup credits expire between one and two years after they are granted. Unused credits are not refunded or converted into cash.
Can I use credits to pay for models like Claude, Gemini or GPT?
Often, yes, as long as you access them through the cloud provider. Claude models are available through Amazon Bedrock, Gemini through Google Vertex AI, and OpenAI models through Azure OpenAI Service. Direct API subscriptions bought from the model companies themselves are billed separately.
Is it worth applying to all three programs?
Apply to the one that matches your stack first. Add others when you have a concrete workload for them. Holding unused credits on several platforms adds administrative work and rarely saves money.
Final thoughts
AWS Activate, Google for Startups Cloud Program and Microsoft for Startups can each cover a large share of an AI startup’s infrastructure costs during its first one to two years. The right choice comes down to three questions: which models your product depends on, which cloud your team already knows, and how far along you are in fundraising. Answer those honestly, apply to one program well, and treat the credits as runway with a fixed end date rather than free money.
