Google Cloud AI credits
Google publicly lists up to $350K in credits for eligible AI-first startups using or planning Vertex AI or Gemini.
AI startup credits
AI startup? You may be able to get up to $350K in cloud credits.
We check Google Cloud AI credits, AWS Activate, Azure startup credits, funded AI work, model/API pricing, migration support, and cost audit routes.
If AI is part of your product, you may be eligible to check major startup cloud-credit routes: Google Cloud publicly lists up to $350K for eligible AI-first startups, AWS Activate publicly lists up to $200K for eligible startups, and Microsoft lists up to $150K in startup credits over time. The partner review checks which route fits your stage, workload, prior credits, spend, and provider setup.
Paths we check
The right answer is not always the same benefit. We look at the case before forcing a path.
AWS Activate and AI route
AWS publicly lists up to $200K in Activate credits, with additional AI startup support for some scale-stage cases.
Azure startup credits
Microsoft publicly lists startup credits that can unlock over time, with Azure AI and Microsoft Foundry routes to review.
Funded AI work and model pricing
If credits are not the cleanest route, check funded POCs, migration support, cost audit, or Gemini/Claude/API pricing routes.
Good fit
- + AI is part of the product customers use, not only an internal tool.
- + You use or plan to use inference, agents, RAG, model training, video AI, document AI, data pipelines, GPUs, Vertex AI, Gemini, Claude, Bedrock, Azure AI, or similar workloads.
- + You have current spend, projected spend, or a launch that will create cloud usage.
- + Funding, customer rollout, pilot deployment, or migration timing explains why usage will grow.
- + You are open to credits, funded AI work, model/API pricing, discounts, payment terms, migration support, or cost audit.
Weak fit
- - AI is only a pitch-deck label with no model, data, inference, API, or deployment workload.
- - No provider preference, service plan, spend estimate, or customer timeline.
- - The team wants credits for third-party tools that are not part of AWS, Google Cloud, Azure, or a partner route.
- - No funding, customer usage, pilot, or launch trigger.
- - The company refuses to share enough technical detail for review.
How the check works
1
Document AI workload, model/API usage, provider services, spend, funding, and launch timing.
2
Check Google Cloud, AWS, Azure, funded AI work, model/API pricing, migration, and cost audit routes separately.
3
Decide whether the case is approved for partner review, needs more evidence, or is not a clean credit route.
4
Package the case around usage and business growth, not generic AI language.
Detailed guide
The operator version
Practical checks, edge cases, and decision rules for this route. No generic provider-program summary.
If you are an AI startup, the main routes to check are:
- Google Cloud: up to $350K for eligible AI-first startups.
- AWS Activate: up to $200K in credits for eligible startups.
- Microsoft for Startups: up to $150K in startup credits over time.
- Funded AI work: POCs, migration, model deployment, data architecture, security, or optimization.
- Model/API pricing: Gemini, Claude, Vertex AI, Bedrock, Azure AI, and marketplace routes.
The review is not "are you an AI company?" The review is "which route can your AI workload actually support?"
The evidence that matters
To approve an AI case for partner review, collect:
- Product type: agent, RAG, document AI, video AI, medical AI, robotics, architecture, analytics, security, or model platform.
- Workload: training, inference, fine-tuning, evaluation, data pipeline, vector search, GPU, API calls, batch jobs, or customer deployment.
- Provider relevance: Vertex AI, Gemini, BigQuery, GKE, SageMaker, Bedrock, Azure AI, Azure OpenAI, GPUs, databases, storage, or networking.
- Current spend and projected spend.
- Funding, customer pilots, contracts, or launch dates.
- Prior credits and expiry timing.
- Whether the team needs credits, AI API pricing, funded work, migration support, payment terms, or cost optimization.
If those details are missing, "we are an AI startup" is not enough.
Examples from AI partner calls
| What we saw on the call | Internal outcome | Eligible to check |
|---|---|---|
| Medical AI team on GCP; prior AWS credits expired; Google credits almost consumed | Approved for AI partner review | Google AI credits up to $350K, cost audit, funded migration, Vertex/Gemini work |
| Robotics AI company interested in Gemini and Claude pricing | Approved if usage/account data confirms | Google credits, model/API pricing, funded AI POCs |
| Architecture AI platform planning GCP migration with large projected spend | Approved for migration review | Google startup credits, migration support, funded technical help |
| Conversational AI startup with technical founder and likely compute-heavy product | More evidence needed | Projected spend, provider route, AI credit eligibility |
| Company using AI only as an internal helper | Not a strong AI-credit case | Cost audit or general commercial review |
Clear rule: AI helps only when it explains cloud usage.
What you can get
| Route | What it can mean | Best fit |
|---|---|---|
| Google Cloud AI credits | Up to $350K for eligible AI-first startups | Seed to Series A, Vertex AI/Gemini plan, limited prior Google credits |
| AWS Activate | Up to $200K in AWS Activate credits | Pre-Series B, AWS workload, Activate eligibility or provider route |
| Azure startup credits | Up to $150K over time through Microsoft for Startups | Azure AI, Microsoft ecosystem, marketplace/co-sell fit |
| Funded AI work | Partner-funded POC, migration, architecture, security, or optimization | Real AI project with provider value |
| Model/API pricing | Gemini, Claude, Vertex AI, Bedrock, Azure AI, marketplace routes | High API/model usage or upcoming production rollout |
Do not mix all AI spend together
Separate these before asking for credits:
- Infrastructure spend.
- Managed AI service spend.
- Model API spend.
- Marketplace or third-party vendor spend.
- Engineering work needed to move or optimize the workload.
This avoids the common mistake: treating every AI cost as if one cloud-credit program can cover it.
Fast qualification questions
Ask these before routing the case:
- What is the AI feature or product?
- Which workloads run today?
- Which workloads will grow in the next 90 days?
- Which provider services are required?
- What is current monthly spend?
- What will spend become after launch or customer rollout?
- Have you used AWS, Google Cloud, or Azure credits before?
- Are credits the only goal, or would funded work, discounts, payment terms, or API pricing help?
The answers show whether this is a real AI infrastructure case or just a startup looking for free infrastructure.
When funded work is better than credits
Some AI startups need help more than a credit balance:
- Migrating a model or data pipeline.
- Building a POC with a provider service.
- Optimizing inference cost.
- Setting up governance, security, or observability.
- Moving from on-prem or a smaller provider to a hyperscaler.
- Validating whether Vertex AI, Gemini, Bedrock, SageMaker, Azure AI, or another service fits.
That can be a stronger route than another credit request.
Weak AI cases
Be careful with:
- No model, no data, no inference, no API usage, no deployment.
- "AI" only appears in marketing language.
- No current or projected spend.
- No funding, customers, or launch timing.
- The desired provider has no technical relevance.
- The request is for third-party tools, but the pitch is framed as cloud credits.
Those leads may still be worth nurturing, but they are not strong AI credit cases yet.
Check your path
The quiz takes about 60 seconds and helps route credits, discounts, terms, project funding, or funded help.
Step 1 of 714% complete
About the author
Neta Arbel
Founder, CloudCredits
Neta Arbel builds outbound and partner-led growth systems for cloud companies and startup infrastructure offers. He started working with startups at 17 and now focuses on helping funded startups understand which cloud credits, payment terms, discounts, project funding, or funded technical help may be available before they book a partner call.
Common questions
How much cloud credit can an AI startup get?
Public program ceilings include up to $350K through Google Cloud for eligible AI-first startups, up to $200K through AWS Activate, and up to $150K through Microsoft for Startups over time. The actual route depends on eligibility, workload, prior credits, spend, and provider review.
Can AI startups get credits after already using AWS or Google credits?
Sometimes. Prior usage can help if it proves real demand, but the partner call needs to check what changed: AI workload, spend growth, funding, customer rollout, migration, or credits expiring soon.
Do Claude, Gemini, or model API costs count?
They need a separate review. Gemini, Vertex AI, Claude, Bedrock, Azure AI, marketplace, and third-party model costs may fit different routes. Do not assume one credit program covers all AI spend.
Can an AI startup get funded technical help instead of credits?
Yes, in some cases. Architecture, migration, model deployment, data work, security, and optimization can be stronger routes when the AI project gives a provider or partner a reason to support it.
What AI evidence makes the case approved for review?
Model serving, inference, agents, RAG, GPU usage, Vertex AI, Gemini, Bedrock, Azure AI, data pipelines, customer deployments, current spend, and projected monthly usage make the case concrete.
Should AI startups choose AWS, Google Cloud, or Azure for credits?
Choose by workload fit, not the headline credit number. Google may fit Vertex AI or Gemini, AWS may fit existing AWS or Bedrock/SageMaker workloads, and Azure may fit Microsoft ecosystem, Azure AI, Foundry, or enterprise customers.