A Founder's Guide to GenAI Proofs-of-Concept on Google Cloud

The GenAI POC is the dominant form of early-stage corporate AI work. Six to twelve weeks. Constrained budget. A target executive who wants to see whether this is real before approving anything bigger. The wrong outcome isn’t usually “the POC failed.” It’s “the POC was inconclusive and now nobody can decide what to do next.”

This post is the playbook we’d give a founder or engineering leader running their first GenAI POC on Google Cloud. Some of it overlaps with our full engagement playbook; think of this as the compressed version aimed at the smaller initial commitment.

Before you start: get the credits

Google Cloud offers several credit programs that are directly relevant to GenAI POCs. They go underused because nobody tells founders they exist. Worth pursuing before you spend your own money on compute.

Google for Startups Cloud Program. If you’re a startup building on AI, the program’s AI tier offers up to $350,000 in cloud credits over two years — the right answer for most GenAI POCs. The non-AI Scale tier (VC-backed startups generally) caps at $200,000 over two years, and the pre-funded Start tier caps at $2,000 over one year. Tiers are based on funding stage and AI focus. Apply through the program portal; the process takes a few weeks.

Cloud free trial. $300 in credits for new GCP accounts, valid 90 days. Sufficient for a small POC or for prototyping.

Always-free tier. Cloud Run, BigQuery (with caveats), Cloud Run functions (the rebranded Cloud Functions, now folded into Cloud Run), Pub/Sub, and Firestore have free tiers that are real, not marketing. For low-traffic POCs, you can run almost entirely on the free tier.

Vertex AI free credits. Periodic promotional credits specifically for Vertex AI Gemini usage. Worth asking your Google account team about (every GCP customer has an account team, even if you’ve never spoken to them).

Partner-funded POCs. Some partners (including us) will run POCs at reduced rates with partial Google funding under specific programs. Worth asking about.

The credit landscape changes. Check the current state in the GCP console under Billing > Promotions, and ask the account team directly.

Defining the POC: the three questions

Every GenAI POC should answer three questions before any code is written.

1. What decision will this POC inform?

A POC is a decision-support exercise. The decision is usually one of: should we invest in a full build, should we change vendor or platform, should we kill this idea. Without a defined decision, the POC has no end condition.

Write the decision down. Share it with the executive sponsor. Confirm they agree this is the decision the POC will inform. Surprisingly often, this conversation surfaces that different stakeholders are running the POC for different decisions.

2. What’s the measurable success criterion?

“It works” is not a success criterion. “It achieves 80% accuracy on a defined test set with 200 examples” is. “Users in the pilot rate the experience 4 or higher on average” is. “It reduces our manual processing time by at least 50% in a controlled comparison” is.

This needs to be defined before you start building. If you can’t define it, you don’t know what you’re trying to build. See our evaluation guide for the eval mechanics.

3. What’s out of scope?

A POC is constrained by definition. Production-grade security, full integration with downstream systems, polished UI, scale testing, multi-tenant deployment, internationalization. These are all things a real production system needs and a POC doesn’t. Be explicit about what you’re not doing, so success isn’t undermined by missing capabilities that were never in scope.

Scoping a 6-week POC on GCP

A reasonable shape for a 6-week GenAI POC on GCP:

Week 1-2: Scoping + eval set + baseline
   - Define decision, success criterion, scope
   - Identify or build a 50-150 example eval set
   - Stand up baseline implementation (stock `gemini-2.5-flash`, minimal prompt)
   - Measure baseline against eval set

Week 3-4: Iteration
   - Improve toward success criterion: prompt engineering, grounding,
     retrieval, model selection
   - Multiple measured iterations against the eval set
   - Cost and latency tracking

Week 5: Internal user testing
   - 5-15 internal users
   - Structured task-based feedback
   - One round of iteration based on feedback

Week 6: Documentation + decision
   - Final eval scores
   - Cost and latency analysis
   - Production-readiness gap analysis
   - Decision memo for the executive sponsor

For a 4-week POC, compress weeks 3-4 into one week of iteration and skip the formal user testing in week 5 (do it informally throughout). For an 8-week POC, add more iteration time or scope-expansion time.

Technology choices for the POC

You don’t need much. The right starting stack:

• Cloud Run for the API surface. Stateless, scales to zero, almost-free at POC traffic levels.
• Vertex AI gemini-2.5-flash for generation. Start with Flash; only escalate to gemini-2.5-pro if Flash measurably falls short.
• Vertex AI Search (now branded Agent Search; APIs still use the Vertex AI Search / Discovery Engine endpoints) for grounding if your POC involves a knowledge corpus. Fastest path to a working retrieval setup.
• BigQuery for storing the eval set, logging interactions, and analyzing results. Free tier covers POC volumes.
• Cloud Logging for observability. Built-in, no setup required.

You don’t need: AlloyDB (unless your POC needs persistent transactional data), Agent Builder (now part of the Gemini Enterprise Agent Platform, the Cloud Next 2026 umbrella covering Agent Builder, ADK, Agent Engine, Agent Studio, Agent Garden, and Agentspace — unless you specifically need multi-step tool use), Pub/Sub (most POCs are synchronous), GKE (almost never appropriate for a POC).

The instinct to build the production architecture during the POC is almost always wrong. The POC is for proving the AI part works. The infrastructure can grow up afterward.

What to harden before piloting with real users

If the POC succeeds and you’re moving to a pilot (real users, even a small number), here’s what becomes non-optional:

Auth and access control. Identity-Aware Proxy in front of Cloud Run. Real user accounts, not shared service credentials.

Logging that doesn’t leak PII. If you’re handling personal data, log structured events with the PII redacted. The full data can live in a more controlled store with shorter retention.

Rate limiting. Per-user quotas. Cloud Run can enforce these at the load balancer level.

Error handling. Real users see real errors. The system needs structured error responses, not stack traces.

Cost alerting. Set a Cloud Billing budget alert at 50% and 80% of your monthly spend ceiling. POCs sometimes get exciting and then expensive.

Basic monitoring. Cloud Monitoring dashboards for request volume, latency, error rate, model token usage. Alerting on anomalies.

A way to turn it off. A feature flag or a kill switch. If the system starts behaving badly, you need to be able to disable it without redeploying.

This is the minimum. It’s not production-grade. But it’s enough to put in front of real users without creating an incident.

Migrate the POC or rewrite it?

If the POC succeeds, the next question is whether to build the production system on the POC code or rewrite it.

Migrate (extend the POC) when:

• Architecture choices were considered and remain valid for production scale
• Code quality is reasonable (test coverage, clear structure, no major shortcuts)
• The team that built the POC is the team that will own the production system

Rewrite when:

• POC was time-boxed and shortcuts were taken (hardcoded values, mocked components, “we’ll fix this later” comments)
• Production scale or latency requirements are materially different
• The production team is different from the POC team and doesn’t have the context

In our experience, most POCs that were genuinely scoped as POCs need at least a substantial rewrite of the application layer. The infrastructure (Cloud Run, Vertex AI, BigQuery) usually carries over directly. The prompts, the eval harness, the data layout, the integrations get rebuilt with production discipline.

This isn’t waste. The POC’s job was to prove the approach. The production build’s job is to deliver the system.

Decision frameworks for after the POC

Once the POC is complete, the decision the sponsor has to make depends on the outcome:

POC clearly succeeds. Move to a production build. Allocate budget. Plan a 12 to 16 week production engagement. The POC team’s eval harness, prompts, and architecture carry forward.

POC clearly fails. Kill it or pivot. Document what failed and why (often the bottleneck is data quality, not model quality). Sometimes the right next step is data investment, not another POC.

POC inconclusive. Hardest case. Usually means the success criterion was wrong or the scope was too narrow. Extend by 2 to 4 weeks with a refined criterion, or restart the scoping conversation.

The inconclusive case is the one to avoid. Hence the upfront discipline on defining what success means.

Working with Google’s account team

You probably have one and haven’t met them. Find your Google Cloud account team via the GCP console (Support > Account Team) or by emailing your billing contact.

What they can help with: credit programs, technical architecture review, introductions to specialists (Solutions Architects, AI Specialists), introductions to validated partners (including us, where appropriate). What they can’t help with: free engineering time. Their job is to make GCP successful for your account, not to staff your project.

For startups specifically, the Google for Startups team is the right entry point. They have a more relationship-driven model and meaningful credit packages.

How Accelyze helps

Accelyze runs GenAI POCs on Google Cloud for clients who need to make an investment decision in 4 to 6 weeks. The engagement covers scoping, eval design, baseline implementation, iteration to threshold, and a decision memo for the executive sponsor. We work with the Google account team where appropriate to identify applicable credit programs. If you’re scoping a GenAI POC and want a partner who treats it as a decision-support exercise rather than an open-ended exploration, get in touch.