How to Choose a GenAI Consultancy on Google Cloud

Choosing a consultancy for a GenAI engagement is harder than it should be. Every firm has the same website. Everyone has case studies that are light on specifics. Every team is “AI-first.” The marketing has converged to the point where it’s almost no help in deciding.

This post is the buyer’s-side framework we wish more clients used. Some of it overlaps with how we’d hope to be evaluated. Most of it applies to evaluating any consultancy in the space, including ones we’re competing with. It’s worth publishing either way because the alternative (vague scoping conversations that don’t lead anywhere good) hurts everyone.

What a “GCP-aligned consultancy” should actually mean

The phrase is overloaded. Useful to be specific about what it implies, and what it doesn’t.

A serious GCP-aligned GenAI consultancy is using the products that differentiate Google Cloud, not just running generic infrastructure that happens to be hosted there. Specifically, you should see evidence that they work with:

• Vertex AI as the model platform, not just as a thin API wrapper. Real use of Gemini, the Agent Development Kit (ADK) and Agent Engine (both part of the Gemini Enterprise Agent Platform, the Cloud Next 2026 umbrella over Agent Builder, ADK, Agent Engine, Agent Studio, Agent Garden, and Agentspace), Vertex AI Search (now branded Agent Search; APIs still use the Vertex AI Search / Discovery Engine endpoints), Model Garden, the Vertex AI Gen AI evaluation service (Vertex AI Evals), and Model Armor for prompt-injection and PII filtering.
• BigQuery as more than a storage layer. Use of VECTOR_SEARCH, integration with the eval and analytics workflow.
• AlloyDB or Cloud SQL where transactional needs warrant. Not defaulting to “spin up Postgres on a VM.”
• The native security and observability story. VPC-SC, CMEK, Cloud Logging, Cloud Trace, and the Vertex AI Gen AI evaluation service.

A consultancy whose architecture for every problem is “Cloud Run plus an OpenAI API key” is not really a GCP partner, regardless of what their logo strip says. They’re a generic cloud consultancy that happens to deploy on GCP. That’s a different thing, and it shows up in the depth of the work.

What partner status does and doesn’t tell you

Google’s partner program has structure. A few things to look at, and a few things to discount.

Specialization status is more informative than partner tier. Google’s partner network organizes capability into specializations (Applied AI, Data Analytics, Infrastructure, Security, Workspace, plus industry-specific ones). Specializations require validated customer success and technical assessments. A firm with the Applied AI specialization has demonstrated capability against an external bar. A firm with no specialization but a “Premier” badge has cleared a revenue threshold, which is different.

Newer firms can be excellent. Specialization requires shipped customer references. A firm that’s six months old can’t have them yet. That doesn’t mean the team is junior, it means the corporate entity is young. Look at where the founders shipped before. Real GenAI engineering experience compounds slowly and is mostly carried in people, not corporate badges.

Logos on the website are not references. Until you’ve spoken to a named contact at a logo-on-website client, you don’t know what the engagement looked like. Some firms list logos based on a single workshop or a stalled POC. Ask for references in your vertical and your use-case category.

Certifications matter less than you’d expect. Individual certifications (Professional Cloud Architect, ML Engineer, etc.) are a baseline. They tell you the person passed an exam. They don’t tell you the person can scope a 90-day engagement or design an eval harness.

The signals that actually predict good delivery

After running engagements on both sides of the table for years, here’s what we’ve learned to look for in a consultancy. Some of these are obvious. Several aren’t.

Technical content depth. Does the firm publish opinionated, specific technical writing? Architecture posts that name products and trade-offs? Decision frameworks with worked examples? Or is it generic “AI is transformative” marketing? The variance between firms on this dimension is huge, and it tracks reasonably well with engineering judgment.

A defined engagement methodology. Ask the firm to describe how they’ll run a 90-day engagement. What are the phases? What are the gates? What does each phase produce? A firm with a clear methodology will answer specifically. A firm without one will answer vaguely. The “we’re agile and adapt to client needs” answer is a yellow flag; in practice it often means “we don’t have a methodology.”

Willingness to say no. A consultancy that will tell you a use case isn’t ready, that the data doesn’t exist, that the value isn’t there, or that the eval threshold isn’t achievable is worth more than one that will agree to anything. The latter ends up with delivery failures that you pay for. Test this in the scoping conversation: bring a use case you suspect is half-baked and see what they say.

Eval-first orientation. Does the firm talk about evaluation as an afterthought, or as the first thing? GenAI delivery without an eval harness is delivery without a definition of success. Listen for whether evals come up unprompted in their scoping conversation.

Clarity on the handover. What happens after the engagement ends? A serious consultancy will describe runbooks, architecture decision records, an eval harness extension guide, and a hypercare plan. A less serious one will describe “documentation” without specifics.

Cost transparency. Will the firm tell you what a typical engagement costs before you sign? Will they break down where the cost goes? Firms that can’t or won’t are usually managing a margin model they don’t want to discuss.

Questions to ask in a scoping conversation

A short list of questions that distinguish consultancies in the first call. These work because they’re hard to answer well without having actually done the work.

1. “Walk me through a recent engagement you ran on GCP. What was the architecture, and why those product choices?” The answer should be specific. If it’s vague or could apply to any cloud, the firm doesn’t have the depth they claim.

2. “What does your eval harness for this kind of use case look like?” A serious answer describes a golden set, scoring functions, baseline measurements, and integration with the Vertex AI Gen AI evaluation service or equivalent. A weak answer is “we’d build that out as part of the engagement.”

3. “What’s the most common reason your engagements fail or get scope-cut?” A firm that can’t answer this candidly is either inexperienced or unwilling to be honest. The good firms have an answer ready and it usually involves data quality, sponsor changes, or unclear success criteria.

4. “How do you handle the handover to my team after the engagement?” Listen for specifics: documentation deliverables, runbook formats, training sessions, hypercare period, escalation paths.

5. “Where in your architecture do you make trade-offs we should know about?” A firm with strong architectural opinions will have specific answers (e.g., “we default to AlloyDB pgvector for retrieval until corpus size justifies Vertex AI Vector Search 2.0, here’s the threshold and reasoning”). A firm without will give general answers.

6. “What would you push back on us about, based on what we’ve told you so far?” This is the willingness-to-say-no test. A firm that has no pushback at all on a brand-new scoping conversation is probably not engaging deeply with what you’ve described.

What to look for in the proposed engagement

When the firm proposes an engagement, the document itself tells you something. The right shape:

• Phases with explicit gates. Not just a timeline.
• Deliverables tied to each phase. Not “we’ll deliver a working system in 12 weeks.”
• Success criteria stated as numbers. Eval thresholds, latency targets, cost targets.
• Out-of-scope items explicit. What you’re not building. Surprisingly important.
• The team named. Who specifically will be on the engagement, what their roles are. Not just a logo and a “team of experts.”
• An acceptance process. How you’ll formally accept that each phase is complete.

If the proposal is two pages of bullet points and a price, that’s a signal about how the engagement will run.

Red flags

A few patterns we’ve seen, in approximately decreasing order of how often they predict trouble:

“We’ll figure out success criteria during the engagement.” No. Success criteria define when the engagement ends. They have to be in the proposal.

“This will take a year.” Most GenAI use cases don’t justify a year-long engagement, especially as a first project. A firm that proposes 12-month engagements as the default is either using your project to build their bench or hasn’t decomposed the problem properly.

“We need to scope it after we get started.” Some discovery is reasonable. “Scoping after starting” usually means the firm doesn’t know what they’re going to build and is hoping it’ll become clear later.

Architectures with no opinions. A proposal that lists every possible GCP product without saying which ones will be used and why is a sign the firm doesn’t have a strong default. You want a firm with defaults, even if you push back on them.

Cost models that are mostly hourly. Fully hourly billing aligns incentives badly. Mixed models (fixed-fee phases plus hourly for defined scope changes) are more standard for mature consultancies.

Vague “AI experts” without named individuals. Who specifically will be doing the work? If the answer is unclear in the proposal, it’ll be unclear in delivery.

A pilot is the right way to evaluate a new firm

For any consultancy you haven’t worked with before, a small paid pilot is the right risk control. Four to six weeks, defined deliverables, defined success criteria, an explicit option to expand or not expand into a larger engagement.

Pilots benefit both sides. The firm gets a chance to demonstrate capability without asking for upfront trust. The buyer gets a chance to evaluate the team in real work rather than in sales conversations. The pilot’s success criteria are also the eval harness for the larger engagement, so the work isn’t thrown away.

For pilot scoping specifically, see our founder’s guide to GenAI POCs.

The broader direction of the consultancy market

A few patterns worth knowing about as a buyer.

Generalist “we do everything on every cloud” firms are losing ground in technical engagements. Buyers know what they want and want specialists. If a firm’s pitch is breadth, ask about depth. The differentiation that wins now is depth in a specific platform and a specific category, not coverage across platforms.

Boutique firms with experienced founders often outperform large generalist consultancies on technical work. The economics work in their favor: smaller team, senior engineers, less overhead, more direct accountability. The trade-off is bench depth; large firms can absorb a key person leaving mid-engagement, boutiques can’t.

The line between consultancy and ISV is blurring. Firms increasingly productize parts of their delivery (eval harness frameworks, deployment templates, monitoring scaffolds). This usually means faster delivery, but also a less custom outcome. Ask whether what you’re getting is bespoke or a productized template configured for your case.

How Accelyze helps

If you’re evaluating consultancies for a GenAI engagement on Google Cloud, we’d be glad to be one of the firms you compare against. We’d suggest comparing on the criteria above rather than on logos or partner tier. If you’re earlier and want to talk through your use case before you start the evaluation process, get in touch. If we’re not the right fit, we’ll usually be able to point you toward firms that are.