Why This Buyer’s Guide Matters (And How We Messed It Up First)

After spending six painful months evaluating “enterprise AI platforms” that all sounded identical on paper, we still picked the wrong one. The sales demos were magical, the models were strong, and yet we hit a wall on security reviews, governance, and integration. We ended up running parallel stacks-one for experiments, one for production-doubling our cost and complexity.

The breakthrough came when we stopped treating this as a generic software purchase and wrote a 12‑criteria checklist tuned to how we actually build and operate AI systems. Once we ran vendors like AWS, Google, Microsoft, IBM, H2O.ai, Vellum, Cognigy, and a few niche players through the same lens, the picture got much clearer very quickly.

This guide is that checklist, cleaned up so your team doesn’t repeat our mistakes. It’s written for a combined audience: senior engineering/ML platform leads and procurement/legal. If we could get from chaos to a defensible platform choice, you can too.

How to Use This 12‑Criteria Checklist

Before diving into each criterion, decide how you’ll evaluate vendors. What finally worked for us was a simple three‑phase process:

  • Phase 1 – Paper filter (1-2 weeks): Use this checklist to kill obvious bad fits from RFP responses and security questionnaires.
  • Phase 2 – Hands‑on bake‑off (2–3 weeks): Run 2–3 serious contenders through the same technical scenarios and governance checks.
  • Phase 3 – Commercial & risk deep dive (1–2 weeks): Negotiate pricing, SLAs, data terms, and exit strategy with your top choice (plus a fallback).

For each criterion below, I’ll call out:

  • What to look for in 2026‑ready platforms
  • Questions to ask vendors
  • Mistakes we made so you can skip them

Criterion 1 – Model & Provider Flexibility

Our first mistake was letting a single flagship model drive the whole platform decision. Six months later, new models appeared and we were locked in.

In 2026, assume your model mix will change every quarter. Your platform should:

  • Support multiple foundation models (OpenAI‑style, open‑source, and domain‑specific)
  • Offer easy swapping and routing between providers without code rewrites
  • Let you bring your own models (fine‑tuned or custom) with proper deployment tooling

Ask vendors: “Show me how we’d migrate a workload from Model A to Model B in your platform, including evals and rollback.” If this takes more than a few clicks and a config change, be wary.

Criterion 2 – Data Security & Isolation

This is where our security team almost killed our first deployment. The vendor’s story depended on “trust us” more than controls we could actually verify.

  • Dedicated VPC or private link options; no training on your data by default
  • Granular RBAC down to workspace, dataset, and model level
  • Data residency controls (region pinning) and clear sub‑processor list
  • Support for your identity stack (SAML/OIDC, SCIM, conditional access)

Ask vendors: “Show our security team the exact path a prompt and response take through your system, including logs and backups.” A whiteboard session here will reveal gaps fast.

Criterion 3 – Governance, Compliance & Auditability

The next step is where most teams fail-governance gets bolted on after pilots, and suddenly nothing passes risk review.

  • Approval workflows for moving from dev → staging → prod
  • Full lineage: which model, prompt, and dataset produced each decision
  • Policy‑based controls (e.g., no PII in prompts, restricted tools for certain groups)
  • Exportable audit logs for regulators and internal risk teams

Ask vendors: “How would we prove to an auditor how this production decision was made three months ago?” If they can’t show you in the UI, you’ll be building your own wrappers.

Criterion 4 – Full ML/LLM Lifecycle & CI/CD

Don’t make my mistake of treating the platform as just an “inference API.” The hidden cost is everything around it: experiments, versioning, rollouts.

  • Experiment tracking for prompts, system messages, and model variants
  • Versioned deployments with canary/blue‑green rollout support
  • API or CLI hooks that plug into your existing CI/CD (GitHub Actions, GitLab, Azure DevOps)
  • Config‑as‑code (YAML/JSON) for reproducible environments

Ask vendors: “Show us your recommended Git branching and deployment workflow for an LLM application.” If the answer lives only in PowerPoint, that’s a red flag.

Criterion 5 – Observability, Evals & Guardrails

What finally worked for us was treating LLMs like any other production system: logs, metrics, traces, and tests. The platforms that couldn’t support that got cut quickly.

  • Request tracing with prompt, context, tools invoked, and latency
  • Built‑in evals (accuracy, hallucination, toxicity, PII leakage) with custom metrics
  • Guardrails: content filters, safety policies, and allow/deny lists for tools
  • Dashboards for cost, quality, and safety over time

Ask vendors: “How do we A/B test prompts or models and roll back if quality drops?” If the answer is “export logs and write your own scripts,” move on.

Criterion 6 – Orchestration, Agents & Tools

By 2026, most serious workloads look like agents calling tools, not single prompts. Our early platform choice struggled here, and we ended up building a parallel orchestration layer with n8n and custom code.

  • First‑class support for tools/function calling and multi‑step workflows
  • State management for long‑running conversations and processes
  • Sandboxing and permissioning of tools (who can run what, where)
  • Debugging view for agent traces (which step failed, with what inputs)

Ask vendors: “Show us an end‑to‑end agent executing three tools with error handling, then expose it as an API.” You want this to be a configuration exercise, not a research project.

Criterion 7 – Integration & Connectors

Your platform is only as useful as the systems it can safely touch. We underestimated how much work it would take to wire into CRM, ticketing, and data warehouses.

  • Native connectors for your data stack (Snowflake, BigQuery, Databricks, S3, SharePoint)
  • Webhooks and event integration (Kafka, Pub/Sub, EventBridge)
  • SDKs in your main languages (Python, TypeScript/JavaScript, Java, .NET)
  • Clear story for integrating with existing API gateways and service meshes

Ask vendors: “Which integrations are product‑maintained vs. partner or community‑maintained?” Relying on one‑off connectors is how you end up with fragile glue code.

Criterion 8 – Performance & Scalability

Demo traffic is easy; month‑end batch jobs and peak usage are not. We learned this the hard way when latency spiked during a company‑wide rollout.

  • Documented SLAs for latency and availability by region
  • Support for concurrency controls, batching, and streaming
  • Clear capacity planning guidance (tokens/sec, requests/sec per workload)
  • Performance metrics exportable to your observability stack (Datadog, Prometheus, etc.)

Ask vendors: “Give us your performance numbers on a workload shaped like ours, and let us verify in a load test.” Don’t accept benchmarks you can’t reproduce.

Criterion 9 – Cost Controls & FinOps

I’ve seen more AI projects get paused over surprise bills than over model quality. This is where a good platform quietly pays for itself.

  • Per‑team and per‑project budgets, quotas, and rate limits
  • Cost attribution at request, app, and business‑unit level
  • Support for cheaper models/tiers and routing rules to use them
  • Alerts when spend or usage patterns spike unexpectedly

Ask vendors: “Show us the cost view a finance partner would use, and how we’d export it to our existing FinOps reports.” If costs aren’t transparent, they will become political.

Criterion 10 – Enterprise UX & Collaboration

Our early pilots were “platforms for ML engineers only.” Adoption stalled because product teams, analysts, and operations couldn’t participate without filing tickets.

  • Role‑appropriate interfaces: notebooks for engineers, low‑code builders for ops, chat interfaces for business teams
  • Shared workspaces with permissions and project templates
  • Reusable components (prompts, tools, eval suites) discoverable in a catalog
  • Onboarding flows and in‑product guidance, not just PDFs and training decks

Ask vendors: “Show us how a non‑engineer would build and safely deploy a small AI assistant with sign‑off from engineering.” That’s your adoption litmus test.

Criterion 11 – Deployment Models & Regulatory Fit

We lost months when legal realized our initial vendor couldn’t support some regulated workloads. Fixing this after the fact is painful.

  • Options for SaaS, private cloud/VPC, and on‑prem/air‑gapped if needed
  • Certifications relevant to you (SOC2, ISO, HIPAA, financial or public‑sector standards)
  • Region‑specific support for emerging AI regulations and documentation needs
  • Clear data retention and deletion controls you can trigger yourself

Ask vendors: “Which of our high‑risk use cases can’t you support today, and what’s the roadmap?” You want honest constraints upfront.

Criterion 12 – Vendor Health & Ecosystem

Finally, don’t ignore the boring stuff. We once fell for a beautiful product that had three solutions engineers covering all of North America. Guess how support went.

  • Clear 12–24 month roadmap that aligns with your strategy (agents, governance, multi‑model)
  • Partner ecosystem (SI partners, integration partners, training providers)
  • Real customer references in your industry and scale band
  • Support model: SLAs, named TAMs, and escalation paths

Ask vendors: “If our main champion leaves your company, what does continuity look like?” You’re buying a relationship, not just a product.

Common RFP Traps (And How to Avoid Them)

I wasted hours writing RFP questions vendors could easily spin. Here’s what I’d do instead:

  • Avoid yes/no questions. Ask for concrete demos and architectures (“Show us…” rather than “Do you support…”).
  • Force trade‑offs. Ask where the platform won’t fit, and what they recommend you run elsewhere.
  • Include real workloads. A short spec of your top 3 use cases beats 50 generic questions.
  • Score by risk, not features. Weight security, governance, and lifecycle higher than shiny UX.

Putting It All Together: A Practical Evaluation Plan

Once you have your shortlist, here’s the flow that finally worked for us:

  • Week 1: Run this 12‑criteria checklist on paper; cut to 3 vendors.
  • Weeks 2–3: Hands‑on bake‑off with the same 2–3 use cases, same metrics, and shared eval datasets.
  • Week 4: Security, legal, and procurement deep dive with your top 1–2, focused on data, governance, and exit strategy.
  • Week 5: Executive decision with a simple scorecard: capability fit, risk profile, total 3‑year cost, and ecosystem strength.

If you walk away with one thing, let it be this: don’t optimize for the best demo. Optimize for the platform that will still be safe, adaptable, and operable when your model mix, regulations, and business priorities all shift—because they will. This 12‑point checklist is how we finally got there, and it should give your team a defensible path to do the same.