What the guide covers:
26
Evaluation criteria spanning gateway, deployment, observability, governance, and agents
4
Capability areas, evaluated as one connected platform rather than separate tools
18
Must-have capabilities that separate production platforms from demo-only features
3
Teams aligned on one operating model: platform engineering, security, and AI
Inside the guide
Download the guide to get:
A working framework your platform, security, and AI teams can use to compare vendors side by side and agree on one platform.
Download the Guide✓
The full 26-point evaluation framework
Every criterion grouped by capability area, with the question to ask and a must-have, should-have, or nice-to-have priority.
✓
An ownership alignment step
Who owns platform operations, security review, model access, data controls, cost visibility, and incident response.
✓
A real-work pilot plan
How to pilot with multiple teams, multiple providers, one self-hosted model, and at least one tool workflow.
✓
Beyond-the-gateway criteria
What to require for Kubernetes-native model deployment, GPU autoscaling, fine-tuning, and progressive rollouts.
Why it matters
Choosing an Enterprise AI Platform Means Choosing an Operating Model
Most AI platform evaluations start with a demo: one team, one model provider, one happy-path prompt. Nearly every vendor looks good there. The differences show up later, when ten teams share the platform, a provider has an outage, a self-hosted model needs GPUs, finance asks who spent what, and security asks who called which tool.
Many enterprises end up with a fragmented stack: a gateway from one vendor, model serving from another, observability from a third, and governance stitched together in between. Each piece works on its own, but no one team can operate the whole thing, and every security review has to cover four products instead of one.
Three teams, one control point, every model and tool behind it. The guide helps you test whether a platform can run this way in production.
The AI gateway is the control point that ties this together: governed access, routing, failover, and cost control for every model, tool, and agent request. But a gateway alone isn't a platform. Teams that self-host open-source or fine-tuned models also need deployment, GPU autoscaling, and rollouts that plug into the same gateway, identity, and observability.
What a production-ready enterprise AI platform looks like
Developers keep their OpenAI- or Anthropic-compatible code and get one API for hosted, self-hosted, and fine-tuned models. The gateway adds only a few milliseconds of overhead, fails over automatically, and enforces rate limits and quotas per user, team, and app. Models deploy natively on Kubernetes with GPU autoscaling, fractional GPUs, scale-to-zero, and canary rollouts.
Every request produces OTEL logs, metrics, and traces, and spend is attributed by team, app, model, and environment with budget alerts. Agents, skills, and MCP servers are published in a shared registry, and tool calls are authenticated, authorized, and audited at the gateway. The whole platform runs where your data has to stay, with tenant isolation, enterprise SLAs, and compliance attestations.
Pilot real work, not a demo
The fastest way to separate production capabilities from demo-only features is a pilot that looks like production: multiple teams, multiple providers, at least one self-hosted model, and at least one tool workflow. The Enterprise AI Platform Buyer's Guide gives you the 26 criteria to score it against.
Preview
The four capability areas in the enterprise AI platform buyer's guide
A sample of the criteria in each area. The full guide includes all 26, with priorities and evaluation questions.
Area 1 · 8 criteria
Gateway and developer experience
One consistent interface for hosted, self-hosted, and provider-native requests
Existing OpenAI or Anthropic client code works behind the gateway
Provider keys rotate without app redeploys
+5 more criteria
Area 2 · 7 criteria
Model deployment and operations
Deploys as a native Kubernetes workload with Helm charts, CRDs, and operators
Open-source LLMs run on GPUs via vLLM, TGI, or Ollama
Budgets block requests automatically when exhausted
+4 more criteria
Area 3 · 6 criteria
Observability, cost, and governance
OTEL logs, metrics, and traces for every model, tool, and agent request
Spend attributed by model, provider, user, team, app, and environment
Teams and business units are isolated at the gateway
+3 more criteria
Area 4 · 5 criteria
Agents, MCP, and enterprise fit
Agents, skills, and MCP servers are published and governed in a shared registry
Tool calls are authenticated, authorized, rate-limited, and audited at the gateway
Runs in cloud, VPC, hybrid, on-premises, or air-gapped environments
+2 more criteria
4.7
★★★★★
Gartner Peer Insights
Rated 4.7 on Gartner Peer Insights
Enterprises run TrueFoundry's AI Gateway (LLM, MCP, and Agent Gateway), prompt management, tracing, and governance in production, plus Kubernetes-native model deployment beyond the gateway, all on one platform.
96%
of reviewers likely to recommend
4.9
ease of deployment and admin
3–4 ms
added gateway latency
