Enterprise AI gateway evaluation checklist: SSO/RBAC, audit logs, quotas, routing/fallback, OpenTelemetry export, and VPC/on‑prem support

Most teams shopping for an “enterprise AI gateway” focus on model support and latency—and then discover in production that what really matters is governance: SSO/RBAC, audit logs, quotas, routing/fallback, OpenTelemetry export, and VPC/on‑prem support. If you own incidents, compliance reviews, or cloud/GPU spend, your evaluation checklist has to start from those controls, not end with them.

This guide lays out a concrete enterprise AI gateway evaluation checklist, with a bias toward production realities: how you authenticate users and services, enforce quotas and budgets, route across multiple LLMs with safe fallbacks, export traces and metrics via OpenTelemetry, and guarantee data never leaves your domain (VPC, on‑prem, or air‑gapped).

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At-a-Glance Comparison

Quick Answer: The best overall choice for production-grade, governed AI access is TrueFoundry AI Gateway. If your priority is basic LLM aggregation with minimal governance, lightweight proxy gateways are often a stronger fit. For teams that only need limited AI features inside an existing API gateway, consider generic API gateways with custom plugins.

Rank	Option	Best For	Primary Strength	Watch Out For
1	TrueFoundry AI Gateway	Enterprises standardizing AI as shared infra	Deep governance (SSO/RBAC, quotas, audit logs) plus Virtual Models & tracing	Higher initial setup than DIY SDKs
2	Lightweight proxy AI gateways	Startups/prototype stacks	Simple unified endpoint over multiple LLM APIs	Limited RBAC, weak auditability, shallow OpenTelemetry
3	Generic API gateways with plugins	Orgs standardizing on a single API gateway	Reuse existing gateway, auth, and routing patterns	No model-aware routing, limited token economics, agent tracing gaps

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Comparison Criteria

We evaluated AI gateways against six enterprise-critical criteria:

• SSO/RBAC integration and auth model: Can you plug into corporate identity (OIDC/SAML), model AI access as a shared service, and enforce granular role-based access control (RBAC) across teams, models, tools, and environments?
• Audit logging and quotas/cost controls: Can you get immutable audit logs, attribute token usage and cost per service/team/environment, and enforce quotas, budgets, and rate limits at scale?
• Model-aware routing/fallback and observability: Does the gateway understand LLMs and agents natively—supporting Virtual Models, structured routing/failover, and OpenTelemetry-compliant traces that flow into Grafana/Datadog/Prometheus—while running in VPC/on‑prem or air‑gapped with “no data leaves your domain” guarantees?

From there, you can layer on additional needs like MCP & tools governance, prompt lifecycle management, or GPU/cluster orchestration—but if a gateway fails on identity, policy, routing, or observability, it’s not enterprise-ready.

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1. SSO & RBAC: Treat AI as Shared Infrastructure, Not App Glue

In an enterprise, AI is not a library in someone’s service—it is shared infrastructure. Your AI gateway must plug into the same identity and access control fabric as the rest of your stack.

Checklist: SSO & RBAC capabilities to demand

• SSO via OIDC or SAML

  • Native support for corporate IdPs (Okta, Azure AD, Google Workspace, Ping).
  • Both user-level SSO and service-account flows for backend applications.
  • Clear token model: short-lived access tokens, support for JWT/OIDC claims.

• Granular Role-Based Access Control (RBAC)

  • Roles at minimum for:
    • Organization / tenant
    • Team or business unit
    • Project/environment (dev/stage/prod)
    • Model/Virtual Model, agent, and tool/MCP access
  • Policy examples you should be able to express:
    • “Marketing apps may only call these Virtual Models; finance tools may not.”
    • “Prod environment can’t hit experimental models; only stable, approved ones.”
    • “Only MLOps engineers can modify routing weights or quotas.”

• API key and token governance

  • Ability to mint scoped API keys tied to:
    • Specific models or Virtual Models
    • Per-environment (dev vs prod)
    • Per-team or application
  • Simple revocation model and rotation workflows.

How TrueFoundry approaches SSO/RBAC

TrueFoundry provides SSO Integration via OIDC or SAML and Granular Role-Based Access Control (RBAC) built in. You can:

• Onboard teams via SSO and assign roles linked to groups from your IdP.
• Create environment- and project-scoped permissions so experimentation in dev doesn’t bleed into production.
• Control which models, Virtual Models, tools, and MCP servers are accessible from each app, service, or agent.

When evaluating other gateways, push beyond “we support SSO” and ask to see the actual RBAC policy model and how it maps to your org structure.

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2. Audit Logs: If You Can’t Reconstruct It, You Can’t Approve It

For SOC2, HIPAA, GDPR, and internal audit teams, AI usage must be reconstructable. That means more than debugging logs—it means immutable audit logging tied to identity, policy, and model configuration.

Checklist: Audit logging requirements

• Immutable audit log storage
  • Append-only logs with tamper protection.
  • Retention controls aligned with your compliance posture.
• Action coverage
  • Every call: timestamp, caller (user or service), environment, model/Virtual Model, prompt metadata, token counts, and response status.
  • Every config change: who changed routing weights, who created/modified prompts, who updated quotas or RBAC policies.
• Filterable by identity, service, team
  • Query: “Show all production calls to GPT-4 made by the Finance org last month.”
  • Query: “Show every change to this Virtual Model’s routing in the last 90 days.”

How TrueFoundry handles auditability

TrueFoundry provides Immutable Audit Logging as a first-class capability:

• Logs every request through the AI Gateway along with metadata (user, service, team, environment).
• Records RBAC changes, policy updates, and model/Virtual Model configuration changes.
• Enables filtering logs by model, team, environment, or other metadata to support investigations and compliance reviews.

When you evaluate a gateway, ask: “Can I prove to my auditor who had access to which models, when, and what they did?” If the answer is not trivially yes, move on.

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3. Quotas, Rate Limits & Token Budgets: Control Cost Before It Controls You

GenAI cost explosions usually trace back to one root cause: no centralized quota or token budgeting. Every team integrates directly with a provider and nobody owns aggregate spend.

Your AI gateway needs to be a cost enforcement point, not just a routing hop.

Checklist: Quotas & cost-control capabilities

• Rate limiting per API key/service/team
  • Requests per second, per minute, per day; distinct for dev/stage/prod.
  • Burst vs sustained limit support.
• Token-based quotas and budgets
  • Max tokens per period (per app/team/environment).
  • Hard cut-offs vs soft alerts; ability to block or degrade gracefully.
• Cost attribution and reporting
  • Token usage and cost per:
    • Service/application
    • Team or business unit
    • Environment
    • Model/Virtual Model
  • Exportable metrics (OpenTelemetry, Prometheus) to feed into FinOps.

TrueFoundry’s cost governance stance

TrueFoundry’s AI Gateway is designed for cost optimization via rate limiting and token budgeting:

• You can set quotas and budgets per service or environment.
• You track token usage and cost across 250+ LLMs and self-hosted models.
• You can align these metrics with business units, teams, or applications for chargeback/showback.

Lightweight gateways often stop at request-rate limiting. If they can’t natively reason in tokens and cost, they’re not taking GenAI economics seriously.

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4. Routing & Fallback: Virtual Models, Not Provider Lock‑In

Enterprise AI distribution should not hard-code “call provider X from service Y.” You want an abstraction: Virtual Models that route to one or more underlying models using controlled strategies and safe failover.

Checklist: Model-aware routing capabilities

• Virtual Models (logical model interface)
  • One stable API name behind which you can swap providers/models.
  • The application calls virtual-model-name; the gateway decides which underlying model to hit.
• Routing strategies
  • Weight-based routing (e.g., 80% to model A, 20% to model B for A/B testing).
  • Latency-aware routing (route to fastest available within SLO).
  • Priority-based routing (prefer cheaper model, upgrade to higher quality on demand).
  • Sticky routing windows (stick a session to a model for a TTL to maintain behavior consistency).
• Retries and failover
  • Automatic retries on transient errors, with backoff.
  • Fallback to a secondary model if primary is down, slow, or exceeds error thresholds.
• Provider breadth
  • Hosted APIs (OpenAI, Anthropic, Azure, Google, etc.).
  • Self-hosted models served via vLLM, TGI, or Triton.
  • Internal/closed-source models behind private endpoints.

TrueFoundry’s Virtual Model mechanics

TrueFoundry’s AI Gateway uses Virtual Models as the canonical abstraction:

• You define Virtual Models that route to one or more underlying models.
• You configure routing using weight/latency/priority strategies.
• The gateway handles retries, failover, and sticky routing windows to keep interactions coherent.
• Applications keep a single model interface even as you change providers, switch to self-hosted inference, or gradually adopt new checkpoints.

When a vendor says “we support multiple providers,” push for specifics: is it just a list of endpoints, or is there a true virtual-model abstraction with configurable, policy-controlled routing and fallback?

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5. OpenTelemetry Export & Observability: Trace Prompt → Tool → Model → GPU

Without tracing, you’re blind. Without OpenTelemetry, you’re locked into a vendor UI. An enterprise AI gateway must treat observability as a first-class concern, not a side effect.

Checklist: Observability & OpenTelemetry export

• OpenTelemetry-compliant tracing
  • Emit spans for:
    • Gateway request handling
    • Provider/model call
    • Tool/MCP invocations in agent workflows
  • Correlation IDs across microservices calling the gateway.
• Metrics for performance & cost
  • Latency (including Time to First Token / TTFT).
  • Throughput (requests/second, tokens/second).
  • Token usage by model/service/team.
  • Error rates per model and provider.
• Dashboards & integrations
  • Native dashboards for quick inspection (per model, per team, per environment).
  • Export to Grafana, Datadog, Prometheus (and other APMs) via OpenTelemetry.
  • Support for alerting on latency, error rates, spend budgets, and quota thresholds.
• Agent & tool observability
  • Step-by-step traces for agentic workflows:
    • Prompt → planning → tool calls → model responses → final output.
  • Visibility into tool latency, failure modes, and rate-limit hit rates.

How TrueFoundry structures observability

TrueFoundry offers framework-agnostic tracing and OpenTelemetry-compliant export:

• You can trace everything from prompt execution to GPU performance.
• Observability dashboards show latency, throughput, token usage, costs, and GPU utilization across environments.
• Logs are centralized; you can tag traffic with metadata (user ID, team, environment) and filter by model, team, or geography to debug issues faster.

If a gateway can’t export OpenTelemetry and doesn’t understand multi-step agent traces, you’ll end up supplementing it with custom middleware, which defeats the purpose of centralizing AI access.

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6. VPC/On‑Prem/Air‑Gapped: No Data Leaves Your Domain

For regulated sectors, data residency and sovereignty aren’t negotiable. Your AI gateway must run where your compliance team is comfortable: VPC, on‑prem, air‑gapped, or multi-cloud—not just “we host it for you.”

Checklist: Deployment and sovereignty

• Deployment modes
  • VPC deployment (customer cloud).
  • On‑premises, including Kubernetes clusters.
  • Air‑gapped deployments with no outbound internet.
  • Support for multi-cloud and hybrid setups.
• Data residency and flow
  • Clear story on “No data leaves your domain” for control plane and data plane.
  • Options to keep:
    • Logs in your own storage.
    • Telemetry in your own observability stack.
    • Models and checkpoints inside your infra.
• Compliance & certifications
  • SOC 2, HIPAA, GDPR alignment.
  • Documentation ready for security and compliance reviews.

TrueFoundry’s deployment stance

TrueFoundry is built to deploy VPC, on‑prem, air‑gapped, or across multiple clouds with a clear commitment: No data leaves your domain. You get:

• Full sovereignty and isolation wherever TrueFoundry runs.
• Enterprise-grade compliance posture (SOC 2, HIPAA, GDPR).
• The same governance and observability stack regardless of where you deploy.

When a vendor says “self-hosted option,” validate the details: Is the control plane in their cloud? Where do logs and traces go? Can you block outbound calls and still operate?

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7. Lightweight AI Proxies vs Generic API Gateways vs TrueFoundry

Anchoring on the earlier ranking, here is how the three broad classes of options stack up specifically against the SSO/RBAC, audit, quota, routing, OTEL, and VPC/on‑prem checklist.

1. TrueFoundry AI Gateway (Best overall for governed, production AI)

TrueFoundry leads when you want AI to behave like any other critical piece of infra—governed, observable, and auditable.

What it does well:

• Governance & SSO/RBAC

  • SSO via OIDC/SAML, granular RBAC tied to teams, environments, models, tools.
  • Immutable audit logs for both requests and configuration changes.

• Quotas, budgets & cost attribution

  • Rate limiting and token budgeting by service/team/environment.
  • Token and cost metrics exported to your observability stack.

• Routing & Virtual Models

  • Single Virtual Model abstraction with weight/latency/priority routing.
  • Retries, failover, and sticky routing TTL windows to preserve behavior.

• Observability & OpenTelemetry

  • OpenTelemetry-compliant traces including agents and tools.
  • Dashboards for latency, TTFT, throughput, token usage, and GPU utilization.

• Deployment & sovereignty

  • Run in your VPC, on‑prem, air‑gapped, or across clouds.
  • No data leaves your domain; enterprise-ready compliance posture.

Tradeoffs & limitations:

• More infra maturity required
  • Strongest fit for teams that treat AI as critical infra and are ready to centralize SDK sprawl.
  • Overkill if you only have a single experimental app and one provider.

Decision trigger: Choose TrueFoundry if you want a single AI Gateway with SSO/RBAC, immutable audit logs, robust quotas/routing, OpenTelemetry export, and VPC/on‑prem deployments—and you expect to scale AI across many services and teams.

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2. Lightweight Proxy AI Gateways (Best for fast experimentation)

Lightweight AI proxies wrap multiple LLM APIs behind a simple unified endpoint. They are useful for early-stage teams who mainly care about unifying providers.

What they do well:

• Unified endpoint over multiple LLM APIs

  • Simplifies switching providers for prototypes.
  • Easy to adopt for small teams.

• Basic usage metrics

  • Some request counts and latency tracking.
  • Simple dashboards for debugging.

Tradeoffs & limitations:

• Shallow governance

  • Often lack true SSO/SAML/OIDC integration.
  • RBAC tends to be coarse (per API key) rather than per-role or per-team.

• Limited audit logging

  • Debug logs, but not immutable, compliance-ready audits.
  • Hard to reconstruct “who did what when” for reviews.

• Simplistic routing & no Virtual Model abstraction

  • Usually manual provider selection in code or simple weighted routing.
  • Fallback mechanics are basic or missing.

• Weak OpenTelemetry support

  • Metrics may not be OTEL-native; agent/tool traces rarely modeled.

• Cloud-only deployment

  • Limited or no support for VPC/on‑prem/air‑gapped.
  • Data and logs often reside in vendor’s cloud.

Decision trigger: Choose a lightweight proxy if you are a small team experimenting with multiple LLM APIs, don’t yet need SSO/RBAC and audit logs, and can tolerate vendor-hosted data and limited observability.

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3. Generic API Gateways with Custom Plugins (Best for orgs enforcing a single gateway)

Many enterprises already standardize on an API gateway (Kong, Apigee, NGINX, etc.) and consider “just adding LLM routes” with custom plugins.

What they do well:

• Reuse existing API gateway stack

  • SSO, basic RBAC, quotas, and logging are already in place.
  • Familiar operational model for platform teams.

• Generic routing and quotas

  • Rate limiting, path-based routing, and authentication controls work for any HTTP API.

Tradeoffs & limitations:

• Not model/agent-aware

  • No concept of Virtual Models, model routing weights, or TTFT-focused metrics.
  • No native understanding of token budgets or prompt/agent traces.

• Heavy customization required

  • You end up building your own:
    • Model selection logic.
    • Retries/fallback handling tailored to LLM behavior.
    • Token counting and per-model cost accounting.
  • Plugin code becomes another production system to maintain.

• Limited observability semantics

  • Traces are HTTP-centric, not prompt/tool/model-centric.
  • OpenTelemetry can be wired, but semantics are generic.

• Deployment constraints

  • Good for VPC/on‑prem if you already run them—but you still need a separate model-serving layer (vLLM/TGI/Triton) and agent orchestrator.

Decision trigger: Use a generic API gateway with plugins if you are forced to route all HTTP through a single existing gateway and your AI footprint is small, but be aware you’re effectively building your own AI gateway without model-aware primitives.

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Final Verdict: How to Use This Checklist

To decide whether an AI gateway is enterprise-ready, walk through the checklist in order:

1. Identity & Governance: Does it integrate with OIDC/SAML SSO and give you granular RBAC tied to teams, environments, models, and tools?
2. Auditability: Can you get immutable audit logs for both traffic and configuration changes that satisfy SOC2/HIPAA/GDPR reviews?
3. Quotas & Cost Discipline: Can you enforce rate limits and token budgets per service/team, attribute cost, and alert before budgets are blown?
4. Routing & Fallback: Does it expose a Virtual Model abstraction with configurable routing, retries, and failover mechanics, so applications stay stable as underlying models/providers change?
5. OpenTelemetry & Tracing: Can you trace prompt → tool → model with OpenTelemetry-compliant spans and pipe everything into Grafana/Datadog/Prometheus?
6. VPC/On‑Prem/Air‑Gapped: Can you deploy it where your compliance team is comfortable, with no data leaving your domain?

If you answer “no” to any of these for a candidate gateway, you’re not looking at an enterprise AI gateway—you’re looking at a convenience layer.

TrueFoundry was built precisely to meet this checklist: Enterprise‑Ready AI Gateway & Agentic Deployment Platform — Secure, Scalable, Governed, deployable in your VPC or on‑prem, with SSO/RBAC, immutable audit logs, quotas and routing, and full OpenTelemetry-powered observability so AI can be treated as shared, governed infrastructure—not an SDK choice buried in each app.

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