Best enterprise AI gateways

Summary

• An enterprise AI gateway is the control plane between your applications and the models, agents, and tools they call: it routes requests, manages credentials, enforces rate limits and budgets, applies safety policies, and logs every call for audit.
• The strongest choice for most enterprises extends the governance you already run on your data instead of standing up a separate silo, because AI risk and data risk are the same risk.
• Evaluate a gateway on unified routing across providers, centralized credentials, per-team budgets and rate limits, usage and cost attribution, guardrails, audit, and governance of agents and tools.
• Databricks Unity Gateway is generally available and builds the gateway on Unity Catalog, so models, MCP servers, functions, and connections are governed with the same permissions used for tables.

Best enterprise AI gateways
An enterprise AI gateway is the control plane between your applications and the models, agents, and tools they call. The best one governs every request from a single place: routing, credentials, rate limits, budgets, safety policies, and audit logging. The strongest choice for most enterprises extends the governance you already run on your data rather than standing up a separate silo, because AI risk and data risk are the same risk. Databricks Unity Gateway takes that approach, building the gateway on Unity Catalog so models, agents, and tools are governed with the same permissions you use for tables.

What is an AI gateway?

An AI gateway is middleware between your applications and one or more model providers. It doesn't host the models; it adds a policy and observability layer in front of them. Each request flows through the gateway, which authenticates the caller, checks permissions, applies routing and rate limits, optionally filters the prompt and response, forwards the call to the right model, and records what happened. Platform, security, and finance teams set controls once, centrally, instead of each application team reinventing them in code.

Key capabilities to evaluate

Capability	Why it matters
Unified routing	One endpoint should reach both self-hosted and external models, with traffic splitting, fallback, and load balancing, so you can change models without changing apps.
Centralized credentials	Provider API keys should be registered once, not scattered across notebooks, apps, and developers.
Rate limits and budgets	Per-user, per-team, and per-endpoint caps on usage and spend keep one workload from starving others or blowing the budget.
Usage and cost attribution	Token-level usage and spend by user, team, and app make chargeback and forecasting possible.
Safety guardrails	Request and response filtering, PII detection, and prompt-injection mitigation reduce the risk of data leakage and abuse.
Audit and observability	Full request and response logging, tracing, and a record of who called which model are required for compliance.
Identity and access integration	The gateway should reuse your existing identity and permissions, not create a parallel access system.
Agent and tool governance	As agents call tools and other models, those endpoints (including MCP servers) need governing too.

How Databricks approaches the enterprise AI gateway

Unity Gateway is Databricks' control plane for enterprise AI, and it is generally available. It routes every model and MCP request from one place and enforces governance built on Unity Catalog, so a model, MCP server, function, or connection is a securable object governed with the same privileges and attribute-based (ABAC) grants you apply to tables and volumes. You write one rule to cover a class of assets by tag, and new assets that match are governed automatically.
On routing and credentials, Unity Gateway reaches Databricks-hosted models and external model providers through one governed path, with traffic splitting, fallback, and load balancing across endpoints. Provider credentials are registered once in Unity Catalog, so developers use the models they are authorized for without ever handling raw API keys, and you can add or swap providers without rewiring applications. For cost, budgets monitor and control monthly spend with per-user thresholds and hard caps (in Beta), rate limits cap usage per user, team, or endpoint, and token-level usage and estimated spend flow to system tables and MLflow, on by default for external models, giving finance real attribution across hosted and external usage.
For observability, inference tables log full request and response payloads to governed Delta tables for monitoring, debugging, and audit, and the audit trail captures agent identity and usage. You can analyze agent logs in natural language with Genie. Behavior governance comes from service policies, currently in Beta, which allow, deny, or require approval for individual requests and responses based on who is calling and what the content contains, with content filtering, PII detection, and prompt-injection mitigation. See the ai gateway documentation and the blog on the governance layer for agentic AI.

Use cases

A shared AI platform team gives product teams self-service access to models while capping each team's budget and applying guardrails centrally. A regulated business enforces approved-provider lists, PII redaction, and a full audit trail to satisfy examiners. A finance organization gets per-team spend attribution and hard caps across hosted and external models. An agentic system routes every agent, tool, and MCP call through one control plane, so autonomous behavior stays observable and bounded.

FAQs

How is an AI gateway different from a traditional API gateway?

A traditional API gateway routes generic HTTP traffic. An AI gateway understands models, tokens, prompts, and responses, so it can meter token usage, attribute spend, redact PII, and mitigate prompt injection.

Do I have to change application code to adopt one?

Little to none. You point your model client at the gateway instead of the provider, and routing, credentials, limits, and policies are enforced there. Applications stay largely unchanged.

Can a gateway govern both external providers and open source models?

Yes. Unity Gateway routes to Databricks-hosted models and external providers through one governed path, so the same policies, credentials, and tracking apply no matter where a model runs.

Is Unity Gateway generally available?

Unity Gateway is generally available. Some capabilities, including service policies and agent services, remain in Beta, so confirm the status of a specific feature before you depend on it.

How does an AI gateway stop credential sprawl?

Provider API keys are registered once in Unity Catalog. Developers call the models they are entitled to without seeing the underlying keys, which removes them from notebooks, source code, and individual apps.