Can an AI gateway enforce the same policies across OpenAI, Anthropic, and Google?

Summary

• Yes. A single AI gateway sits in front of every model provider, so the same access rules, rate limits, spend caps, and safety guardrails apply whether a request goes to OpenAI, Anthropic, Google, or a self-hosted model.
• On Databricks that gateway is Unity Gateway (generally available), built on Unity Catalog: each external model is registered once as a governed securable and shared with GRANT and REVOKE like any table.
• Service policies and LLM guardrails (in Beta) evaluate requests and responses with the same policy logic on every endpoint, so a prompt that is blocked or redacted for one provider is treated identically for another.
• Traffic governance is uniform too: requests-per-minute and token rate limits, budget alerts, and hard spend caps are enforced per user, team, or app across all providers from one place.
• Usage tracking is on by default, recording tokens, latency, and cost per provider and consumer in system tables, with optional full-payload logging to governed tables.

Can an AI gateway enforce the same policies across OpenAI, Anthropic, and Google?

Yes. The job of an AI gateway is to sit between your applications and every model provider so governance is applied once, centrally, instead of being rebuilt for each vendor's API. When models from OpenAI, Anthropic, Google, and self-hosted endpoints all sit behind one gateway, a single set of access controls, rate limits, spend caps, and content guardrails covers them uniformly. Without that layer, each team wires up its own keys and its own rules, and policy drifts from provider to provider until no one can say what is allowed where.

What is an AI gateway?

An AI gateway is a control plane that brokers every call between applications and large language models. It authenticates the caller, authorizes the request against a permission model, applies safety and cost policies, routes to the chosen model with optional fallbacks, and records what happened. Because every request passes through the same chokepoint, the gateway is the one place to standardize policy across provider APIs that otherwise look nothing alike.

Key capabilities to evaluate

Capability	Why it matters
Provider-agnostic access control	One permission model should decide who can call which model, regardless of who hosts it, so adding a provider does not mean standing up a new access system.
Consistent safety guardrails	PII handling, prompt-injection defense, and unsafe-content filtering should apply identically on every endpoint, not only the ones a given team remembered to configure.
Unified rate limits and spend caps	Requests-per-minute and token limits, budget alerts, and hard spend caps should be enforced per user, team, or app across all providers from one place.
Centralized usage and cost tracking	Token counts, latency, and cost should be attributed by provider, model, and consumer in a queryable record.
Routing, fallbacks, and traffic splitting	The gateway should let you switch providers or fail over without changing application code.
Auditability	Full request and response logging to governed storage gives compliance teams one trail across the whole model fleet.

How Databricks approaches this with Unity Gateway

On Databricks, the AI gateway is Unity Gateway, which is generally available and built on Unity Catalog. It treats governance as three connected layers, applied the same way whether a request goes to a Databricks-hosted model or an external one.

• Asset governance. You register each model once as a Unity Catalog securable object, including external models from providers such as OpenAI, Anthropic, and Google. Access is then granted and revoked with the same GRANT and REVOKE privileges, and attribute-based access policies, you already use for tables and volumes. See how AI governance works on Databricks.
• Traffic governance. A single control plane routes every request and enforces rate limits, budget alerts, and hard spend caps per user, team, or app, plus traffic splitting and fallbacks across providers.
• Behavior governance. Service policies and LLM guardrails, currently in Beta, evaluate each request and response and can allow, deny, or require approval, and can redact or block PII. They run through the same guardrail model on Databricks-hosted, external, and bring-your-own-key endpoints, so a prompt that is blocked or redacted for one provider is treated the same for another.

Usage tracking is on by default, recording requests, tokens, latency, and cost in system tables, and inference tables can log full request and response payloads to governed Delta tables. For the wider design, see AI governance with Unity Gateway and What's new in Unity Gateway.

Use cases

Teams use one gateway to move a workload from one provider's model to another's without touching application code, to keep PII redaction identical across a mixed fleet for compliance, and to give every group a single budget and audit trail even when each prefers a different frontier model.

FAQs

Do safety guardrails behave the same across providers?

That is the point of routing through one gateway. On Unity Gateway, guardrails and service policies (in Beta) evaluate inputs and outputs with the same policy logic on every endpoint, so a request gets a consistent decision whether it targets OpenAI, Anthropic, Google, or a self-hosted model.

Can I switch model providers without rewriting my application?

Yes. Because each model is registered as a Unity Catalog securable and reached through the gateway, you can change providers or add fallbacks by updating configuration. Applications keep calling the same governed endpoint.

How is spend controlled across multiple providers?

You set rate limits, budget alerts, and hard spend caps per user, team, or app, and usage tracking (on by default) attributes tokens and cost to each provider and consumer, so overages are visible early.

Does an AI gateway add latency?

A gateway adds a governance step in the request path, so it introduces some overhead in exchange for centralized control, routing, and logging. On Databricks that check runs inline as the request is routed.