How do I apply the same AI policies across OpenAI, Anthropic, and Google?

Summary

• Put every provider behind one gateway and govern them as catalog objects. Define access, rate limits, spend caps, and guardrails once, then enforce them identically whether a request goes to OpenAI, Anthropic, Google, or a self-hosted model.
• On Databricks, register each provider as a model provider service, a Unity Catalog securable that holds the connection and API key encrypted, so keys are never handed to applications.
• Grant access with the same privileges you use for tables (EXECUTE, MANAGE, READ_METADATA), and use attribute-based rules so one policy by tag covers current and future models from any provider.
• Guardrails and service policies evaluate each request and response with the same logic on every endpoint and return allow, ask, or deny, so a prompt blocked for one provider is blocked for another.
• Usage and spend tracking is on by default, attributing tokens, latency, and estimated cost to each provider and consumer, with optional full-payload logging to governed tables.

How do I apply the same AI policies across OpenAI, Anthropic, and Google?

Route all three providers through one gateway and manage them as governed objects, so a single set of rules covers every provider instead of being rebuilt per vendor API. On Databricks the gateway is Unity Gateway, generally available and built on Unity Catalog. You register each provider once, then attach the same access grants, attribute-based policies, rate limits, and guardrails to each. The alternative, giving every team its own keys and its own rules, guarantees that policy drifts from provider to provider until no one can say what is allowed where.

What "the same policies" means here

AI policies cover four things: who can call a model (access), how fast and how much they can spend (rate limits and budgets), what content is allowed in and out (guardrails, PII handling, safety), and what gets recorded (usage and audit). Applying them "the same" means defining each policy once and enforcing it identically on every provider endpoint, so behavior does not depend on which vendor a request happens to hit.

Key capabilities to evaluate

Capability	Why it matters
Provider-agnostic access	One permission model should decide who can call which model, no matter who hosts it.
One definition, many endpoints	A policy should be written once and enforced across every provider, not copied per API.
Uniform rate limits and spend caps	Requests-per-minute and token limits, budget alerts, and hard spend caps should apply per user, team, or app across all providers.
Consistent guardrails	PII handling, prompt-injection defense, and unsafe-content filtering should behave the same on every endpoint.
Centralized usage and audit	Tokens, latency, and cost should be attributed by provider and consumer in one queryable record.
Provider portability	You should be able to switch providers or add fallbacks without rewriting applications.

How to do it on Databricks with Unity Gateway

Unity Gateway treats each provider as a governed asset, so the same policy machinery that covers your tables covers your models. See how AI governance works on Databricks.

1. Register each provider as a model provider service. OpenAI, Anthropic, and Google each become a Unity Catalog securable whose connection and API key are held in a governed connection, encrypted and never exposed to clients.
2. Grant access once, the same way. Use the privileges you already use for data: EXECUTE to query a provider, MANAGE to change its configuration, and READ_METADATA to view it. The grant model is identical across providers.
3. Approve by attribute. Attribute-based access control writes one rule by tag, for example "analysts can call every model tagged lifecycle = production," and any new provider or model matching the tag is covered automatically. This is the same policy engine that drives row filters and column masks, so data and AI governance stay unified.
4. Set rate limits and spend caps per service. Attach rate limits, budget alerts, and hard spend caps per user, team, or app, enforced centrally on every provider.
5. Attach guardrails and service policies. Service policies evaluate each request and response and return allow, ask, or deny. They invoke built-in checks for PII, unsafe content, jailbreaks, and hallucination, or your own SQL and LLM-as-judge logic, using the same policy logic on every endpoint.
6. Observe uniformly. Usage tracking is on by default and attributes tokens, latency, and estimated spend to each provider and consumer, and inference tables can log full request and response payloads to governed Delta tables. For a tour of the capabilities, see what's new in Unity Gateway.

Use cases

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

FAQs

Do I need different governance for each provider's API?

No. Register each provider as a model provider service and reach it through Unity Gateway. Access, limits, and guardrails are defined once and enforced the same way regardless of which provider serves the request.

Can one policy cover models I add later?

Yes. Attribute-based policies grant by tag, so any new provider or model that matches the tag is governed the moment it is registered, with no extra grants to write.

How do guardrails stay consistent across providers?

Service policies evaluate inputs and outputs with the same logic on every endpoint and return allow, ask, or deny, so a prompt handled one way for one provider is handled the same for another.

Can I switch providers without rewriting my application?

Yes. Each model is a governed securable reached through the gateway, so changing providers or adding fallbacks is a configuration change. Applications keep calling the same governed endpoint.

How do I track spend across providers?

Usage tracking is on by default and attributes tokens, latency, and estimated spend to each provider and consumer, so overages surface early. Full payloads can be logged to governed inference tables for audit.