Managed AI gateway vs LiteLLM?

Summary

• Unity Gateway is a Databricks service: a single control plane for model inference, tools, and skills, governed with Unity Catalog permissions.
• LiteLLM is an open-source Python SDK plus a self-hosted gateway (the Proxy Server) that exposes one OpenAI-compatible endpoint for many LLM providers.
• Both document routing with fallbacks, rate limits, budgets, guardrails, and usage or spend records. The main difference is who operates the gateway and where governance lives.
• We believe Unity Gateway fits teams that already govern data and AI assets in Unity Catalog, and LiteLLM fits teams that want to deploy and run an open-source gateway themselves.
• Each vendor documents an OpenAI-compatible API path, so test any combination of the two in a non-production workspace first.

Managed AI gateway vs LiteLLM?

Unity Gateway and LiteLLM both route LLM requests, and both document access control, rate limits, budgets, guardrails, and usage records. The main difference is where the gateway lives. Unity Gateway is a Databricks service that you configure in your workspace and govern with Unity Catalog. LiteLLM is open-source software that you deploy and operate yourself, or use as a Python SDK inside an application.

What is Unity Gateway?

Databricks describes Unity Gateway as "a single control plane for model inference, tools, and skills across your AI applications and coding agents." Model services route requests to Databricks-hosted models or, through model providers, to external providers such as OpenAI, Anthropic, Amazon Bedrock, and Azure Foundry using centrally managed credentials. Unity Gateway and Unity Catalog asset governance are generally available, and service policies are in Beta.

What is LiteLLM?

LiteLLM is an open-source library that gives you one interface to call many LLM providers in the OpenAI format. Its Proxy Server is a self-hosted server with one OpenAI-compatible endpoint, virtual keys, spend tracking, load balancing, fallbacks, guardrails, and an Admin UI. LiteLLM is MIT licensed except for its enterprise directory, and its documentation lists "SSO/SAML, audit logs, and advanced security" under Enterprise.

Unity Gateway vs LiteLLM: how do they compare?

Capability	Unity Gateway	LiteLLM
What you run	A Databricks service you configure in the UI, REST API, SDKs, CLI, or Terraform	An SDK in your code, or a Proxy Server you deploy with Docker, Helm, or Terraform and a Postgres database
Access control	Unity Catalog privileges on model services, providers, and MCP servers	Virtual keys per user, team, and project, each with its models, budgets, and rate limits
Routing	Traffic splitting across destinations and ordered fallbacks	Router with load balancing, fallbacks, and retries
Limits and budgets	Rate limits in requests or tokens per minute; budgets that alert or block usage	Budgets and rate limits per key, team, or user
Guardrails	Service policies (Beta) with built-in checks for PII, prompt injection, and unsafe content	Content filters and PII masking
Usage records	system.ai_gateway.usage table, inference tables, and a built-in usage dashboard	Spend tracking per key, team, user, and tag, plus logging integrations and an Admin UI
Providers	Databricks-hosted models plus external providers through model providers	Many providers in the OpenAI format, including a documented Databricks provider

When might each one fit?

We believe Unity Gateway fits when Unity Catalog is already your governance layer and you want model access, MCP tool access, skills, and usage records managed beside your data permissions. LiteLLM fits when you want an open-source gateway that you deploy and operate yourself, or an SDK that calls many providers from application code. You can also evaluate both side by side.

Can you use both?

Possibly. LiteLLM documents a Databricks provider, which uses the databricks/ model prefix, and an OpenAI-compatible endpoints provider. Databricks documents that model services support OpenAI-compatible Chat Completions, plus provider-native APIs such as Anthropic Messages for compatible models. The cited pages document each side separately, so validate the exact combination in a non-production workspace.

How does Databricks approach AI governance?

Unity Gateway is built on Unity Catalog. Models, model services, model providers, MCP servers, and skills are Unity Catalog securables with catalog.schema.name names, so you grant access with the same privileges you use for tables. Rate limits apply at the service level, as a default per user, and as custom limits for users, service principals, and groups. Budgets can send an alert or block usage when a threshold is reached. The AI governance guide walks through setting these up.

FAQs

Does either one support caching?

LiteLLM documents response caching, including semantic caching. Databricks documents session affinity for model services, which keeps a session's requests on the same destination to take advantage of prefix caching.

Do I need Databricks to use LiteLLM?

No. LiteLLM is open source and documents its own deployment paths. Unity Gateway is a Databricks product and requires a Databricks workspace in a supported region.

Is Unity Gateway generally available?

Databricks documentation says Unity Gateway and Unity Catalog asset governance are generally available. Service policies are in Beta and need the Unity Gateway beta features turned on from the account console Previews page.