AI Gateway vs LiteLLM
Detailed side-by-side comparison to help you choose the right tool
AI Gateway
Developer Tools
Databricks central AI governance layer for LLM endpoints, MCP servers, and coding agents. Provides enterprise governance with unified UI, observability, permissions, guardrails, and capacity management across providers.
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CustomLiteLLM
đ´DeveloperApp Deployment
LiteLLM: Y Combinator-backed open-source AI gateway and unified API proxy for 100+ LLM providers with load balancing, automatic failovers, spend tracking, budget controls, and OpenAI-compatible interface for production applications.
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đĄ Our Take
Choose AI Gateway if you need enterprise-grade RBAC, payload-level audit, and a managed UI tied into Databricks. Choose LiteLLM if you are a smaller team or individual developer who wants a free, open-source, self-hosted proxy with broad provider support and are comfortable operating the infrastructure yourself.
AI Gateway - Pros & Cons
Pros
- âNative integration with Unity Catalog means permissions, audit logs, and lineage work identically to the rest of your Databricks data assets without extra IAM plumbing
- âOpenAI-compatible client interface allows existing application code to point at AI Gateway endpoints with minimal refactoring
- âGoverns three distinct asset types (LLM endpoints, MCP servers, coding agents) in a single pane of glass â rare across the 870+ tools in our directory
- âNo charges during Beta (confirmed on docs as of April 15, 2026), letting teams pilot full governance workflows before committing to enterprise pricing
- âSupports major coding agents including Cursor, Claude Code, Gemini CLI, and Codex CLI, covering the dominant agent tools developers use in 2026
- âInference tables land as Delta tables in Unity Catalog, making audit and monitoring queries trivially accessible via SQL or notebooks
Cons
- âOnly available inside the Databricks platform â teams not already on Databricks cannot adopt AI Gateway as a standalone product
- âCurrently in Beta, meaning feature set, APIs, and limits may shift before GA and enterprise SLAs may not apply
- âTwo parallel versions exist (new AI Gateway in left nav vs. previous AI Gateway for serving endpoints), which creates documentation and migration ambiguity
- âCustom MCP server hosting requires packaging as a Databricks App, adding a layer of platform-specific deployment knowledge
- âPricing is opaque enterprise-contract based with no public tier breakdown, making TCO comparisons against standalone gateways difficult
LiteLLM - Pros & Cons
Pros
- âFully open-source core with 40K+ GitHub stars and 1,000+ contributors
- âOpenAI-compatible API requires minimal code changes for adoption
- âSelf-hosted deployment keeps all data on your infrastructure â no third-party routing
- âGranular spend tracking with per-key, per-user, per-team budget enforcement
- âAutomatic failover and intelligent load balancing for production reliability
- âRapid new model support â typically within days of provider launch
- âBacked by Y Combinator with active development and weekly releases
- âNative integrations with Langfuse, Langsmith, OpenTelemetry, and Prometheus
Cons
- âRequires Docker and infrastructure knowledge for self-hosted deployment
- âEnterprise features like SSO and audit logging locked behind paid tier
- âEnterprise pricing requires sales consultation with no published rates
- âConfiguration complexity increases significantly with many providers and routing rules
- âLimited built-in UI for non-technical users â primarily CLI and API-driven
- âObservability integrations require separate setup of Langfuse, Grafana, etc.
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