AI Gateway vs Competitors: Side-by-Side Comparisons [2026]

The new AI Gateway, launched in Beta and visible in the left nav of the Databricks UI, is a broader central governance layer that covers LLM endpoints, MCP servers, and coding agents together. The previous AI Gateway was scoped only to model serving endpoints — external model endpoints, Foundation Model API endpoints, and custom model endpoints — and focused on usage tracking, payload logging, rate limits, and guardrails at the endpoint level. Both versions coexist in the documentation as of April 15, 2026, and Databricks recommends account admins enable the new version from the account console Previews page. Existing serving-endpoint governance continues to function while teams migrate.

According to the official documentation, AI Gateway features do not incur charges during the Beta period. Standard Databricks consumption charges for model serving, DBU usage, and underlying compute still apply, and once the product moves to GA, enterprise pricing will be set through standard Databricks contracts. Because pricing is not published publicly, prospective customers should request a quote through their Databricks account team. This makes the Beta window a good opportunity to pilot full governance before any commercial commitment.

The documentation explicitly calls out support for Cursor, Gemini CLI, Codex CLI, and Claude Code, which covers most of the dominant AI coding agents developers use in 2026. Integration routes each agent's model calls through the AI Gateway, so prompt/response payloads, token usage, and cost attribution are captured in Unity Catalog inference tables. This lets platform teams apply the same rate limits and guardrails to developer coding traffic that they apply to production LLM workloads. Other OpenAI-compatible agents can also point at AI Gateway endpoints using the OpenAI client.

AI Gateway supports three MCP deployment patterns: Databricks-managed MCP servers that expose native platform features, external MCP servers connected through managed connections, and custom MCP servers hosted as Databricks Apps. For each, AI Gateway enforces access control through Unity Catalog permissions and logs every MCP interaction for audit. Non-Databricks MCP clients can also connect to Databricks-hosted MCP servers through documented client connection flows. This unified governance is differentiated from pure LLM gateways — based on our analysis of 870+ AI tools, AI Gateway is the only offering that natively governs MCP servers alongside LLM endpoints.

AI Gateway emits two complementary telemetry streams into Unity Catalog. System tables capture endpoint-level usage and cost aggregates for budgeting and chargeback, while inference tables capture full request and response payloads as Delta tables for granular audit, replay, and quality monitoring. Both are queryable through standard SQL, notebooks, or BI tools, and inherit Unity Catalog row- and column-level access controls. Rate limits can be configured per endpoint to cap capacity and prevent runaway cost, and guardrails can be applied to block unsafe content across providers consistently.