Permit MCP Gateway vs AI Gateway
Detailed side-by-side comparison to help you choose the right tool
Permit MCP Gateway
Integrations
Secure AI agents with drop-in Model Context Protocol gateway that automates OAuth authentication, fine-grained authorization policies, and audit logging without code changes to existing MCP servers.
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CustomAI Gateway
Integrations
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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CustomFeature Comparison
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Permit MCP Gateway - Pros & Cons
Pros
- ✓Drop-in proxy architecture requires zero code changes to existing MCP servers or AI agents
- ✓Comprehensive identity binding ensures every AI agent action traces back to authenticated human users
- ✓Fine-grained authorization policies support RBAC, ABAC, and ReBAC models for flexible access control
- ✓SOC 2 Type II compliance with enterprise-grade security features and audit capabilities
- ✓Real-time policy updates via OPAL enable dynamic authorization changes without system restarts
- ✓Visual consent management editor reduces development time for custom authorization workflows
- ✓Agent fingerprinting and behavioral monitoring prevent privilege escalation and detect anomalies
- ✓Hybrid deployment options support both cloud and on-premises security requirements
Cons
- ✗Limited to MCP-compatible agents and servers, restricting applicability to emerging ecosystem
- ✗Proxy architecture introduces latency to agent operations through additional network hops and policy evaluation
- ✗Relatively new product category with limited real-world deployment case studies and best practices
- ✗Requires understanding of OPA policy language for advanced authorization rule customization
- ✗Enterprise pricing model may be cost-prohibitive for small organizations with limited AI agent deployments
- ✗Dependency on Model Context Protocol adoption limits current market applicability
AI Gateway - Pros & Cons
Pros
- ✓Native integration with Unity Catalog means permissions, audit logs, and lineage work identically to the rest of your Databricks data assets without extra IAM plumbing
- ✓OpenAI-compatible client interface allows existing application code to point at AI Gateway endpoints with minimal refactoring
- ✓Governs three distinct asset types (LLM endpoints, MCP servers, coding agents) in a single pane of glass — rare across the 870+ tools in our directory
- ✓No charges during Beta (confirmed on docs as of April 15, 2026), letting teams pilot full governance workflows before committing to enterprise pricing
- ✓Supports major coding agents including Cursor, Claude Code, Gemini CLI, and Codex CLI, covering the dominant agent tools developers use in 2026
- ✓Inference tables land as Delta tables in Unity Catalog, making audit and monitoring queries trivially accessible via SQL or notebooks
Cons
- ✗Only available inside the Databricks platform — teams not already on Databricks cannot adopt AI Gateway as a standalone product
- ✗Currently in Beta, meaning feature set, APIs, and limits may shift before GA and enterprise SLAs may not apply
- ✗Two parallel versions exist (new AI Gateway in left nav vs. previous AI Gateway for serving endpoints), which creates documentation and migration ambiguity
- ✗Custom MCP server hosting requires packaging as a Databricks App, adding a layer of platform-specific deployment knowledge
- ✗Pricing is opaque enterprise-contract based with no public tier breakdown, making TCO comparisons against standalone gateways difficult
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