AgentRPC vs AI Gateway
Detailed side-by-side comparison to help you choose the right tool
AgentRPC
🔴DeveloperIntegrations
AgentRPC: Open-source RPC framework (Apache 2.0) that lets AI agents call functions across network boundaries without opening ports. Supports TypeScript, Go, and Python SDKs with built-in MCP server compatibility.
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FreeAI 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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AgentRPC - Pros & Cons
Pros
- ✓Bridges network boundaries without VPN or port configuration — register functions from private VPCs, Kubernetes clusters, and firewalled environments in minutes using outbound-only connections
- ✓Long-polling SDKs solve the 30-60 second HTTP timeout problem that breaks agent tasks running for minutes — critical for database queries, report generation, and multi-step data processing
- ✓Multi-language SDKs across 3 languages (TypeScript, Go, Python) with a 4th (.NET) in development let polyglot teams expose functions from every stack through one unified RPC layer
- ✓Built-in MCP server in the TypeScript SDK means instant compatibility with Claude Desktop, Cursor, and any MCP-compatible host without additional configuration
- ✓OpenAI-compatible tool definitions work with Anthropic, LiteLLM, and OpenRouter without modification — covering essentially every major LLM provider through a single tool schema
- ✓Open-source under Apache 2.0 license on GitHub with optional managed hosting available — permits unrestricted commercial use, self-hosting, and modification with no vendor lock-in
Cons
- ✗Small user community with very few public production deployment examples or documented case studies as of early 2026 — limits available reference architectures
- ✗Documentation covers setup basics but lacks depth on security hardening, scaling patterns, and production deployment best practices
- ✗Adds unnecessary complexity for publicly accessible tools — overkill when direct HTTP calls or standard MCP servers work fine
- ✗Managed server adds a network hop that introduces tens of milliseconds of latency — meaningful overhead for sub-millisecond function calls
- ✗.NET SDK still in development — teams using C# or F# cannot use AgentRPC yet and have no announced timeline
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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