AI Gateway vs Helicone
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.
Was this helpful?
Starting Price
CustomHelicone
đ´DeveloperBusiness Analytics
Open-source LLM observability platform and API gateway that provides cost analytics, request logging, caching, and rate limiting through a simple proxy-based integration requiring only a base URL change.
Was this helpful?
Starting Price
FreeFeature Comparison
Scroll horizontally to compare details.
đĄ Our Take
Choose AI Gateway if you need full governance â permissions, rate limits, guardrails, and MCP control â integrated into a data platform. Choose Helicone if your primary need is lightweight LLM observability and cost tracking with a fast open-source option and a generous free tier, and you do not require deep access control or MCP governance.
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
Helicone - Pros & Cons
Pros
- âProxy-based integration requires only a base URL change â genuinely zero-code setup for OpenAI and Anthropic users
- âReal-time cost analytics with per-user, per-feature, and per-model breakdowns are best-in-class for LLM spend management
- âGateway-level request caching can reduce API costs 20-50% for applications with repetitive queries
- âOpen-source with self-hosted option gives full data control for security-conscious teams
- âBuilt-in rate limiting and retry logic at the proxy layer eliminates operational code from your application
Cons
- âProxy architecture adds 20-50ms latency per request, which compounds in latency-sensitive agent loops
- âIndividual request-level visibility doesn't capture multi-step agent workflows or retrieval pipeline context natively
- âSession and trace grouping features are less mature than Langfuse or LangSmith's dedicated tracing capabilities
- âFree tier limited to 10,000 requests/month â production applications will quickly need the $20/seat/month Pro plan
Not sure which to pick?
đ¯ Take our quiz âđ Security & Compliance Comparison
Scroll horizontally to compare details.
Price Drop Alerts
Get notified when AI tools lower their prices
Get weekly AI agent tool insights
Comparisons, new tool launches, and expert recommendations delivered to your inbox.