How Helicone's AI Gateway Helps Route and Monitor AI App Requests
Helicone is an LLMOps platform that sits between your application and your LLM providers, acting as a gateway that routes requests and a monitoring layer that records what happens to them. It's aimed at teams building AI apps who need to debug, analyze, and keep those apps reliable in production. The site describes it as "the LLMOps platform behind the fastest-growing AI companies," with routing and monitoring for reliable AI apps. You can try it with a 7-day free trial and no credit card required, per the site's own signup messaging.
What the AI Gateway does
The gateway is the piece that intercepts traffic between your app and the model providers. Instead of your code calling OpenAI, Anthropic, or another provider directly, requests pass through Helicone. That single insertion point is what makes the rest of the platform possible: because every request flows through one place, Helicone can route it and record it.
Routing here means directing requests to a model or provider. The practical value is that you change routing behavior at the gateway rather than rewriting call sites across your codebase. The site frames the overall product around three verbs — route, debug, and analyze — which maps cleanly onto the gateway (route) and the observability layer (debug, analyze).
What you can monitor
Once requests flow through the gateway, Helicone exposes them across several dimensions. The dashboard navigation on the site lists these views:
| View | What it covers |
|---|---|
| Requests | Individual calls through the gateway |
| Segments | Grouped slices of traffic |
| Sessions | Related requests treated as one session |
| Users | Activity attributed to specific users |
| HQL | A query interface over your request data |
| Improve Prompts | Prompt-level iteration |
| Datasets | Collected data for evaluation or testing |
| Playground | Experimenting with prompts/models |
| Monitor | Rate limits and alerts |
The distinction between Requests, Sessions, and Users matters for debugging. A single user action in your app may trigger several LLM calls; viewing them as a session lets you see the whole interaction rather than isolated calls. Attributing traffic to users lets you answer "which users are hitting errors or high latency" rather than only "how many calls failed."
How this supports reliability
Reliability work on AI apps usually comes down to three questions: what did the model actually receive and return, which requests are failing or slow, and what changed when quality dropped. The gateway-plus-observability combination addresses each:
- What happened — every request is logged as it passes through, so you have the actual prompt and response rather than a reconstruction.
- Where it's failing — the Monitor view covers rate limits and alerts, so you can catch provider throttling or error spikes instead of discovering them from user complaints.
- What to change — Improve Prompts, Datasets, and Playground give you a place to iterate on prompts and test them against collected data.
HQL is worth calling out separately: it's a query interface over your request data, which means you can ask specific questions of your logs (for example, filtering to a time window or a user segment) instead of only reading a fixed dashboard.
Where it fits in an LLMOps stack
Helicone's positioning is as an LLMOps platform, and the gateway is the mechanism that makes the platform's data complete. Observability tools that rely on SDK-level instrumentation only see what your code reports; a gateway sees the traffic itself. That's the architectural reason routing and monitoring are bundled here rather than sold as separate concerns — the routing layer is what produces the monitoring data.
If you're evaluating it, the useful question isn't "does it log requests" but "does routing through a gateway fit how my app is built." If you already call providers directly from many services, inserting a gateway is an architectural change; if you're early or centralizing LLM access anyway, the gateway is a natural place to put that boundary.
Getting started
The site offers a free trial with no credit card required for 7 days. To evaluate it against your own traffic, the practical sequence is: route a small share of requests through the gateway, confirm the Requests view shows them, then check whether Sessions and Users line up with how you think about your app's activity. If those views match your mental model, the debugging and alerting features are worth the setup cost; if they don't, that mismatch is itself useful information before you commit further.