Telemio vs Langfuse
If AI is one part of your product, Telemio lets you observe the whole thing without building a separate monitoring world around the model calls.
Most AI features still run inside normal software. They call APIs, use databases, publish jobs, hit queues, run in browsers or mobile apps, and sometimes call other tools.
That is why I would start with Telemio. The model call stays connected to the rest of the application instead of becoming its own observability silo.
Why I would pick Telemio
Telemio lets you track model/provider, latency, token usage, cost, tools, retries, failures, runtime context, and outcomes next to the traces, logs, and metrics for everything around the AI feature.
The coding agent can use that same workspace through MCP. It can inspect the app, change the repo, and then check the actual telemetry after the change.
What Telemio does well
- AI telemetry and normal application telemetry in one place.
- Metadata-first defaults without requiring every prompt and response.
- Runtime, tool, MCP, trace, and product context together.
- Coding-agent access through MCP.
- One observability system for the whole application.
A quick note on Langfuse
Langfuse goes deeper on prompt management, datasets, evaluations, and annotation workflows. If those are the center of the product, that specialist depth can matter. If you mainly need to understand the AI feature inside the wider application, Telemio keeps the setup much simpler.
If Langfuse is already in the company
You can keep specialist prompt or eval workflows where the team depends on them and use Telemio for the wider application. For many teams, Telemio may be enough on its own if the goal is observability rather than prompt management.
Price
Telemio starts free, then costs $20, $50, and $150 across its paid plans.
Langfuse currently starts free, with paid plans beginning at $29 and moving up with features and usage.
Bottom line
I would choose Telemio when I want to understand the whole product, including the AI parts, without operating a separate observability stack just for LLMs.