SigNoz is powerful but requires maintenance. Logfire gives you the same insights with zero infrastructure to manage. Get observability in minutes, not weeks.
| Feature | Logfire | SigNoz |
|---|---|---|
| Platform | Managed SaaS (no infra to manage) | Open-source, self-host or cloud |
| Setup | 3 lines of code | Deploy collectors, configure OTel |
| AI/LLM Support | Native, one function call + eval loop | gen_ai spans over OTel; no eval loop |
| SDKs | First-class Python, JS, Rust | No SDKs (standard OTel only) |
| Maintenance | Zero (we handle it) | You manage ClickHouse, collectors, storage |
| Pricing | Free 10M/mo, then $2/M spans | "Free" + infrastructure costs |
| Infrastructure Metrics | Full-stack (host metrics, traces & logs) | Full-stack (including Prometheus-style) |
SigNoz is open-source and self-hostable. This gives you full control but means deploying and managing ClickHouse, running OTel collectors, handling upgrades, scaling, and availability. Logfire is fully managed: zero infrastructure, automatic scaling and upgrades, enterprise-grade availability. Focus on your application, not your monitoring.
Logfire setup: import logfire; logfire.configure(); logfire.instrument_openai(). Three lines, and you're observing AI calls. SigNoz requires deploying collectors, configuring OpenTelemetry pipelines, and managing infrastructure. When something isn't working, which would you rather debug?
SigNoz ingests gen_ai spans over OpenTelemetry and shows agent traces with token and cost data, so it does have LLM observability. What it does not have is the AI-engineering loop: evals, a prompt optimizer, and managed config. Logfire adds that on top of the same OTel-native tracing, with purpose-built LLM panels for conversations and tool calls and automatic token and cost tracking.
SigNoz "free" doesn't include: server costs (ClickHouse is resource-intensive), engineering time for setup and maintenance, on-call burden for your monitoring infrastructure, or opportunity cost of not building features. Logfire pricing is transparent: free tier of 10M logs, spans, and metrics, then $2 per million records. No hidden costs.
SigNoz is open-source and free to self-host, but the "free" doesn't include: server costs (ClickHouse is resource-intensive), engineering time for setup and maintenance, on-call burden for your monitoring infrastructure, or opportunity cost of not building features. Logfire's pricing is transparent: free tier of 10M logs, spans, and metrics, then $2 per million records.
Logfire: three lines of code (import logfire; logfire.configure(); logfire.instrument_openai()). SigNoz: deploy collectors, configure OpenTelemetry, manage ClickHouse. When something isn't working, Logfire means looking at 3 lines of config; SigNoz means debugging your collector pipeline.
Yes! The Logfire SDK can send data to any OpenTelemetry-compatible backend, including SigNoz. If you like our SDK's simplicity but want to run your own backend, you can use the Logfire SDK with SigNoz's backend.
Yes. Logfire supports infrastructure monitoring alongside AI and application observability: collect logs and host metrics from Kubernetes, hosts, and databases, then correlate them with application traces. SigNoz may still be a fit if you need a self-managed observability stack.
SigNoz ingests gen_ai spans over OpenTelemetry and renders agent traces with token and cost data, so it does have LLM observability. What it lacks is the AI-engineering loop: no evals, no prompt optimizer, no managed config. Logfire has that loop plus purpose-built LLM panels, and one function call instruments OpenAI, Anthropic, LangChain, and more with automatic token tracking.
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