SigNoz offers a broad OpenTelemetry stack in managed cloud and self-hosted forms. Logfire is the direct path from application code to correlated full-stack and AI telemetry, with evaluation and improvement workflows built into the same product.
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| Feature | Logfire | SigNoz |
|---|---|---|
| Platform | Managed SaaS | Managed Teams Cloud or self-hosted Community Edition |
| Setup | 3 statements for a Python AI setup | Configure OTel SDKs and exporters; collectors where needed |
| AI/LLM Support | Native, one function call + eval loop | gen_ai spans over OTel; no eval loop |
| SDKs | First-class Python, JS, Rust | OpenTelemetry SDKs across languages |
| Maintenance | Managed service | Cloud backend managed; Community Edition self-managed |
| Cloud pricing | Personal: 10M records free; Team: $49/month + $2/M over 10M | Teams Cloud $49 + usage |
| Infrastructure Metrics | Full-stack (host metrics, traces & logs) | Full-stack (including Prometheus-style) |
SigNoz Teams Cloud manages its backend, while Community Edition gives you control of ClickHouse, upgrades, scaling, and availability. Logfire's advantage is the product workflow: common application integrations, PostgreSQL-compatible SQL, evaluations, and improvement tools in one managed service, without choosing or operating an observability backend.
Logfire setup for a Python AI application is three statements: import logfire; logfire.configure(); logfire.instrument_openai(). SigNoz Teams Cloud removes backend operations, but applications still use OpenTelemetry SDK and exporter configuration and some data sources need a collector. Logfire turns the most common Python integrations into one function call each and keeps that configuration in the application.
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.
The pricing table compares managed cloud products only. SigNoz Teams Cloud starts at $49, then meters trace bytes at $0.30/GB for 15 days, $0.40/GB for 30 days, or $0.60/GB for 90 days. Logfire Team starts at $49, includes 10 million records and 30-day retention, then charges $2 per million records; Growth supports up to 90 days. Because bytes do not reveal record count, the disclosure shows SigNoz's exact retention-sensitive totals without inventing a Logfire equivalent.
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| Illustrative monthly workload | Logfire | SigNoz |
|---|---|---|
| 100 GB traces/month | 30 days on Team; up to 90 days on Growth; billed by records | $49 (15/30 days) · $60 (90 days) |
| 1 TB traces/month | 30 days on Team; up to 90 days on Growth; billed by records | $300 (15 days) · $400 (30 days) · $600 (90 days) |
| 864M metric samples/month | 30 days on Team; up to 90 days on Growth; billed by records | $86.40 (1 month) · $103.68 (3 months) |
Calculation scope
SigNoz Teams Cloud list-price calculations at its published 15-, 30-, and 90-day trace-retention rates and 1- and 3-month metric-retention rates. No Logfire total is claimed because a GB or sample count does not reveal how many Logfire records the workload creates.
Basis: USD; public monthly list price. Prices checked .
The Teams Cloud minimum absorbs 100 GB at both 15 and 30 days; 90-day retention raises metered usage to $60.
100 GB × $0.30/GB $30 max($49 included usage, $30 metered usage) $49 100 GB × $0.40/GB $40 max($49 included usage, $40 metered usage) $49 100 GB × $0.60/GB $60 max($49 included usage, $60 metered usage) $60 At 1,000 decimal GB, the published retention tiers produce materially different totals.
1,000 GB × $0.30/GB $300 max($49 included usage, $300 metered usage) $300 1,000 GB × $0.40/GB $400 max($49 included usage, $400 metered usage) $400 1,000 GB × $0.60/GB $600 max($49 included usage, $600 metered usage) $600 The vendor models 10,000 time series reporting every 30 seconds for a 30-day month; three-month retention raises the sample rate.
10,000 series × 2 samples/min × 60 × 24 × 30 864M samples max($49 included usage, 864M × $0.10/M) $86.40 10,000 series × 2 samples/min × 60 × 24 × 30 864M samples max($49 included usage, 864M × $0.12/M) $103.68 “Covers 10 million logs, spans, and metrics every month.”
The official calculator shows 20 million records and no extra seats totaling $69/month on Team.
“Usage worth $49 (e.g. 163 GB logs/traces or 490 mn metric samples)”
SigNoz's own worked example prices 10,000 time series at a 30-second interval as 864 million samples and $86.40/month.
“Retention changes affect pricing.”
SigNoz Cloud offers 15-, 30-, 90-, 180-, and 365-day trace retention and 1-, 3-, 6-, and 13-month metric retention.
No. The table compares Logfire Cloud with SigNoz Teams Cloud, which starts at $49 a month and then meters traces and logs by gigabyte and metrics by sample. Community Edition is a separate self-managed deployment, so we do not assign it an invented monthly price.
Logfire's Python setup is three statements: import logfire; logfire.configure(); logfire.instrument_openai(). SigNoz Teams Cloud manages its backend, while your applications still need OpenTelemetry SDK and exporter configuration, plus a collector where the data source requires one. Logfire's common Python integrations configure that telemetry path directly from application code.
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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