Comparison

Logfire vs SigNoz

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 comparison

Quick comparison

Logfire and SigNoz compared feature by feature
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)

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Pricing

Retention changes the byte price

Worked SigNoz Teams Cloud pricing examples across comparable retention periods
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)

See the exact pricing math and sources 10 published inputs · 3 official sources · checked August 20, 2026

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 August 20, 2026.

Published inputs

Logfire Team base: $49/month Pydantic Logfire source
Logfire included telemetry: 10,000,000 records/month Pydantic Logfire source
Logfire additional telemetry: $2 per 1,000,000 records Pydantic Logfire source
SigNoz Teams Cloud base: $49/month SigNoz source
SigNoz included usage value: $49/month SigNoz source
SigNoz traces, 15-day retention: $0.30/GB SigNoz source
SigNoz traces, 30-day retention: $0.40/GB SigNoz source
SigNoz traces, 90-day retention: $0.60/GB SigNoz source
SigNoz metrics, 1-month retention: $0.10/million samples SigNoz source
SigNoz metrics, 3-month retention: $0.12/million samples SigNoz source

Assumptions

Teams Cloud minimum: max($49, metered usage) The $49 monthly price includes $49 of any mix of logs, traces, and metrics; only usage beyond that value is additional. SigNoz source
Gigabyte: 1 GB = 1,000,000,000 bytes SigNoz's billing API calculates log and span volume in decimal gigabytes.
Compared deployment: Managed cloud only Community Edition is a separate self-managed deployment, so the model does not assign it a synthetic monthly cost.
Retention: Shown beside every total SigNoz changes its byte and sample rates when retention changes; the headline rates use 15 days for traces and one month for metrics. SigNoz source SigNoz source

Calculations

100 GB of traces per month

The Teams Cloud minimum absorbs 100 GB at both 15 and 30 days; 90-day retention raises metered usage to $60.

SigNoz · 15 days

$49

Trace usage 100 GB × $0.30/GB $30
Teams Cloud minimum max($49 included usage, $30 metered usage) $49

SigNoz · 30 days

$49

Trace usage 100 GB × $0.40/GB $40
Teams Cloud minimum max($49 included usage, $40 metered usage) $49

SigNoz · 90 days

$60

Trace usage 100 GB × $0.60/GB $60
Teams Cloud minimum max($49 included usage, $60 metered usage) $60

1 TB of traces per month

At 1,000 decimal GB, the published retention tiers produce materially different totals.

SigNoz · 15 days

$300

Trace usage 1,000 GB × $0.30/GB $300
Teams Cloud minimum max($49 included usage, $300 metered usage) $300

SigNoz · 30 days

$400

Trace usage 1,000 GB × $0.40/GB $400
Teams Cloud minimum max($49 included usage, $400 metered usage) $400

SigNoz · 90 days

$600

Trace usage 1,000 GB × $0.60/GB $600
Teams Cloud minimum max($49 included usage, $600 metered usage) $600

SigNoz's 864-million-sample metrics example

The vendor models 10,000 time series reporting every 30 seconds for a 30-day month; three-month retention raises the sample rate.

SigNoz · 1 month

$86.40

Monthly samples 10,000 series × 2 samples/min × 60 × 24 × 30 864M samples
Monthly total max($49 included usage, 864M × $0.10/M) $86.40

SigNoz · 3 months

$103.68

Monthly samples 10,000 series × 2 samples/min × 60 × 24 × 30 864M samples
Monthly total max($49 included usage, 864M × $0.12/M) $103.68

Source ledger

Pydantic Logfire — Pricing and plans for Pydantic Logfire Official vendor source · checked August 20, 2026 https://pydantic.dev/pricing

“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.
SigNoz — SigNoz pricing and calculator Official vendor source · checked August 20, 2026 https://signoz.io/pricing/

“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.
SigNoz — Configure retention periods for logs, traces and metrics Official vendor source · checked August 20, 2026 https://signoz.io/docs/userguide/retention-period/

“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.

What this calculation does not include

The worked totals are SigNoz-only calculations, not equivalent Logfire workloads; record count cannot be recovered from byte or sample volume.
SigNoz's headline $0.30/GB trace rate includes 15-day retention. The 30- and 90-day totals use the higher rates exposed by its official calculator.
Logfire Team includes 30-day retention and Growth supports up to 90 days, but no Logfire total is manufactured from a byte or sample count.
Community Edition is a separate self-managed deployment and is outside this cloud-pricing comparison.
Taxes, discounts, support contracts, and other enterprise terms are excluded.

Key differences

Why teams choose Logfire

Managed cloud, differentiated by workflow

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.

Developer velocity

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.

First-class AI support

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.

Cloud pricing, without a fake self-hosted estimate

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.

Decision guide

Which should you choose?

Choose Logfire if

You want observability in minutes, not weeks
You don't want to maintain monitoring infrastructure
You're building AI applications and need first-class AI observability
You want three lines of code, not a deployment project
You prefer to focus on your app, not your monitoring stack

Choose SigNoz if

You must self-host for strict data residency requirements
You have a DevOps team ready to manage the stack
You want full control and customization of your observability infrastructure

FAQ

Common questions

Does this pricing comparison include SigNoz Community Edition?

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.

How is setup different?

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.

Can I use Logfire SDK with SigNoz?

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.

Does Logfire support infrastructure metrics?

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.

What about AI/LLM observability?

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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