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Logfire is Pydantic’s observability platform and SDK for collecting and examining traces, metrics, and logs from Python applications. You instrument the libraries and operations that matter, then inspect related telemetry—including with SQL—while OpenTelemetry provides a route for standard instrumentation and compatible backends.
What Logfire does for a Python application
Observability data helps connect an application’s behavior to the work it performs. Logfire’s Python SDK can capture spans for timed operations such as database queries, outbound requests, and validation, linking related work into traces. Developers can also instrument their own operations. The platform presents traces, metrics, and structured logs for examination and SQL querying. See Pydantic’s Python product page and the Logfire project repository for current product and SDK details.
This is a way to collect and investigate telemetry, not evidence of a particular performance improvement. Pydantic’s product materials describe the product’s capabilities; they do not establish comparative benchmarks or overhead figures.
How to start instrumenting a Python app
The general pattern is to install the SDK with extras for the integrations your application uses, configure it, and enable instrumentation for the relevant libraries. Treat the snippets below as examples rather than a complete recipe: the exact configuration depends on your framework, library versions, and deployment.
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- Install the SDK and integration extras. Use the current Python setup guide to select extras matching your stack.
- Authenticate. The documented workflow supports authentication through the CLI or a token. Follow the current setup guide for the appropriate option.
- Configure the SDK. Import Logfire and call
logfire.configure()in the application’s initialization path. - Instrument the libraries you use. The product page shows examples such as
logfire.instrument_fastapi(app),logfire.instrument_httpx(), andlogfire.instrument_sqlalchemy(engine=engine). Check the live integration documentation for supported versions and setup details. - Instrument important custom work. Where automatic library instrumentation does not express the operation you need to investigate, add instrumentation for that operation using the SDK’s current guidance.
OpenTelemetry and portability
OpenTelemetry is central to Logfire’s design. Pydantic says standard OpenTelemetry instrumentation can send data to Logfire, and its documentation describes configuring the SDK to send data to another OpenTelemetry-compatible backend. Its FAQ puts the position this way: “Logfire is built on OpenTelemetry, the industry standard for observability, and works with any language.” Read the Logfire FAQ for the vendor’s compatibility details.
That compatibility is an architectural option, not a promise that changing backends is cost-free. A move can still require changes to credentials, exporters, data handling, dashboards, alerts, retention settings, or operational procedures. If portability matters, verify that the instrumentation and export path you plan to use meet your team’s requirements.
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Querying traces, metrics, and logs
Logfire’s product page describes SQL querying across traces, metrics, and logs. This gives developers a familiar way to explore collected telemetry and investigate application behavior. The practical value depends on the data your application emits and the questions your team needs to answer; SQL support alone does not establish superiority over another observability platform. See the current product documentation for details of the query workflow.
Integrations and deployment options
Examples across a Python stack
Pydantic’s Python page lists integrations including FastAPI, Django, Flask, Starlette, SQLAlchemy, Psycopg, asyncpg, Redis, PyMongo, HTTPX, Requests, aiohttp, Celery, Airflow, Pydantic AI, OpenAI, Anthropic, and LangChain. The FAQ also describes broader coverage spanning Python AI and LLM libraries, web frameworks, databases, JavaScript and TypeScript, and other OpenTelemetry-compatible applications. The list can change, and coverage does not imply identical depth or support status for every integration. Check the live integration information against your framework and library versions.
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Cloud or self-hosted Enterprise
Pydantic describes Logfire Cloud as managed SaaS and says Enterprise arrangements are available in cloud or self-hosted forms. The FAQ directs readers to the current pricing and usage documentation for plan specifics; verify the available deployment, limits, and terms directly in the official FAQ.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What Logfire costs
Pydantic’s product page, accessed September 30, 2026, advertised 10 million free spans, logs, and metrics per month with no credit card required. This is a vendor-advertised allowance, not a guarantee of permanent terms or universal eligibility. Before adopting it, check the current Logfire product page and the FAQ for current pricing, usage limits, and eligibility.
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How to evaluate Logfire for your team
Rather than treating any single feature as decisive, compare Logfire with the observability stack you already use on the dimensions that affect your application and operations:
- Instrumentation and coverage: Check whether your frameworks, databases, clients, and AI libraries are covered, and whether the integration depth suits your needs.
- OpenTelemetry compatibility: Confirm how your existing instrumentation sends data and whether the export options support your planned backend or migration path.
- Query workflow: Assess whether SQL over your telemetry fits how your team investigates incidents and application behavior.
- Deployment: Determine whether managed Cloud or an Enterprise cloud or self-hosted arrangement meets your operational requirements.
- Usage, retention, and price: Check the current plan limits, data retention, and charges against your expected telemetry volume in the official pricing and usage information.
Vendor documentation establishes the capabilities Pydantic describes, but not independent performance or comparative superiority. A decision should therefore rest on your required integrations, deployment constraints, telemetry needs, and current plan terms.
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