Built with Pydantic
Case studies from teams using our tools to build, observe, evaluate and ship reliable AI applications.
Customer stories
How SentinelOne evaluates the AI agents behind a 99.7% agreement rate
SentinelOne holds a 99.7% agreement rate, and Pydantic Evals in CI/CD cut its time to ship new AI features by 30%.
99.7% Agreement rate across hundreds of thousands of agentic investigations
How Sophos surfaced silent production failures
Logfire connects Sophos's LLM calls, APIs, and workers, exposing silent production failures.
“We can see the whole conversation thread, the LLM call, and every API hop, all in one go”
Peter Kim Principal Software Engineer
How Evergreen.ai runs its offline judges against live traffic
Logfire and Pydantic Evals give Evergreen.ai end-to-end observability and continuous evaluation for financial-services AI agents.
“The same evaluators we use with our actual eval dataset are now being applied against a real-time dataset”
Bryan Godwin CTO
How Elumynt cut LLM costs by over 80% with Pydantic Monty
80%+ Lower LLM costs on daily agentic routines
How Silex Data cut AIOps agent costs by up to 32% with Pydantic Monty
10/10 Test incidents solved, including two that direct tool calling failed
How the Justice AI Unit validates AI output for judges and probation staff
1 Validated contract from Python backend to TypeScript client
How GIC made live evaluation possible at all
96.2% Reduction in trace query time after migrating from LangSmith
How Rendreal turns a conversation into a print order
3 Channels for ordering: web chat, WhatsApp, or any MCP-compatible assistant
How Dosu cut time to root cause across 54 agents
90% Less debugging time, using the Logfire MCP
How Boosted.ai unified tracing across Python and Go
12x Faster to find and fix issues, from an hour to five minutes
How STCC made every clinical answer reviewable
0% Hallucinations across 329 clinician-validated triage scenarios
How MindsDB cut agent experiments from weeks to hours
10x Better agent performance, one month after the switch
How Overjoy caught an agent burning 20x its usual spend
“Now we can see exactly what’s being called, how an agent thinks, what it costs”
Ishaan Nagpal Founding Engineer
How Datalayer cut the tokens a data question costs
90% Best case reduction in token use for data analysis tasks
How Lema AI rebuilt its risk agents on Pydantic AI
“We implemented the exact same thing in LangChain, LangGraph, and Pydantic AI. The latter was much cleaner”
Alon Menczer Engineering Lead
How Synera turns text prompts into engineering workflows
2 min To write a prompt that builds a workflow which took up to an hour
How Qualio turned plain-language criteria into release gates
98.6% Overall score on the compliance intelligence eval, across 24 tests
How AutonomyAI let its agents check their own work
“Most of our engineers and our internal systems now use the Logfire MCP for basically everything”
Tammuz Dubnov Co-founder & CTO
How ARIJ built a bilingual assistant for journalists
22 Countries across the Middle East and North Africa reached
How Mixam turned a billion print combinations into a guided order
2x Customer conversion, from around 20% to around 40%
Trusted by the world’s best engineering teams
“Logfire is the first observability product our team genuinely enjoys using.”
“I’ve been using it from the early stages of implementation, appreciating the simple setup with just a few lines of code and clarity of the UI”
“Everything in this product is easy to find, and you can extend it with your own tools if necessary, as it follows Python telemetry standards”
“Clean interface, ease of use, killer features”
“Logfire is extremely intuitive for agent builders and the SDK/MCP is built thoroughly for coding harnesses”
“Logfire has been incredible for our platform; our development team couldn’t be happier. We started with the basic observability and now have expanded to use the full AI tool set”
“Super clean UI - Love that it’s built on top of open telemetry - Really easy to setup and get logging w/ right away”
Reviews from Gartner Peer Insights
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01 — Install
$ uv add logfire
Resolved 7 packages in 128ms
Installed 7 packages in 89ms
+ logfire02 — Instrument
import logfire
logfire.configure()
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