Pydantic Official implementation partner

Pydantic Development Services

Typed data contracts your agents and services can trust

Bad data stops at the boundary, before it breaks your APIs, pipelines or LLM outputs. Typed contracts from an official Pydantic implementation partner, owned by your team.

Book a free consultation See how we work
Why validation projects stall

Validation at the end catches errors. Validation in the flow removes them.

When the only real check runs after the last step, bad records pass every hop before it. A Pydantic model at each boundary stops a record where it enters.

  1. 01

    Where it stallsSchemas get written around the data flow as it runs today, hand checks included.

    What we do We trace where records enter and change hands, and put a model there.

  2. 02

    Where it stallsEvery service keeps its own idea of what a valid record looks like.

    What we do One set of Pydantic models becomes the contract every service imports.

  3. 03

    Where it stallsModel output enters the pipeline as text, and someone checks it later.

    What we do Typed schemas check each model output on entry; a bad one raises an error.

How we work

Checks that move from the last step to every hop

One team, from the first data-flow map to contracts your engineers extend themselves.

0103

Your data flow, mapped

We trace where data enters, changes hands and is checked today. Often the real check is the last one, so an error travels the whole flow before anyone sees it.

  • Data-flow map, every boundary marked
  • Pydantic models for each boundary
  • Manual re-keying removed
  • Proof of Value on real, adversarial inputs
Engineers moved from hours of tedious setup to minutes, through multi-step validation rather than a single generation pass.
  1. 01

    Controlled outputs

    Outputs are validated against typed schemas, so malformed or hallucinated values are caught before they reach downstream systems.

  2. 02

    Settings management

    Typed configuration and settings management, so a wrong value fails at start-up rather than mid-run.

  3. 03

    Pipeline validation

    Data in the pipeline is validated and checked before processing, not after a report looks wrong.

  4. 04

    Quality extraction

    Extracted data must meet a defined quality bar, validated at the boundary.

  5. 05

    Typed contracts

    Typed contracts at service boundaries, shared by every service that reads the data.

The risk without validation

What unvalidated model output costs

These numbers reflect what happens when LLM outputs are not validated against typed schemas.

Pydantic catches malformed data entering and leaving your Python services. Pydantic AI catches schema-violating LLM outputs at the agent boundary. Logfire catches what shows up under real traffic: drift, creeping costs, eval scores that slide after a model or prompt change.

Healthcare hallucination rate

65.9%

Hallucination rates of "raw" LLMs in healthcare-related environments

Li et al., medRxiv (2025)

Multi-agent chain reliability

20%

End-to-end success in a 10-step workflow when each step is ~85% reliable — compound failure across the chain, not a single bad model.

Temporal — AI reliability in production

Layer three: after deploy

Logfire: what your agents do once they run.

The release is the middle of the lifecycle. Logfire, the Pydantic team's OpenTelemetry-based observability product, is why the platform carries on past the build. Free tier and usage-based plans; because it exports OTel, the same data can flow to Datadog, Grafana, or Honeycomb instead.

  1. 01

    Agent behaviour, traced step by step

    Traces of every LLM call, tool call and retry, so a wrong answer is a span you can open. Pydantic AI instruments itself.

  2. 02

    Cost, before it becomes a surprise

    Tokens and cost per call, agent and workflow, including validation retries, and which changes move the bill.

  3. 03

    Quality drift, caught by evals

    Pydantic Evals turn a model upgrade or prompt change that hurts quality into a failing score, weeks before a support ticket.

Case studies

Agentic AI systems our clients run

Text-to-workflow cuts engineers' tedious task time to seconds with Agentic AI platform

2 hours
Average time required to prepare Synera workflow
3 minutes
Time to generate new workflows using AI Agent

Multi-agent AI-support facilitating highly customized order completion

70%
of new users need significant guidance
96%+
of web-to-print orders handled end to end without human help

Reaching expert-level medical triage with guideline-executing LLMs

98%
Accuracy on the curated 50-scenario open benchmark
44% → 98%
Raw LLM baseline vs guideline-executing performance

The STCC telehealth triage guidelines are used by thousands of nurses at hundreds of healthcare facilities around the world, marking STCC as the gold standard in the industry.”

Patty Maynard
Chief Operating Officer, STCC
Engagement models

Three ways to start

Each path starts with a free call with a founder or senior AI engineer. A build goes ahead only after its Proof of Value meets the agreed threshold.

Engineering-led advisory

An audit of where unvalidated data and model outputs enter your services, and a plan for the contracts that would stop them.

Best whenYou want an engineering view before you commit to a build.

Book a free consultation Engineering-led advisory
  1. Pydantic AI agent development Best whenThe workflow needs an agent, not only validation.
  2. Forward Deployed Engineering Best whenYou have a team and a roadmap, and need Pydantic depth on it.
FAQ

Questions about Pydantic development

What is Pydantic? +
Pydantic is the most widely used data validation library in the Python ecosystem. It uses type hints to define schemas and validates data against them at runtime. Malformed inputs surface as structured errors — not silent failures. Its Rust core makes it one of the fastest validation libraries available. It is the validation layer underneath the OpenAI SDK, the Anthropic SDK, FastAPI, and LlamaIndex.
What is Pydantic AI and how is it different from Pydantic? +
They are two layers of the same platform, not competing choices. Pydantic enforces data schemas at runtime across any Python application — the contract layer. Pydantic AI is the agent runtime built on top of it by the same team, adding type-safe agents, validated tool calls, dependency injection, streaming validation, and automatic retries on malformed LLM outputs. Logfire then covers the third layer, observability after deploy. Most teams end up using all three: contracts underneath, agents on top, and traces telling them what happened once the system is live.
Do I need Pydantic, Pydantic AI, or both? +
It depends on where your problem lives. For typed schemas in FastAPI, validated config settings, or ETL pipelines — use Pydantic. For LLM agents that must return structured, validated outputs — use Pydantic AI. Many systems use both: Pydantic at the data layer, Pydantic AI at the agent layer.
What are the main use cases for Pydantic (without the AI framework)? +
Enforcing data contracts in FastAPI or Django Ninja APIs, managing application configuration with type safety, validating inputs and outputs in ETL pipelines, serializing and deserializing nested data structures, and generating JSON schemas from Python models. None of these require Pydantic AI.
Does Pydantic AI work with any LLM provider? +
Yes. Pydantic AI supports OpenAI, Anthropic, Google, Groq, Mistral, Cohere, AWS Bedrock, and any OpenAI-compatible API, plus locally hosted models. You can swap providers without rewriting agent logic. The framework supports fallback chains to route to a cheaper or faster model when the primary one is unavailable.
What is Pydantic Logfire? +
Logfire is the Pydantic team's OpenTelemetry-based observability product for Python and AI workloads — the commercial layer of the platform, alongside the open-source library and agent framework. It provides real-time LLM call traces, cost tracking, eval-based monitoring, and native Pydantic AI integration. It has a free tier and usage-based paid plans, and because it exports OTel you can send the same data to Datadog, Grafana, or Honeycomb instead of running Logfire as a standalone backend.
Why does observability belong in the same conversation as validation? +
Because validation and observability catch different failures. Typed schemas stop malformed data and schema-violating LLM outputs at the boundary — that is a build-time guarantee. What they cannot tell you is whether a shipped agent still behaves the way it did on day one. Agent behavior drifts as models are upgraded and prompts are edited, token costs creep as usage patterns change, and eval scores slide quietly. Logfire closes that loop, which is why we treat it as part of the AI engineering lifecycle rather than an add-on after go-live.
Start with one data flow

Ready to put typed contracts around your data?

Start with one data flow, in a Python service or an LLM pipeline, and see the contracts run on your data before the full build.

Book a free consultationRead case studies
From the blog

Reading on Pydantic and validation

All articles
Agentic AI

One Dashboard, Full Visibility: Why We Use Logfire in Our Stack

When people ask us about Logfire, they usually think AI agents. Monitoring PydanticAI workflows, tracking token usage, debugging LLM calls.
Agentic AI

Production-Ready Template for AI/LLM Applications: Full-Stack-AI-Agent + Next.js + 20+ Integrations

JWT auth, WebSocket streaming, migrations and Logfire observability, set up once. The open-source FastAPI and Next.js template behind ten projects.
Inside Vstorm

Official announcement of Vstorm and Pydantic partnership

From beta adoption to 30+ client deployments and upstream contributions: how Vstorm's partnership with Pydantic came about, and what it covers.