PydanticAI gives you a clean, type-safe way to write agents in Python; it does not give you the system that runs them. With a framework you assemble the parts and operate the result — availability, durable state, governance, and the pager are yours. With a platform the system comes assembled and operated, under a contractual SLA.
PydanticAI is a typed Python agent library from the team behind Pydantic. PydanticAI gives you model-agnostic agents, structured and validated outputs, dependency injection, MCP support, and graph-based workflows, all checked by your type checker. PydanticAI's pieces are components for writing agents. Your engineers wire in the durability, streaming, and governance around them.
Then your team runs it — scaling it, handling failover and recovery, and carrying the pager. The framework publishes no application-level SLA, so the availability of the running system is yours to guarantee.
Akka gives you the agents, durable state, streaming, APIs, and governance as one pre-integrated runtime. You describe the system you want, and the platform generates it, tests it, and runs it. Because everything runs in one runtime, a system that compiles is ready for production.
Akka keeps it running around the clock, with active-active replication and automatic failover, and stands behind a contractual 99.9999% availability SLA on the running system.
Akka Specify builds agentic systems from your specifications and releases only what meets your definition of done.
| Dimension | With PydanticAI | With Akka |
|---|---|---|
| Availability | The framework publishes no availability SLA, because it does not run anything — uptime and recovery belong to the infrastructure your team assembles. A contractual SLA is offered only on the Logfire Enterprise observability service, not on your agents. | Akka owns 99.9999% on the running system — a ~31-second yearly budget — active-active, sub-1-minute RTO, zero-byte RPO. |
| State & memory | Pydantic AI Harness can save each completed step of a run to a store you choose, so the run can resume or fork. Crash recovery requires one of eight external engines — Temporal, DBOS, Prefect, Restate, AWS Lambda, Absurd, pgtask, or Apache Airflow — that your team runs or buys separately. | Held in memory, active-active replicated — 4 ms reads, sub-10 ms writes. Durability is a runtime property, not a database round-trip. |
| Governance | Pydantic AI Harness guardrails block or redact a prompt before the model call and a tool call before it runs, and can hold a tool call for human approval. Logfire traces each run and scores a sample of live traffic. | Akka Verify applies any control to any AI system and guarantees its enforcement on every action — HITL, HOTL, guardrails, evaluations — with every call recorded in tamper-evident storage. |
| Spec-driven delivery | Agent Specs define an agent's model, instructions, and capabilities in a YAML or JSON file. Pydantic Evals scores an agent against a dataset of test cases, offline or on live traffic through Logfire. | Akka Specify builds agentic systems from your specifications and releases only what meets your definition of done. |
| Model optimization | Pydantic AI Gateway fails over between providers by priority, splits traffic by weight, and caps spend per project, user, and API key. Logfire proposes prompt changes from production traces, and a person accepts each one. | Akka Optimize puts each request on the best model for the task, including smaller models trained on your data. |
| Runtime track record | PydanticAI reached version 1.0 in September 2025 and version 2.0 in June 2026, with 2.61 released in October 2026. PydanticAI delegates the durable runtime to external engines. | since 2009, 100,000+ deployments — the runtime behind systems two billion people use daily. |
| Infrastructure cost | The Python infrastructure footprint, plus the separate datastore and operations of the durable-execution engine, plus the memory, streaming, API, observability, and governance tiers — each provisioned and operated separately. | One shared-compute runtime carries the same volume on up to 90% less infrastructure, at a fixed annual fee. Akka Optimize keeps improving the models on your own production traffic, so cost per task keeps falling as the system runs. |
| Portability | A production deployment accretes dependencies on the Logfire observability service, offered hosted, single-tenant, or self-hosted on Enterprise, and on whichever durable-execution engine and datastore provide recovery. | Any cloud, on-prem, or sovereign — portable specs, BSL licensing, no per-trace meter. |
With PydanticAI, engineers write guardrails into each agent's Python code. Those guardrails run inside PydanticAI agents only.
Block, approve & trace
Pydantic AI Harness adds guardrails that block or redact a prompt before the model sees it, block a tool call before it runs, and hold a tool call for human approval. Engineers write each guardrail into the agent's Python code. Pydantic Logfire traces every run and scores live traffic with online evaluations, with SOC 2 Type II, HIPAA, and GDPR for the observability service and an EU data region.
The guardrails cover agents built with PydanticAI. Pydantic maps its controls to no AI regulation such as the EU AI Act. Logfire keeps traces for 30 days on Team and up to 90 days on Growth, with longer retention on Enterprise.
Block & escalate
Risk teams govern every agentic system without waiting on its developers, and every control they apply is guaranteed to be enforced. Risk teams choose from 1,718 controls drawn from 241 regulations and frameworks. Human-in-the-loop approvals, human-on-the-loop halt switches, sanitizers, guardrails, evaluations, testing gates, and simulations all run before an action executes and block or escalate anything that does not pass.
Every call is traced automatically into petabyte-scale, tamper-evident storage, and that same trace data feeds continuous evaluations that keep running in production, right alongside your agents.
Reach for the framework when you are building agents in Python and want your type checker and IDE to catch errors at write-time, when the system is internal or low-stakes enough that some downtime is fine, and when your team is ready to own the durability, scaling, recovery, and governance that come with running it yourself. Assembling the components yourself is also the right call when you want low-level control over each one.
Reach for the platform when the system has to stay up, survive failure, and recover on its own, and when it has to pass an audit and enforce governance while it runs. Akka fits when you want a single vendor accountable for availability under an SLA, and when you would rather ship the system than build and operate the plumbing underneath it.
The Akka Agentic AI Platform builds, runs, governs and optimizes agentic systems under a contractual SLA. Tell us what you are building, and we will show you how it runs in production.
