Both trace agents, run offline and online evals, version prompts and pair the platform with an AI gateway. Logfire is full-stack observability from the Pydantic team, strongest for Python and Pydantic AI teams; Maxim AI is built around pre-release agent simulation and evaluation shared by product and engineering, including voice. Where you need visibility, the whole application or the agent alone, usually decides it.[1][4]
Logfire traces your whole application on OpenTelemetry, with LLM and agent calls next to HTTP and database spans, and adds evals, prompt versioning and an AI gateway; Maxim AI focuses on simulating and evaluating agents before release, including voice agents, and sells Bifrost, an open-source gateway, alongside. Logfire prices by records ingested; Maxim prices per seat with log caps. Both can block model traffic only through their gateways.
| Decision factor | Logfire | Maxim AI |
|---|---|---|
| Company | Pydantic, independent; $12.5M Series A led by Sequoia in 2024 | Maxim AI (H3 Labs Inc.), independent; $3M seed led by Elevation Capital in 2024 |
| Tracing scope | Whole application: HTTP, database, LLM and agent spans on OpenTelemetry | Agents and LLM calls: traces, spans, generations, tool calls, sessions |
| Simulation | Not documented | Multi-turn text and voice agent simulation |
| Online evals | Run in your application via pydantic-evals, scored after the call | Evaluates production logs at session, trace and node level, from Professional upwards |
| Human review | Partial: run annotations in beta; queues in early access | Human evaluators and annotation queues, including external raters |
| Gateway and guardrails | AI gateway in Logfire with observe, flag, redact or block rules and spend caps | Bifrost gateway (Apache-2.0); guardrails are a Bifrost Enterprise feature |
| Self-hosting | Enterprise only, on Kubernetes | Enterprise in-VPC for the platform; Bifrost open source self-hosts freely |
| First paid tier | Team: $49 a month, 10M records, 5 seats | Professional: $29 per seat a month, 100k logs |
| Capability | Logfire | Maxim AI | Prefactor |
|---|---|---|---|
| Observe | |||
| LLM call tracing | ✓ | ✓ | ✓ |
| Multi-step agent tracing (tool calls, nested spans) | ✓ | ✓ | ✓ |
| Cost tracking | ✓ | ✓ | ✓ |
| OpenTelemetry support | ✓ | ✓ | ✓ |
| Evaluate | |||
| Offline evals (datasets, experiments) | ✓ | ✓ | ✓ |
| Online evals on production traffic | ✓ | ✓ | ✓ |
| LLM-as-a-judge scoring | ✓ | ✓ | ✓ |
| Code-based evaluators | ✓ | ✓ | ✓ |
| Human review and annotation | Partial | ✓ | ✓ |
| Prompt versioning and playground | ✓ | ✓ | — |
| Act on findings | |||
| Runtime controls on live agent actions | Partial | ✓ | ✓ |
| Gateway or proxy in the request path | ✓ | ✓ | — |
| Licence and deployment | |||
| Open source | Partial | Partial | Partial |
| Self-hosting | Partial | ✓ | Partial |
Logfire is an observability platform first. It is built on OpenTelemetry, so the same trace holds your web requests, database queries, retrieval steps and model and tool calls, with dedicated Agents and LLMs views on top. Its SDKs cover Python, JavaScript/TypeScript and Rust, and any standard OTel SDK can send data. Python teams get the most from it, and Pydantic AI users get the tightest fit, since the same company builds both.
Maxim AI starts from the agent and from testing before release. Its platform runs test sets over prompts, no-code agents and agents behind an HTTP endpoint, simulates multi-turn conversations in text and voice, and schedules runs or triggers them from GitHub Actions. Product and engineering staff share the same evaluator library, human rating queues and prompt deployment rules. It traces production too, and ingests OTLP, but its documentation centres on agent and LLM spans rather than the rest of your application.
If the question you need answered is why a request was slow or failed somewhere in your stack, Logfire answers more of it. If the question is whether a new agent version handles a hundred simulated customers correctly, Maxim is built for that.
The evaluator libraries overlap. Logfire's pydantic-evals offers an LLM judge that records its reasoning with each score, and plain code scorers. Maxim offers pre-built and custom AI evaluators, programmatic evaluators in JavaScript or Python, statistical evaluators and evaluators that call an external API.
Both vendors ship a gateway, and in both cases that gateway is the only place anything is blocked. Logfire's AI gateway, part of Logfire since March 2026, routes calls to OpenAI, Anthropic, Google, Bedrock and others, applies guardrail rules that observe, flag, redact or block content such as secrets and personal data, and enforces hard spend caps. Its live evaluations score calls after they run and cannot alter outputs.
Maxim's Bifrost is an Apache-2.0 gateway that self-hosts via Docker, Kubernetes or a Go binary, with failover, caching, virtual keys and an MCP gateway. Its guardrails also check MCP tool-call arguments and results and can block, redact or transform content, but the pricing page lists guardrails as a Bifrost Enterprise feature. The Maxim evaluation platform itself sends alerts and does not block live actions. Neither vendor documents approval steps for agent actions outside model traffic.
Logfire's release notes show steady 2026 changes, including the gateway merger in March, guardrails and spend caps in July and a Python SDK release on 13 October. Its human review is less mature: run annotations are in beta and annotation queues are early access.
Maxim's public product changelog shows no dated 2026 entries, and its newest monthly update post covers December 2025, which makes recent platform changes hard to verify. Its homepage and main pricing page now lead with Bifrost, with the evaluation platform moved under /evals. Bifrost itself is active, with a release on 19 October 2026. Ask Maxim directly about the evaluation platform's roadmap if it matters to your decision.
The units differ. As of 20 October 2026, Logfire bills by records (spans, logs and metrics): Personal is free for 10M records a month and one seat, Team is $49 a month with 10M records and five seats, Growth is $249 a month with unlimited seats, and extra records cost $2 per million on paid plans. Maxim bills per seat with log caps: Developer is free for up to three seats and 10k logs a month with 3-day retention, Professional is $29 per seat a month with 100k logs, Business is $49 per seat a month with 500k logs, and overage is $1 per 10k logs. Self-hosting is Enterprise at both, though Bifrost's open-source gateway is free.
Large teams with modest traffic may find per-record pricing cheaper; small teams with heavy traffic may find the reverse. Model both on Logfire's pricing and Maxim AI's pricing.
We sell the layer this section describes. Read it with that in mind.
Stated plainly, since Prefactor publishes this page: Prefactor is an agent evaluation platform that scores each step of a run and acts on what it finds through runtime policies on tool calls and data access, approval holds for risky actions, and a kill switch for a run, an agent or a team. Logfire and Maxim AI can both block content, but only for traffic routed through their gateways, and neither documents approval holds. Prefactor has no prompt playground or prompt registry, no gateway, and its platform is hosted, so for prompt versioning or gateway routing either vendor here is the better fit. See the full comparison of 15 platforms.
Reviewed against public product and documentation pages on October 20, 2026. If a vendor has changed a feature, product name, or positioning since then, send a correction and we will update it. Numbered source links in the page body point to the ordered sources below.
Prefactor scores each step of every agent run and can hold or stop an action when a score says it should not happen. Start on the free tier: 5,000 spans a month, no credit card required.
Prefactor helps teams observe, evaluate, and improve their AI agents in production, across every framework and provider.