Both trace agents and score production spans. Fiddler fits enterprises that monitor traditional ML models as well as agents and want real-time content guardrails with on-premises deployment; Logfire fits developer teams that want self-serve plans, prompt versioning and a gateway alongside application traces. How you prefer to buy, through sales or by card, often decides it.[1][4]
Fiddler is a commercial platform for observability, evaluation and guardrails across classic ML models, LLM applications and agents, while Logfire is Pydantic's OpenTelemetry platform for tracing whole applications, with evals, prompt versioning and an AI gateway. Fiddler's guardrails score content in real time and it can run in a VPC or on-premises; Logfire has prompt versioning and its own gateway, which Fiddler does not. Both companies are independent.
| Decision factor | Fiddler | Logfire |
|---|---|---|
| Ownership | Independent; $30M Series C in January 2026 | Pydantic, independent; $12.5M Series A in 2024 |
| Scope | Classic ML models, LLM applications and agents | Whole application, with LLM and agent views |
| Prompt tooling | None; Prompt Specs are judge templates only | Prompt versioning with labels, plus a Prompt Playground |
| Run-time checks | Real-time content guardrails; inline blocking through a third-party gateway | Own gateway: flag, redact or block model calls; spend caps |
| Native SDKs | Python only; other languages via OpenTelemetry or REST | Python, JavaScript/TypeScript, Rust; any OTel SDK |
| Self-hosting | Enterprise: VPC or on-premises; SageMaker Partner AI App | Enterprise: Kubernetes via Helm |
| Free tier | Guardrails only, through a demo request | Personal: 10M records a month, 1 seat |
| Paid entry | Developer: $0.002 per trace, SaaS | Team: $49 a month, 10M records, 5 seats |
| Capability | Fiddler | Logfire | Prefactor |
|---|---|---|---|
| Observe | |||
| LLM call tracing | ✓ | ✓ | ✓ |
| Multi-step agent tracing (tool calls, nested spans) | ✓ | ✓ | ✓ |
| Cost tracking | Partial | ✓ | ✓ |
| 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 | Partial | ✓ |
| Prompt versioning and playground | — | ✓ | — |
| Act on findings | |||
| Runtime controls on live agent actions | Partial | Partial | ✓ |
| Gateway or proxy in the request path | — | ✓ | — |
| Licence and deployment | |||
| Open source | Partial | Partial | Partial |
| Self-hosting | ✓ | Partial | Partial |
Fiddler covers classic ML model monitoring, LLM applications and agents in one product. Its integration list includes Amazon SageMaker, Databricks, MLflow, Snowflake, BigQuery, Kafka and Airflow next to LangGraph, LangChain, Strands Agents and Google ADK. After a $30M Series C in January 2026 it repositioned around AI agents. Agent traces are hierarchical across agents, tools and model calls, through native Python SDKs for those four frameworks or a generic OpenTelemetry SDK for custom Python agents.
Logfire does not list classic ML model monitoring. Its scope is the running application: OpenTelemetry SDKs for Python, JavaScript and TypeScript, and Rust record HTTP requests, database queries, logs and metrics around each model and tool call, and its integrations run from Pydantic AI and the OpenAI and Anthropic SDKs to CrewAI, Mastra and n8n. If one team owns traditional ML models and agents, Fiddler's shared platform is the draw. If the agent is part of a web service, Logfire's single trace is.
Fiddler Guardrails score requests and responses in real time for harmful content across 11 dimensions, faithfulness, PII and PHI, and secrets, using its Centor models (renamed from Trust Models in May 2026). Your application acts on the score, or Fiddler blocks or redacts inline when it is plugged into a third-party gateway: Kong AI Gateway, AgentGateway, LiteLLM or Portkey, the last added with inline redaction in October 2026. Fiddler does not run a gateway of its own for application traffic.
Logfire runs its own gateway, part of Logfire since March 2026. Per-rule guardrails can observe, flag, redact or block prompts and completions containing secrets, personal data or policy violations, spend caps stop requests over budget, and routing groups add failover and load balancing. Both products check content in model traffic; neither documents approval steps or policies on an agent's tool calls.
Fiddler's Evals SDK runs experiments on datasets with 13 pre-built evaluators, custom evaluators and side-by-side comparison. Evaluator Rules score new production spans on Fiddler's side, with backfill and downsampling to limit judge cost, and judges can use Fiddler-hosted models such as Ministral 3 8B, generally available since September 2026, or your own provider keys. Span-level annotations have been in public preview since August 2026, with no review queue. There is no prompt versioning or playground.
Logfire's evals come from pydantic-evals: LLM judges with recorded reasoning, code scorers, datasets and experiments compared against a baseline. Live evaluations run in your application rather than on Logfire's servers. Run annotations are in beta. Prompt versioning, with immutable versions, labels and rollout targeting, plus a Prompt Playground that replays prompts from traces, covers ground Fiddler leaves to other tools.
Language coverage follows the same split. Fiddler's native agent SDKs are Python only, with other languages going through raw OpenTelemetry or the REST API, and its SDK drops Python 3.10 support on 27 October 2026. Logfire's SDKs cover Python, JavaScript and TypeScript, and Rust, though its eval and auto-instrumentation tooling is richest in Python.
As of 20 October 2026, Fiddler's free plan covers real-time guardrails only and starts with a demo request. The Developer plan is $0.002 per trace on SaaS and adds observability, experiments, custom evaluators, RBAC and SSO; at that rate, 100,000 traces a month costs $200. Enterprise is quote-based and adds VPC and on-premises deployment. Logfire Personal is free for 10M records and one seat, Team is $49 a month for five seats, Growth is $249 a month with unlimited seats, and extra records are $2 per million.
Fiddler counts traces; Logfire counts every span, log and metric record, so convert a typical week of your own traffic into both units before comparing. See Fiddler's pricing and Logfire'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. Fiddler's guardrails and Logfire's gateway both check content in model traffic; neither covers approvals or tool-call policies. Prefactor has no prompt playground or prompt registry and its platform is hosted, so if on-premises deployment or prompt iteration is the requirement, one of these two 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.