Both trace agents, run experiments on datasets and score production spans automatically. Confident AI is the stronger fit for the build-and-test loop: metrics in code, prompt versioning, annotation and red teaming; Fiddler is the fit for real-time content guardrails and for teams that also monitor classic ML models. Decide which half of the problem you are buying for.[1][4]
Confident AI is an evaluation-first platform for LLM apps and agents, built around the open-source DeepEval framework; Fiddler is a commercial monitoring, evaluation and guardrails platform that also covers classic ML models. Fiddler scores content in real time so your application or an AI gateway can block it; Confident AI does not act during a run. Confident AI has prompt versioning, annotation queues and a self-serve paid plan, which Fiddler does not.
| Decision factor | Confident AI | Fiddler |
|---|---|---|
| Ownership | Independent; Y Combinator Winter 2025 company | Independent; $30M Series C in January 2026 |
| Scope | LLM apps and agents | Classic ML models, LLM apps and agents |
| Real-time guardrails | None; threshold checks run on recorded data | Partial: content scoring for your app to block on, or inline via a third-party gateway |
| Prompt versioning | Yes: Prompt Studio with commits, versions and labels | None documented |
| Human review | Annotation queues, multi-reviewer forms, feedback API | Span annotations in public preview; no review queue |
| Native SDKs | Python and TypeScript | Python; other languages through OpenTelemetry or REST |
| Self-hosting | Enterprise only, on your AWS, GCP or Azure Kubernetes | Enterprise: VPC or on-premises; also a SageMaker Partner AI App |
| Entry pricing | Free tier, then Starter at $200 a month per organisation | Developer plan at $0.002 per trace; free plan is guardrails only, by demo request |
| Capability | Confident AI | Fiddler | 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 | ✓ |
| 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 |
The two products start from opposite ends. Confident AI grew out of DeepEval, an Apache-2.0 framework with more than 40 metrics for agents, multi-turn conversations, RAG and MCP. Its hosted platform adds test-run history, regression reports, online evals on traces, annotation queues, Prompt Studio, red teaming and conversation simulation. The weight is on finding problems before and after release.
Fiddler grew out of model monitoring and since January 2026 has positioned itself as a control plane for AI agents. Its distinctive part is Fiddler Guardrails: real-time scoring of requests and responses for harmful content, faithfulness, personal and health data, and secrets, using its own Centor Models. It also offers experiments through its Evals SDK with 13 pre-built evaluators, and evaluator rules that score new production spans.
Fiddler's judges can run on models it hosts, such as a Ministral 3 8B model made generally available in September 2026, or on your own provider keys. DeepEval's metrics use any LLM you choose as the judge.
Fiddler can act during a run, within limits. Its guardrails score content in real time for your application to block on, and when Fiddler is plugged into a third-party gateway such as Kong, AgentGateway, LiteLLM or Portkey it can block or redact inline without code changes. The scope is content on requests and responses; there is no human approval step or tool-call policy in the docs.
Confident AI documents no inline blocking. Its threshold checks assess recorded production data over a trailing 24-hour window and report pass or fail, and its red-teaming detections flag traces after ingestion. If a run-time content filter is a hard requirement, Fiddler covers it and Confident AI does not.
Fiddler has no prompt versioning or playground, and its human review is span-level annotation in public preview without a dedicated queue. Its native agent SDKs are Python only, for LangGraph, LangChain, Strands and Google ADK, with other languages going through OpenTelemetry or REST. Confident AI has prompt versioning and annotation queues, with Python and TypeScript SDKs and a long list of framework integrations.
The gap on Confident AI's side is breadth: it does not monitor classic ML models, and its online evals run only referenceless metrics during tracing. If one vendor for classic ML models and agents is the goal, Fiddler is built for that.
Fiddler's self-serve Developer plan is SaaS only. Its Enterprise plan adds VPC and on-premises deployment, and Fiddler can also run inside your own AWS account as an Amazon SageMaker Partner AI App. Its free tier and self-hosting both start with a conversation with sales. Confident AI hosts in the US or EU on every plan, offers custom data residency on request and self-hosts on Enterprise, on AWS, GCP or Azure Kubernetes with Terraform and Helm. It is also listed on AWS Marketplace.
Cost tracking is uneven on both. Fiddler tracks token usage across agent interactions and stores monetary cost only when the trace source sends cost attributes, such as LiteLLM's; an API for the cost of its own judges entered private preview in October 2026. Confident AI records token usage and cost on model spans and sums them per trace, inferring prices automatically for OpenAI, Anthropic and Gemini and using rates you set for other providers.
As of 20 October 2026, Fiddler's Developer plan is $0.002 per trace on SaaS and adds observability, experiments, custom evaluators, RBAC and SSO. Its free plan covers guardrails only and is reached through a demo request, and Enterprise, which adds VPC and on-premises deployment, is quote-based. Confident AI starts with a self-serve Free plan of 2 seats and 1 GB-month of trace data, then Starter at $200 a month per organisation with unlimited seats, and Team at $2,000 a month.
Fiddler scales with trace count; Confident AI with stored trace data. See Confident AI's pricing and Fiddler'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 check content on requests and responses, and Confident AI checks recorded data after the fact; neither documents approval holds or tool-call policies. Prefactor has no prompt playground or prompt registry, its platform is hosted, and it does not monitor classic ML models, so Confident AI or Fiddler may suit those needs better. 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.