Both run offline and online evals, trace agents, queue traces for human review and simulate users. Confident AI starts from DeepEval, an open-source framework engineers run in code and CI; Maxim AI starts from a hosted workspace where product and engineering run simulations and test runs together, with a gateway alongside. Who writes the evals on your team tends to decide it.[1][4]
Confident AI is built around DeepEval, an Apache-2.0 evaluation framework you run in code and CI, with a hosted platform on top; Maxim AI is a hosted platform for simulating, evaluating and tracing agents, aimed at product and engineering teams working together. Both simulate users, including voice. Confident AI charges per organisation with unlimited seats from $200 a month; Maxim AI charges per seat from $29 a month.
| Decision factor | Confident AI | Maxim AI |
|---|---|---|
| Ownership | Independent; Y Combinator Winter 2025 company | Independent; $3M seed led by Elevation Capital in 2024 |
| Open source | DeepEval and confident-trace (Apache-2.0); platform proprietary | Bifrost gateway and SDKs (Apache-2.0); platform proprietary |
| Starting point | Code: DeepEval test cases with 40+ metrics, locally and in CI | Workspace: simulations and test runs via UI or SDK |
| Simulation | Journeys (beta, October 2026); voice via Pipecat, LiveKit and SIP | Multi-turn text and voice simulation |
| Gateway | None; integrates with LiteLLM, Portkey, OpenRouter, Bifrost and others | Bifrost, its own Apache-2.0 gateway |
| Runtime guardrails | None; threshold checks run on recorded data over 24 hours | Through Bifrost Enterprise guardrails on inputs, outputs and MCP tool calls |
| Free tier | 2 seats, 1 GB-month of traces, 5 test runs a week | Up to 3 seats, 10k logs a month, 3-day retention |
| First paid tier | Starter: $200 a month per organisation, unlimited seats | Professional: $29 per seat a month, 100k logs |
| Capability | Confident AI | 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 | ✓ | ✓ | ✓ |
| Prompt versioning and playground | ✓ | ✓ | — |
| Act on findings | |||
| Runtime controls on live agent actions | — | ✓ | ✓ |
| Gateway or proxy in the request path | — | ✓ | — |
| Licence and deployment | |||
| Open source | Partial | Partial | Partial |
| Self-hosting | Partial | ✓ | Partial |
Confident AI grows out of DeepEval, an Apache-2.0 framework for Python and TypeScript with more than 40 metrics covering agents, multi-turn conversations, RAG, MCP and multimodal output. Engineers write test cases, run them locally and gate CI on the results. The hosted platform stores test runs, datasets and regression reports, and adds online evals, annotation queues and red teaming. DeepEval on its own has no UI or persistence, so teams that want history, dashboards and review queues need the platform.
Maxim AI is designed to be used from its UI or SDK by product and engineering together. Test runs cover prompts, no-code agents and agents behind an HTTP endpoint, with scheduled runs and a GitHub Actions integration. Evaluators can be pre-built, LLM judges with a chosen model and scale, JavaScript or Python code, or calls to your own API. Human evaluators get rating guidelines, and since January 2026 external raters can be added by email.
If your evals live in a test suite and you want an open framework you can keep using without a vendor, Confident AI fits. If the people judging quality include non-engineers, Maxim AI's workspace is closer to how they work.
Both now simulate users. Maxim AI documents multi-turn text and voice simulation as a core feature and lists integrations with LiveKit, ElevenLabs and the OpenAI Realtime API, plus simulation for AWS Bedrock Agents and Glean agents. Confident AI added Journeys in October 2026, in beta, for simulating user paths, along with voice connections through Pipecat, LiveKit and SIP.
Recency cuts the other way on the platforms overall. Confident AI's changelog shows a steady run of releases through September and October 2026, including a V2 API and the open-source confident-trace SDK. Maxim AI's public changelog shows no dated 2026 entries, and its newest monthly update post covers December 2025, so its recent changes are harder to verify.
Confident AI has no gateway of its own and integrates with third-party ones for tracing, Bifrost among them. Its run-time controls, part of its AI compliance module, assess recorded production data against thresholds over a trailing 24-hour window and report pass or fail; they do not block live actions. Online evals during tracing run referenceless metrics only.
Maxim AI's Bifrost is an OpenAI-compatible gateway with failover, caching, virtual keys, budgets and an MCP gateway. Its guardrails check inputs, outputs and MCP tool-call arguments and results and can block, redact or transform content, but they are listed as a Bifrost Enterprise feature.
Self-hosting the platforms follows the same Enterprise line. Confident AI ships private container images that need a signed licence key and deploys to AWS, GCP or Azure Kubernetes with Terraform and Helm. Maxim AI deploys in your VPC on the same three clouds, either fully isolated or as a data plane peered to its hosted control plane. The open-source parts, DeepEval and Bifrost, run anywhere without a contract.
As of 20 October 2026, Confident AI's Free tier covers 2 seats, 1 project, 1 GB-month of trace data and 5 test runs a week. Starter is $200 a month per organisation with unlimited seats and 5 GB-months, and Team is $2,000 a month with 75 GB-months and SSO; beyond the allowance it charges $1 per GB-month. Maxim AI's Developer tier is free for up to 3 seats and 10k logs a month with 3-day retention; Professional is $29 per seat a month and Business $49 per seat a month, with overage at $1 per 10k logs.
Per-organisation pricing rewards larger teams; per-seat pricing rewards small ones. Self-hosting either platform is Enterprise and custom. Check Confident AI's pricing and Maxim AI's pricing against your team size and trace volume.
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, approval holds and a kill switch scoped to a run, an agent or a team. Confident AI's checks run on recorded data after the fact; Maxim AI can block content through Bifrost Enterprise guardrails, but documents no approval holds for agent actions. Prefactor has no prompt playground or prompt registry, its platform is hosted, so for a code-first metric library Confident AI is the better pick. 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.