Confident AI
Also known as: DeepEval
LLM evaluation and observability platform from the creators of DeepEval, with 50+ open source metrics for testing agents, RAG, and chatbots.
Confident AI is an LLM quality platform for evaluating, observing, and improving AI applications, built by the team behind DeepEval. DeepEval is its open source evaluation framework, often described as Pytest for LLMs, and it ships more than fifty research backed metrics for things like faithfulness, answer relevancy, hallucination, bias, toxicity, and task completion, with dedicated metrics for multi turn conversations and support for text, images, and audio. It runs locally or in CI/CD, works with any model provider, and is used by more than 150,000 developers and a majority of the Fortune 500.
Confident AI is the cloud platform that layers on top of DeepEval. It turns local test runs into a shared workflow with dataset management and version history, collaboration and commenting, regression testing against a last known good baseline, and dashboards that engineers, QA, and product managers can all read. A no code, HTTP based connection lets non engineers run evaluation cycles and tweak prompts without waiting on the engineering team, and a git based branching workflow gates prompt merges on eval results.
On the production side, every LLM call is captured as a trace with inputs, outputs, tool calls, latency, token cost, and metadata, with unlimited traces on all plans. Quality degradation triggers eval driven alerts, and failing production requests can be turned straight into test datasets, tightening the loop between monitoring and improvement. The platform also centralizes red teaming and safety workflows, simulates thousands of multi turn conversations in minutes to test behavior before release, and produces assessment reports for regulated buyers. It connects to coding agents like Cursor and Claude Code through an MCP server.
Confident AI is SOC2 compliant, stores data in the United States or the European Union, and supports project level data separation, custom permissions, and trace masking. It can run fully self hosted in a customer VPC or on premises in addition to the managed cloud. Pricing is public and self serve: DeepEval is free and open source, a free Confident AI tier covers small use, and paid plans start at $19.99 per seat per month plus $1 per gigabyte month of data. The company was founded in 2024 and raised a seed round in 2025.
Vendor details
Canonical URL
https://www.confident-ai.com
Category
Agent infrastructure
Subcategory
Evaluation and observability
Funding status
Founded 2024 by Jeffrey Ip (CEO) and Kritin Vongthongsri in San Francisco. Raised a $2.0M seed round in 2025. Builds the widely adopted open source DeepEval framework alongside the Confident AI cloud platform. Remains independent.
Company status
independent
Use cases & customers
Primary use cases
Target customers
Deployment options
Integrations
Model and framework agnostic. DeepEval plugs into OpenAI, OpenAI Agents, Anthropic, Azure OpenAI, LangChain, LangGraph, CrewAI, and Pydantic AI, runs in pytest and CI/CD, and emits OpenTelemetry. Confident AI adds an MCP server for running evals and pulling datasets from Cursor or Claude Code, plus no code HTTP based connections.
In practice
Your team keeps shipping prompt changes that quietly break edge cases. You write DeepEval tests in CI, and Confident AI flags the exact cases that regressed against your last good baseline before merge.
Your product managers want to test prompts but every eval cycle waits on an engineer. They run evaluations and tweak prompts themselves through a no code connection, while engineers keep owning the pipeline.
You need to prove your chatbot is safe before a regulated launch. You simulate thousands of multi turn conversations, run red teaming, and export a PDF assessment report for stakeholders.
Sources & related URLs
Related / legacy domains
Capability coverage
7.0 / 14 capabilities · 50%
| Integrations & Tool CallingBroad framework and provider integrations (OpenAI, OpenAI Agents, Anthropic, Azure OpenAI, LangChain, LangGraph, CrewAI, Pydantic AI), plus an MCP server and no code HTTP connections, but it does not provide agent tool calling itself. | Partial |
|---|---|
| Workflow OrchestrationOffers eval pipelines, CI/CD gating, and git based prompt branching, but it does not orchestrate agent runtime execution, sequencing, or routing. | Unable to verify |
| Knowledge Grounding & RAGEvaluates RAG pipelines with metrics like contextual recall, contextual precision, and faithfulness, but provides no retrieval or knowledge grounding layer of its own. | Unable to verify |
| Human Oversight & GuardrailsStrong human in the loop quality tooling: human annotation, commenting, comparison of metric scores against human labels, merge gating on eval results, and red teaming reports. Not a runtime guardrail layer that blocks agent actions in production. | Partial |
| Security, Identity & GovernanceSOC2 compliant with role based access, custom permissions, trace masking, project level data separation, and US or EU data residency, but enterprise SSO/SAML and certifications beyond SOC2 are not clearly documented. | Partial |
| Observability & AuditabilityCaptures every LLM call as a trace with inputs, outputs, tool calls, latency, token cost, and metadata, with unlimited traces on all plans, real time monitoring, dashboards, and eval driven alerts. | Full |
| Memory & State PersistencePersists traces and versioned datasets for observability and testing, but does not provide a runtime memory or state layer for agents. | Unable to verify |
| Deployment & Data ResidencyManaged cloud plus a fully self hosted option in a customer VPC or on premises, with data stored in the United States or the European Union. | Full |
| Prebuilt Agents, Templates & PacksShips more than fifty prebuilt research backed evaluation metrics, standard LLM benchmark suites, and a DeepEval skill for coding agents, but these are eval content rather than prebuilt agents. | Partial |
| Triggers & Channel CoverageEval driven alerts fire on quality degradation and evals run automatically in CI/CD on commits, but Confident AI provides no agent invocation channels or schedulers of its own. | Partial |
| Model Flexibility & RoutingModel and framework agnostic, working with any provider and letting teams choose the judge model, but it does not route production model traffic. | Partial |
| APIs, SDKs & MCP ExtensibilityOpen source Python SDK (DeepEval), an API, a dedicated MCP server usable from Cursor and Claude Code, pytest and OpenTelemetry support, and fully custom metrics in Python or via LLM as a judge. | Full |
| Testing, Debugging & OptimizationCore product. More than fifty research backed metrics, pytest native testing, regression testing against baselines, side by side version comparison, multi turn conversation simulation, and standard benchmark suites. | Full |
| Browser & Computer UseNot applicable. Confident AI is an evaluation and observability platform and does not provide browser automation or computer use. | Unable to verify |
Pricing
From $19.99/seat/mo · free tier + open source
Per seat per month plus $1 per GB-month of data ingested or retained
Included quota
Free tier includes 2 seats, 1 project, and 1 GB-month of data, with unlimited traces. Paid seats are $19.99 each per month and data is $1 per GB-month. Unlimited traces on all plans.
What is public
Confident AI publishes self serve pricing: DeepEval is free and open source, a Confident AI free tier covers small use, paid seats are $19.99 per month, and data is $1 per GB-month with unlimited traces on all plans.
Billing mechanics
Billing is per seat per month plus a usage charge of $1 per GB-month for data ingested or retained. The free tier includes 2 seats, 1 project, and 1 GB-month. Traces are unlimited on every plan, so cost is driven by seats and stored data rather than trace count.
Cost watchouts
Data retention adds $1 per GB-month, which can grow with high trace volume and long retention even though trace counts are unlimited.
Variable cost rationale
Costs are mostly per seat and predictable, traces are unlimited on all plans, and data is a low $1 per GB-month, so spend scales gently with team size and data volume rather than usage spikes.
Additional watchouts
Advanced features like role based access and custom dashboards sit on higher or enterprise plans. Self hosting is a separate deployment path.
Overage / add-ons
Data beyond the included 1 GB-month is billed at $1 per GB-month. Additional seats are $19.99 each per month.
Sales call required
Mixed (some tiers require a call)
Free / trial
DeepEval free and open source; Confident AI free tier (2 seats, 1 project, 1 GB-month)
Lowest paid plan
Seat based from $19.99/seat/mo
Commercial notes
DeepEval drives bottom up adoption among developers, and Confident AI converts teams that need collaboration, governance, and production monitoring. Role based access, custom dashboards, dedicated support, and self hosting are aimed at larger and regulated buyers.
Key ambiguities
Total cost depends on seat count and how much trace data is retained and for how long, and on whether a team needs enterprise or self hosted terms.
Cancellation / refund
Plans are self serve and can be upgraded or downgraded at any time. Detailed refund terms are not published.
Support SLA / resale
Community and standard support on lower tiers; dedicated support on higher and enterprise plans.
Missing data
Exact enterprise and self hosted pricing is not published and is arranged with sales. Per seat discounts at volume are not listed.
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