Confident AI pricing, free plan and limits

Last updated: 2026-10-15

Confident AI is an LLM evaluation and observability platform built by the team behind DeepEval, an open-source framework with 18.9k GitHub stars. It traces production calls, scores them with metrics, and turns failing traces into regression tests that run before each release. The vendor offers self-hosting and lists SOC 2 Type II.

About Confident AI

Confident AI is a platform for testing and monitoring applications built on large language models (LLMs). It is made by Confident AI Inc. of San Francisco, which joined Y Combinator in the Winter 2025 batch. The company comes from the team behind DeepEval, an open-source evaluation framework that GitHub shows at 18.9k stars and 2.1k forks. The hosted platform takes the metrics from that library and wraps a shared workspace around them.

You instrument an app with the Python or TypeScript SDK, and each call is stored as a tree of agent, tool and LLM spans. Evaluation metrics then score those traces, either in test runs during development and CI/CD or on live production traffic. A failing trace can become a test case or a new scoring metric, so the same mistake is checked on every later release. Annotation queues let product managers and domain experts rate traces next to engineers, which replaces the spreadsheet handoff many teams still use.

The DeepEval README lists tracing integrations for the OpenAI and Anthropic SDKs, LangChain, LlamaIndex, CrewAI, Pydantic AI, the Google Agent Development Kit, AWS AgentCore, Strands and Mastra. Because the metrics are model-agnostic, you pick the judge model yourself, including one from OpenAI, Anthropic or Google DeepMind.

On HokAI it sits next to other evaluation and agent-quality tools. Openlayer leans toward governance checks and guardrails, Archal tests agents against sandboxed copies of services such as GitHub and Slack, Kashikoi runs no-code agent simulations, and Maitai watches live LLM traffic and corrects output in real time. Confident AI's own angle is the open-source test library plus the loop from a bad production trace to a regression test. Regulated teams can ask the vendor about its SOC 2 Type II report and HIPAA and GDPR coverage, and the Enterprise plan offers self-hosting. A free plan exists, and paid plans are listed in the pricing details below.

Pricing

Free costs $0. Starter is $200 a month and Team is $2,000 a month, per the vendor's pricing page; Enterprise is custom priced. Trace storage past each plan's included amount is billed at $1 per GB-month ingested or retained, and the page's rule of thumb is about 100K traces per GB.

The page also lists startup grants for YC companies and for startups that have raised less than $5M. Checked on 2026-10-15.

Key Features

• LLM unit and regression testing: Pytest-style test cases run during development and in CI/CD, and the build fails when a score drops below a threshold you set.
• Production tracing: Records each LLM call as a trace of agent, tool and model spans with inputs, outputs, latency and token cost.
• Online evals and alerts: Scores live traffic against your metrics and raises real-time alerts when quality falls below a threshold.
• Trace-to-test loop: Turns a flagged production trace into a dataset row or a new metric so the failure is checked on later releases.
• Annotation queues: Lets product managers and subject-matter experts rate traces next to engineers, with ratings rolled into weekly trend views.
• Chat simulations: Simulates multi-turn conversations against a chatbot to test its behaviour before launch.
• Self-hosted deployment: Runs on the vendor cloud, or in your own VPC or on-prem infrastructure on the Enterprise plan, keeping data inside your network.
• Red teaming and governance modules: The Enterprise ++ tier on the pricing page lists an AI red teaming module and an AI governance module.

Pros

• Its metrics come from DeepEval, an open-source library you can run locally without the platform, which lowers lock-in compared with closed evaluation tools.
• Non-engineers can review and label traces in the same workspace, which removes the spreadsheet handoff many teams use for expert feedback.
• The vendor lists SOC 2 Type II alongside HIPAA and GDPR support, next to a self-hosted option, so regulated teams have a route through security review.
• The free plan includes tracing and the full testing suite, so a team can check fit on a real workflow before talking to sales.

Cons

• The free plan caps test runs per week and drops trace spans past its storage limit, so it suits trying the tool rather than running a team on it.
• SOC 2, single sign-on and custom roles sit on the Team tier, so a small regulated team pays for the whole bundle to get them.
• Only the DeepEval library is open source; the platform is hosted software, and self-hosting needs the Enterprise plan.
• Storage is metered by the GB-month, so forecasting cost means estimating trace volume; the vendor suggests about 100K traces per GB as a rule of thumb.

Visit Confident AI Official Website