Confident AI

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Introduction

Confident AI is the commercial platform built by the team behind DeepEval, the open-source LLM evaluation framework, and DeepTeam, its open-source red-teaming framework. Where DeepEval gives engineers metrics in code, Confident AI adds a shared workspace so engineers, product managers, QA teams and domain experts can work from the same evals and the same quality bar.

The platform spans the AI quality lifecycle. You benchmark LLM systems with research-backed metrics, trace and monitor production systems with alerts (for example when a RAG pipeline’s faithfulness drops below a threshold), and turn observability traces into evaluation datasets automatically, with failures and edge cases auto-categorised. Engineers can gate LLM changes with eval coverage in CI, while product managers run experiments and edit datasets in the UI without waiting on engineering.

Coverage is broad across kinds of AI. The site lists voice agents (evaluated call by call through Confident Voice), RAG pipelines, MCP and multi-agent systems on the same evals. Conversation simulation generates thousands of multi-turn conversations in about ten minutes to test chatbots before release. Red teaming stress-tests apps against adversarial attacks and produces PDF-ready assessment reports for regulated industries. Prompts can be managed with a git-based branching workflow synced to your codebase, with merges gated on eval results.

For enterprises, Confident AI offers SSO, on-prem or in-your-cloud deployment, data residency across several regions, governance policies and SOC 2. Every part of the product is exposed as an API, so prompts, datasets, traces and projects can be automated.

Its pricing stands out for tracing: the site claims tracing from $1 per GB-month, at least three times cheaper than alternatives, with adjustable retention.

Treat LLM evaluation like any other test suite. Start with a small, curated dataset of real inputs and expected qualities, run it on every prompt or model change, and grow the dataset whenever production traces reveal a new failure. Confident AI’s value is in keeping those datasets, metrics and results in one place so changes can be compared over time.

Share results with product owners too, since they often know which failures matter most.

A useful rule of thumb is to keep evaluation metrics few and well understood. Two or three metrics that the team trusts, such as faithfulness to retrieved context and task completion, beat a dashboard of twenty scores nobody reads.

Launch and background: Y Combinator’s company directory lists Confident AI in its Winter 2025 batch, with the one-liner “Production Monitoring & Evals for AI Agents”. It was launched on Hacker News on 20 February 2025 (“Launch HN: Confident AI (YC W25)”).

Features

1. Research-backed metrics: Benchmark LLM systems with DeepEval’s metrics.
2. CI eval gates: Ship LLM changes with eval coverage in CI.
3. Production tracing: Monitor quality and latency with alerts.
4. Trace-to-dataset: Turn traces into eval datasets automatically.
5. Conversation simulation: Thousands of multi-turn conversations before release.
6. Voice AI evals: Score voice agents call by call.
7. Red teaming: Adversarial testing with shareable PDF reports.
8. Annotation: Domain experts review outputs in plain language.
9. Git-based prompt management: Branching prompts gated by eval results.
10. Enterprise controls: SSO, on-prem, data residency, governance and SOC 2.

Pros

1. Open-source roots: Built on DeepEval and DeepTeam, which you can use for free.
2. Whole-team workflow: Non-engineers can run experiments and annotate.
3. Cheap tracing: Usage pricing per GB-month.

Cons

1. Breadth to learn: Many modules to configure.
2. Paid tiers jump: Team-level plans start at $200 and $2,000 a month.
3. Hosted by default: Self-hosting is an enterprise option.

Usage and Links (Example)

Start with DeepEval in code, then send results to Confident AI:

pip install deepeval
deepeval login          # connect to your Confident AI project
deepeval test run test_chatbot.py

Practical tips:

1. Write a handful of metrics for your riskiest behaviour first, such as faithfulness for RAG.
2. Turn production traces into datasets weekly so evals track real usage.
3. Gate prompt merges on eval results once the metrics are stable.

Resources:

• Confident AI website

Pricing

Confident AI lists a forever-free plan, a $200 a month plan and a $2,000 a month plan, each with tracing billed at $1 per GB-month ingested or retained; enterprise options are custom.

Recommended for?

1. Teams already using DeepEval that want dashboards, tracing and collaboration.
2. Regulated companies needing red-teaming reports and governance.
3. Voice and chat agent teams that want one eval stack for both.

Conclusion

Confident AI is one of the most complete hosted eval platforms, and its DeepEval foundation means your metrics are portable.

Start on the free tier with DeepEval tests in CI and add tracing and simulation once the basics are in place.

On CheckOps, Confident AI is filed under the Release & CI gates and Production monitoring & observability stages. Related tools listed here include DeepEval, DeepTeam, LangSmith, Braintrust, Langfuse and Arize Phoenix. A short trial on a real project, with your own code, data and pipeline, is usually the fastest way to choose between them, because fit matters more than feature lists. Facts here come from the official site, documentation and repository, checked on 11 November 2026.