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Confident AI
LLM evaluation and observability platform built on DeepEval, the open-source testing framework, for regression tests, tracing, and red teaming.
Teams shipping LLM apps and agents that already use or want DeepEval and need CI evals, tracing, and annotation in one workspace.
Teams that want only a free hosted tool for heavy testing, or enterprises that need SOC 2 on the lowest paid plan.
Verdict
Confident AI is an LLM evaluation company founded by Jeffrey Ip and Kritin Vongthongsri and backed by Y Combinator's winter 2025 batch. Teams use DeepEval, its open-source framework, to write evaluation metrics that run like unit tests, and the hosted platform to run regression tests in CI, trace production traffic, run online evals, and route bad answers to annotators. It matters because agent quality slips quietly between releases, and Confident AI lets engineers and product managers catch the slip together.
Score Breakdown
How Confident AI scores in the categories that matter to its buyers.
Pricing
Confident AI publishes monthly plans. Trace storage beyond the included amount costs $1 per GB-month.
| Plan | Price | What stands out |
|---|---|---|
| Free | $0 | 2 user seats, 1 project, 5 test runs per week, 1 GB-month of traces |
| Starter | $200 / month | Unlimited seats, 5 projects, online evals, annotation queues, 5 GB-months |
| Team | $2,000 / month | Unlimited projects, metric versioning, custom RBAC, SOC 2, 75 GB-months |
| Enterprise | Custom | On-prem, data residency, HIPAA, 24x7 support |
The Field at a Glance
How Confident AI compares with other Eval & quality vendors we reviewed, by Overall Score and relative typical engagement cost.
Confident AI scores 7.7 in Eval & quality, under Braintrust (7.8), and ahead of Vellum (7.0) and Patronus AI (6.6). Relative cost lands mid-band for this subcategory.
Use-case matrix
| Use case | Fit | Notes |
|---|---|---|
| Unit-test style evals | Strong | DeepEval metrics run in development and CI. |
| Product managers running evals | Strong | Finom cut an improvement cycle from 10 days to three hours. |
| Annotation with engineers | Strong | Humach annotators work in the same workspace. |
| Enterprise governance across many apps | Strong | RLDatix uses it to standardize evals across initiatives. |
| Heavy testing on the free plan | Weak | Free is limited to 5 test runs a week. |
| SOC 2 on a small budget | Weak | SOC 2 is listed from the $2,000 Team plan. |
Who it’s for
Good fit
- Teams already using DeepEval
- Product-led AI teams with PMs in the loop
- Enterprises standardizing evals across apps
Poor fit
- Hobby projects needing many free runs
- Teams that want a pure APM tool
- Buyers who need SOC 2 for $200 a month
Review Excerpts
Below are excerpts from public reviews. Our team scoured public reviews, forums, and chat rooms to get a balanced view of customers' experience with this company. Paid reviews and pay-for-play sites such as Clutch were excluded.
“We hit a point where every AI team was building their own eval stack. That’s fine for one product. With five, ten, fifteen AI initiatives across the portfolio, it’s never going to live up to our high standards of AI governance.”
“Before Confident AI, a single improvement cycle took 10 days. I'd create a task, assign it to an engineer, wait for availability, and go back and forth. Now the same cycle takes three hours, and our product managers can run it themselves.”
“Our annotators can now work directly in Confident AI alongside engineers. That means no more CSVs, no more scattered spreadsheets, just one centralized workflow where everyone contributes.”
The free plan allows 5 test runs a week and locks additional runs, and SOC 2 appears only from the $2,000-a-month Team plan.
Methodology
This page is an independent evaluation of Confident AI for buyers comparing options in eval & quality. AI Industry Reviews accepts no sponsorships, advertising, or pay-for-placement fees. Confident AI did not pay for this review.
What we scored
The headline number is an Overall Score on a 0-10 scale. Eight criteria sit under it in two groups.
- Buyer outcomes (for eval & quality)
- Open-source eval depth
- Regression testing in CI
- Tracing and cost
- Free plan headroom
- Company & commercial
- Innovation & product leadership
- Project management & communication
- Pricing
- Contract fairness
Pricing measures whether the price looks fair for the value delivered, including packaging and renewal friction that show up in real buying cycles.
Score Composition
| Input | Weight | What it covers |
|---|---|---|
| Reviews | 40% | A proprietary read of what practitioners say about likes, complaints, and day-to-day use, including public review sites, forums, and private chat rooms. Paid reviews and pay-for-play sites such as Clutch are out of scope. |
| Product | 35% | Hands-on look at screens and workflows. |
| Pricing | 15% | Whether the price looks fair for what you get. |
| Docs & training | 10% | Docs, tutorials, and training material. |
How we balanced the evidence
The Overall Score is the simple average of the eight criteria. Recommendation language follows that score and the fit pattern described above.
Scope
Confident AI is graded here as eval & quality. Criteria scores can move as more review volume and product checks are added.