Confident AI Review (2026)

The pricing changed and the internet has not caught up

If you take one thing from this page, take this.

Confident AI now bills flat per organisation. Starter is $200 a month with unlimited user seats and 5 GB-months. Team is $2,000 a month with 75 GB-months. The free tier is 2 seats, 1 project and 1 GB-month.

It previously used per-seat pricing, and third-party reviews still cite it that way - $19.99 per seat per month in some places, $49.99 per user in others, even a $9.99 per user tier. Those numbers are stale, and it is not just the amount that changed, it is the shape of the model.

If you are comparison shopping from review sites or directories, you are working from outdated information. Treat the vendor’s own pricing page as authoritative.

Plan	Price	Seats	Included data
Free	$0	2	1 GB-month, 1 project
Starter	$200/mo per org	Unlimited	5 GB-months
Team	$2,000/mo per org	Unlimited	75 GB-months, SOC 2 / HIPAA / SSO
Enterprise	Custom	Custom	Self-hosted option

Traces are unlimited on all plans, with usage priced at $1 per GB-month.

Who the new model favours

The crossover is not subtle and it is worth working out.

At roughly $20 a seat under the old model, ten engineers cost $200 and twenty cost $400. Under flat pricing, twenty engineers still cost $200. Fifty engineers still cost $200.

So the change is clearly favourable for larger teams, and it compares very well against per-seat competitors - LangSmith’s seat-based model is the obvious contrast, and Arize AX’s decision not to meter seats is the closest analogue.

For a two or three person team it is worse than the old per-seat pricing, and the free tier is where you would start instead.

What is unusual here is that the pricing rewards scale rather than punishing it. Most of this category makes it more expensive to give more people access to quality data, which is a strange incentive for tooling whose whole purpose is making quality visible.

GB-months, and the verbosity problem

Beyond the flat fee you pay for data - $1 per GB-month, with 5 included on Starter and 75 on Team.

Because traces are unlimited, you are not metered on event count the way Datadog and Arize AX meter spans. That is a meaningful advantage for agent workloads, which generate enormous span counts and get punished badly by per-span billing.

The trade-off is the one W&B Weave has: you are metered on volume, so verbose traces cost more than terse ones at identical traffic. A RAG application logging large retrieved-context blocks will burn GB-months far faster than a classification endpoint making the same number of calls.

Measure your average trace payload, not your request count.

What you are actually buying over DeepEval

Worth being clear, because DeepEval is Apache 2.0 and free for any purpose. Metrics, custom metrics and CI integration all work with no account.

Confident AI is the cloud layer: collaboration, dataset management, tracing, real-time monitoring, dashboards, online evaluation against production traffic, and human annotation.

The honest test is whether your problem is measurement or coordination. If you need better metrics, DeepEval already has them and you should not pay anything. If you need several people sharing datasets, reviewing outputs, and tracking production quality over time, that coordination layer is the product.

That is a clean open-core split. The free framework is genuinely complete rather than a funnel, which is more than can be said for vendors who withhold production monitoring from their open-source build.

Self-hosting exists, which is rare

A fully self-hosted deployment option is offered alongside managed cloud.

This matters more than it might seem. Most commercial platforms in this category cannot be self-hosted at all - Datadog, HoneyHive and Braintrust are cloud-only, and Logfire gates it behind Enterprise. Combined with an Apache-2.0 framework underneath, Confident AI gives teams with data residency requirements an unusually good answer.

Pricing and terms for self-hosting are not published, so expect an enterprise conversation.

The gap between tiers

The main structural criticism: $200 to $2,000 is a 10x step with nothing in between.

It is softened by the $1 per GB-month overage rate - a team exceeding Starter’s 5 GB-months can pay incrementally rather than jumping tiers, so it is less of a cliff than the headline suggests. Work out where overage stops being cheaper than the Team plan, because that is your real decision point rather than the tier boundary.

Should you use it?

Use Confident AI if you already use DeepEval and need shared datasets, persistence, online evaluation and collaboration, and your team is big enough that unlimited seats beats per-seat competitors.

Don’t use it if you are a small team where DeepEval alone suffices, or your traces are large and your budget is tight.

Bottom line: a well-structured commercial layer over a genuinely free framework, with pricing that recently changed in a direction that favours larger teams. Verify the current numbers on the vendor’s own page rather than any review site, including this one - this is exactly the field that goes stale first, and we have documented a pricing change that most of the category’s coverage has missed.

---

Pricing verified against the vendor’s own pricing page on 31 July 2026, following a shift from per-seat to flat per-organisation billing. Conflicting per-seat figures widely published by third parties are stale and are flagged as such. This is a researched directory entry - we have not yet instrumented this platform with our reference application.