Confident AI
by Confident AI
Hosted LLM evaluation, observability and red teaming built on the open-source DeepEval framework
Confident AI is the hosted evaluation and observability platform built by the creators of DeepEval, the open-source pytest-style LLM testing framework. It gives engineering, QA and product teams research-backed metrics, production tracing, dataset auto-curation, red teaming and governance in one place, so LLM and agent quality is measured continuously in CI and in production rather than spot-checked by hand before a release.
Confident AI is the commercial cloud platform built by the creators of DeepEval, the open-source LLM evaluation framework that carries roughly 17.5k GitHub stars under an Apache 2.0 licence and, per Y Combinator's company profile, around 8 million monthly downloads. DeepEval structures evaluation like pytest: developers define test cases, attach metrics, and get pass/fail signals they can gate a build on. The library ships G-Eval and DAG custom metrics, RAG metrics (answer relevancy, faithfulness, contextual precision and recall), agentic metrics (task completion, tool correctness, plan adherence, step efficiency), multi-turn conversation metrics, and hallucination, bias and toxicity detectors, with documented integrations for LangChain, LangGraph, CrewAI, Pydantic AI, LlamaIndex, OpenAI, Anthropic, AWS AgentCore, Google ADK and OpenTelemetry. Confident AI wraps that library in hosted infrastructure: production tracing, dataset auto-curation that converts failing traces into regression suites, multi-turn chat simulation, Git-based prompt versioning with branching, real-time alerting, an Enterprise-tier red-teaming module that stress-tests against OWASP-style agentic risks, and a governance module for enforcing standards across teams. The company was founded in 2024 in San Francisco by Jeffrey Ip (CEO, previously an engineer at Google and Microsoft and the creator of DeepEval) and Kritin Vongthongsri, went through Y Combinator's Winter 2025 batch, and lists a team of about seven. It advertises more than 500 customers including Panasonic, Toshiba, Samsung, Epic Games, BCG and Amdocs, is SOC 2 Type II certified with HIPAA and GDPR support, US/EU data residency and an on-premise option on Enterprise, and competes directly with Braintrust, Langfuse, Arize, LangSmith, Opik and Ragas.
The engineering lead who already ships LLM features and now has to prove quality doesn't regress — DeepEval runs free in CI, and the hosted tier adds the tracing, datasets and sign-off trail a platform team needs.
A single set of metrics runs identically in local pytest, in CI and against production traces, so a quality regression fails a build instead of surfacing as a customer complaint.
At a Glance
- Category
- Developer Tools
- Pricing
- Freemium, Subscription, Usage-based, Open-source
- Target Market
- CTOs, Enterprise Developers, Data Scientists, QA Leads, AI Platform Teams
- Deployment
- Cloud-first, Open-source, Self-hosted
- Founded
- 2024
- Headquarters
- San Francisco, United States
- Team Size
- 1-10
- Customers
- 500+ (vendor claim)
Key Features
- ✓DeepEval open-source core
Apache 2.0 pytest-style framework with ~17.5k GitHub stars, so evaluations run locally and in CI without vendor lock-in
- ✓50+ research-backed metrics
G-Eval, DAG, RAG, agentic, multi-turn and safety metrics, all executable from the same test definitions the platform reports on
- ✓Production tracing and alerting
Traces live LLM and agent calls, scores them online and raises real-time alerts when output quality drops
- ✓Dataset auto-curation
Converts failing production traces into labelled evaluation datasets, so regression suites grow from real incidents rather than hand-written fixtures
- ✓AI red teaming module
Adversarially stress-tests deployed applications against agentic risk categories including prompt injection and data leakage; Enterprise tier only
- ✓Git-based prompt versioning
Branch, review and roll back prompts with the same workflow as code, available from the $2,000/month Team plan
- ✓Multi-turn chat simulation
Runs thousands of synthetic conversations against a chatbot to surface failures that single-turn evaluation never reaches
Capabilities
Use Cases
- •Regression-gating a prompt change
Run the same metric suite in CI on every pull request so a prompt or model swap that degrades faithfulness fails the build
- •Proving a RAG assistant is grounded
Score answer relevancy and faithfulness against retrieved context to quantify hallucination rate before a customer-facing rollout
- •Qualifying an agent for production
Measure tool-call correctness, step efficiency and task completion across a scripted scenario set to decide whether an agent is ready to ship
- •Continuous production quality monitoring
Trace live traffic, evaluate a sample online, and alert the on-call engineer when a quality metric drifts below threshold
- •Regulated-industry assurance evidence
Use the governance and red-teaming modules to produce documented, repeatable test evidence for healthcare or financial-services AI reviews
Ideal For
Best For
- ✓Gating CI/CD pipelines on LLM regression tests before a prompt or model change ships
- ✓Evaluating RAG pipelines on faithfulness, answer relevancy and contextual precision/recall
- ✓Testing multi-step agents on task completion, tool correctness and plan adherence
- ✓Turning failing production traces into curated regression datasets automatically
- ✓Adversarial red teaming of customer-facing assistants against prompt-injection and jailbreak patterns
Not Ideal For
- ✗Teams that need root-cause retrieval diagnostics — comparative reviews note DeepEval reports high-level pass/fail without always isolating whether the retriever or the generator caused a failure, which is where Ragas is stronger
- ✗Cost-sensitive, high-volume production monitoring: metrics are largely LLM-as-judge, so running several per example multiplies token spend, and the hosted plans meter trace ingestion at $1/GB-month beyond the tier allowance
- ✗Organisations that need self-hosting on a mid-tier budget — the open-source library is free, but on-premise deployment of the platform is gated behind the custom-priced Enterprise plan
- ✗Teams looking for an automated quality verdict they can trust unattended, given published evidence of systematic LLM-judge biases (position, verbosity, self-enhancement) across this whole tool category
Integrations
Deployment
Market & Ratings
500+ (vendor claim)
Market Analysis
Pros
- ✓Open-source core with a large installed base means the evaluation logic is inspectable and portable, not a black box
- ✓Unusually broad metric coverage for one tool — RAG, agentic, multi-turn and safety metrics in a single framework
- ✓Transparent published pricing with a genuinely usable free tier, plus SOC 2 Type II, HIPAA and EU data residency for regulated buyers
- ✓Deep integration surface (20+ frameworks and OpenTelemetry) means it drops into an existing stack rather than replacing it
Cons
- ✗Independent comparisons note DeepEval gives high-level pass/fail without always localising whether retrieval or generation caused the failure — weaker than Ragas for RAG debugging
- ✗LLM-as-judge metrics carry documented systematic biases (position, verbosity, self-enhancement) and published human-correlation figures for this metric family are modest, so scores need calibration rather than blind trust
- ✗Evaluation cost scales with metric count — running several judge metrics per example multiplies token spend on top of the platform's per-GB trace charge
- ✗Very small vendor (roughly seven employees, YC W25, no disclosed growth round), which is a procurement risk for a system placed in a release-gating path
- ✗Red teaming, governance and on-premise are Enterprise-gated, so mid-size teams get the observability half of the pitch and not the assurance half
Pricing
Free
$0
- ✓2 user seats
- ✓1 project
- ✓5 test runs per week
- ✓1 GB-month of trace spans
- ✓Unit and regression testing
Starter
From $200/mo
- ✓Unlimited seats
- ✓Up to 5 projects
- ✓5 GB-month traces then $1/GB-month
- ✓Custom metrics
- ✓No-code workflows
- ✓Real-time alerting
Team
From $2,000/mo
- ✓Unlimited projects
- ✓75 GB-month traces then $1/GB-month
- ✓Git-based prompt versioning
- ✓Custom RBAC and SSO
- ✓SOC 2
- ✓Dedicated support
Enterprise
Contact for pricing
- ✓Unlimited everything
- ✓On-premise deployment
- ✓AI red teaming module
- ✓AI governance module
- ✓HIPAA
- ✓Custom data residency
- ✓24/7 support
List pricing is published, which is unusual in this category: $0 free tier capped at 2 seats, 1 project and 5 test runs a week; $200/month Starter; $2,000/month Team. Seats are unlimited from Starter up, so the real meter is trace-span ingestion — 5 GB-month on Starter and 75 GB-month on Team, then $1 per GB-month, plus roughly $0.05/M input and $0.40/M output tokens for hosted judge models. SSO and RBAC land at Team; red teaming, AI governance, HIPAA, on-premise deployment and custom data residency are all Enterprise-only and custom-quoted. The DeepEval library itself is Apache 2.0 and free forever.
Security & Compliance
Connect
Sources
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