Confident AI
Cloud platform where engineering, QA and product teams evaluate, trace and red-team their LLM applications against 50+ research-backed metrics, then enforce one shared quality bar across every project through governance policies and CI/CD gates.
What is Confident AI?
Confident AI is a cloud platform that sets one quality bar for every large language model application an organisation ships. It is published by Confident AI, Inc., a Delaware company based in San Francisco, and comes from the team behind DeepEval, the open-source evaluation framework it describes as the most widely adopted in the world; DeepTeam is its red-teaming counterpart.
The product is organised in four modules. Evaluation brings more than fifty research-backed metrics — faithfulness, hallucination, relevancy, bias, toxicity, tool-selection accuracy, planning quality, conversational coherence — alongside G-Eval criteria written in natural language and deterministic code metrics. Scores can be statistically aligned with human annotations, so a team learns which metrics actually track human judgement. Observability captures every call as a nested trace, down to individual tool calls inside a multi-agent chain, with latency, token cost and metadata attached. Red teaming replays adversarial probes mapped to the OWASP Top 10 for Agentic Applications 2026, the OWASP LLM Top 10 and the NIST AI RMF, then scores each finding by CVSS. Governance turns internal standards into operational, runtime and pre-deployment controls, grouped into policies that re-evaluate on a schedule and report compliance per project and per owner.
What distinguishes it is who gets to use it. Applications connect over plain HTTP, in the manner of Postman, so product managers and QA leads run complete evaluation cycles without an engineer writing a script. Traces feed dataset curation automatically, multi-turn simulation generates test conversations, prompts are versioned with Git-style branches and merge gates, and required checks block a pull request when a metric drops.
Python and TypeScript SDKs, native OpenTelemetry ingestion and more than twenty integrations cover the usual stacks, and every capability is exposed as an API. On the enterprise side sit SOC 2 Type II and HIPAA compliance, custom RBAC and trace masking, a 99.9% uptime commitment, data residency in the United States or the European Union, and optional on-premises deployment on AWS, Azure or GCP. Customers named on the site include Panasonic, Toshiba, Samsung, BCG and Amdocs.
What it does
- Trace every LLM call, tool invocation and agent step in production, with inputs, outputs, latency and token cost attached
- Score applications against more than fifty research-backed metrics, including custom G-Eval criteria written in plain language
- Turn production traces into evaluation datasets automatically, with failures and edge cases categorised for you
- Simulate thousands of multi-turn conversations to test a chatbot before it ships
- Red-team an endpoint against OWASP and NIST AI RMF frameworks and export a CVSS-scored risk report
- Version prompts through Git-style branches and pull requests, gated by evaluation results
- Block a merge in CI when a quality metric falls below the threshold you set
When to use Confident AI / When not to
A quick filter to help you decide if Confident AI is the right fit.
When to use Confident AI
- Teams running several LLM applications at once that need one shared evaluation standard instead of a homemade eval stack per squad
- Product owners and QA leads who want to run full evaluation cycles themselves, over HTTP and without queuing behind engineering
- AI and platform engineers who need nested tracing, live alerting and CI/CD gates that block a merge when a metric falls under its threshold
- Regulated organisations in healthcare, insurance and finance that require SOC 2 Type II, HIPAA, EU or US data residency and shareable risk reports
- High-volume teams watching their observability bill, since tracing is billed at one dollar per GB-month with a retention window they choose
When not to use Confident AI
- Teams with a single, narrow evaluation need, for whom the publisher itself admits the breadth of the platform may be more than required
- Organisations that will only adopt fully open-source tooling, since the platform is closed and only DeepEval and DeepTeam are open
- Small budgets that cannot bridge the gap between the free tier and the 200 US dollars per month Starter plan
- Teams that must self-host or need HIPAA coverage but cannot reach an Enterprise contract, as both are reserved for that tier
- Anyone working outside LLM application development, and anyone under 13, who is excluded by the privacy policy
How to use Confident AI
A typical end-to-end flow, from setup to results.
- Create a free account on the platform; no credit card is required to start
- Connect your AI application by pointing at any HTTP or streaming endpoint, exactly as you would in Postman, or install the SDK with pip install deepeval
- Upload or build a golden dataset of test cases, or let the platform curate one from your production traces
- Choose the metrics that matter and set a passing threshold for each
- Run the dataset against your live application rather than a playground, then read the results side by side against a baseline
- Instrument production traffic through the SDK, or export OpenTelemetry traces to the platform's OTEL endpoint
- Set alerts on quality degradation, latency spikes or error rates, routed to email, Slack, Discord or Microsoft Teams
- Point the red-teaming module at the same endpoint, pick a security framework, then read the CVSS-scored report
- Version your prompts in Git-style branches and require passing evaluations before a merge
- Wire the evaluation suite into your CI pipeline so failing checks block the pull request
Pros & Cons
Pros
- One platform covers every evaluation use case — RAG, agents, chatbots, single-turn, multi-turn and safety — instead of three tools stitched together
- Product managers and QA leads own evaluation cycles themselves, which removes engineering as the bottleneck for every quality decision
- Tracing at one dollar per GB-month with retention you choose, presented as at least three times cheaper than the alternatives
- A permanent free plan with no credit card, and unlimited user seats from the Starter tier upward
- Metrics are open through DeepEval, so the scoring logic can be inspected rather than trusted blindly
- The enterprise posture is unusually complete for a seed-stage vendor: SOC 2 Type II, HIPAA, multi-region residency and on-premises deployment
- Native OpenTelemetry support and full trace-export APIs, backed by a contractual no-training clause on customer data
Cons
- The platform itself is closed source; only DeepEval and DeepTeam are open, a limit the publisher acknowledges
- Self-hosting and HIPAA coverage are both reserved for the Enterprise tier
- The pricing ladder jumps sharply, from nothing to 200 US dollars a month, then to 2,000
- Aggregated, anonymised data derived from customer data belongs to the publisher and may be shared with third parties, including other customers
- Third-party integrations are Python-only for now, and the announced alerting webhooks have not shipped yet
- No Article 27 EU representative is designated, despite the GDPR-compliant badge in the footer
- A seed-stage company with a small team, which is worth weighing before making it a critical dependency
Pricing & Plans
A permanent free plan is available at no cost, and no credit card is required to start. The cheapest paid tier, Starter, is priced at 200.00 USD per month, billed per organisation. Usage beyond the trace quota included in a plan is charged at one US dollar per GB-month ingested or retained, and online evaluations are billed by token consumption. An annual commitment carries a discount over monthly billing.
- full LLM unit and regression testing suite
- evaluations in development and CI/CD
- LLM tracing
- prompt versioning
- cloud datasets
- community and documentation support. Limited to 2 user seats
- 1 project
- 5 test runs per week and 1 GB-month of trace spans
- everything in Free plus no-code evaluation workflows
- custom metrics
- online evaluations and classification on live traffic
- annotation queues
- chat simulations
- downstream observability workflows
- real-time alerting and full project API access. Unlimited seats
- 5 projects
- everything in Starter plus metric and dataset versioning
- Git-based prompt workflows
- custom RBAC
- SOC2
- SSO and a dedicated support channel. Unlimited seats and projects
- 75 GB-months of trace spans
- a Team++ option adds custom contracting and custom SLAs
- everything in Team plus advanced AI authentication options
- an organisation management API
- dedicated on-premises deployment
- infosec review
- custom data residency such as Canada
- Australia or Japan
- HIPAA and dedicated 24x7 technical support. All limits removed
- an Enterprise++ option adds the AI red teaming and AI governance modules
Data, GDPR & hosting
A consolidated view of how Confident AI handles your data.
GDPR overview
Implementation is concrete rather than declarative. A GDPR-compliant badge sits in the footer, and a data processing addendum dated 15 September 2025 is published openly: Confident AI signs as processor, commits to article 32 measures and to assisting with articles 35 and 36. The privacy policy, last modified 26 May 2026, carries a dedicated EEA, United Kingdom and Switzerland section listing the legal basis for each purpose and the full set of rights, from access and portability to erasure and objection. Transfers to the United States and Singapore run under standard contractual clauses approved by the European Commission or the UK Information Commissioner, and a dated subprocessor list is published. Two gaps stand out: no Article 27 EU representative is named anywhere, and no data protection officer is designated.
Who owns the data?
Under the terms of service, the customer keeps all right, title and interest in its Customer Data, intellectual property included. Confident AI receives only a non-exclusive, royalty-free, worldwide licence to use that data as needed to deliver the service, and customers can export it at any time. One clause deserves attention: Aggregated Data, derived from customer data but aggregated and anonymised, belongs solely to Confident AI, which holds a perpetual and irrevocable licence over it and may make it available to third parties, including its other customers. On personal data, the publisher acts as controller for account information and as processor for Customer Data.
Reuse rights
The terms grant Confident AI only the rights it needs to run the service. Article 8.2 goes further and states that Customer Data will not be used to train, improve or develop any machine learning or artificial intelligence model without the customer's prior explicit written consent; any consented use would be governed by a separately negotiated written agreement. The homepage FAQ restates it plainly: customer data is never used to train models. Customers, for their part, may reuse and export their own data freely through the product's features and APIs, with native OpenTelemetry support offered as a guarantee against lock-in. The publisher states that it runs no online targeted advertising, does not sell personal data, and never shares User Content with third parties for marketing purposes. Anonymised Aggregated Data, however, may be shared.
Data retention & training
Hosting summary
At registration, customers choose where their data lives: the United States, in North Carolina, or the European Union, in Frankfurt. Enterprise contracts add custom residency, with Canada, Australia and Japan cited as examples. The privacy policy is broader, stating that data sits on servers in the United States or in any other country where Confident AI or its contractors maintain facilities, that the customer chooses the storage location, and that no transfer happens without prior notice. International transfers to the United States and Singapore are covered by standard contractual clauses approved by the European Commission or the UK Information Commissioner. The published subprocessor list gives processing locations service by service: AWS, Supabase and ClickHouse in the United States, Europe and Australia; Railway, OpenAI and Mixpanel in the United States; Stripe in the United States and Europe. DNS resolution points to an Amazon-operated address in the United States. Enterprise customers may instead deploy on their own premises, on AWS, Azure or GCP, and the platform offers project-level data separation, custom permissions and masking of LLM traces.
Things to keep in mind
Risks and trade-offs to weigh before adopting Confident AI.
- Aggregated Data derived from your inputs is owned outright by the publisher, under a perpetual and irrevocable licence, and may be shared with third parties including its other customers
- The no-training guarantee is contractual rather than a product switch: it rests on the terms holding, not on a toggle you control
- Usage-based billing can drift upward quietly, since traces are charged per GB-month ingested or retained and online evaluations consume tokens
- Fees are non-refundable, late payments carry 1.5% monthly interest, and access can be suspended after ten days of non-payment
- Protected health information requires a signed Business Associate Agreement and cardholder data requires prior written approval; using the service without them breaches the terms
- Automated quality scores can quietly replace judgement: a green dashboard measures what someone chose to measure, not whether the product is actually good
- The service is provided as is with warranties expressly disclaimed, by a seed-stage company, which matters once it becomes a critical dependency
Setup & Integrations
Technical difficulty
Two routes exist. The no-code route asks only for an HTTP endpoint and a dataset, which a product manager or QA lead can handle unaided. The engineering route installs the SDK with pip install deepeval and adds a few lines to log traces or run evaluations; the publisher claims under fifteen minutes for most teams. The real effort sits in what follows: choosing metrics, setting thresholds and wiring required checks into CI. Third-party integrations are Python-only today, with OpenTelemetry covering other stacks, and on-premises deployment is a separate, supported project.
Deployment
Integrations
Supported languages
Behind Confident AI
Fundraising
Social
Resources
All the official URLs gathered for verification and reference.
Alternatives
Tools that compete with or complement Confident AI.
Frequently asked questions
What is Confident AI?
How is Confident AI different from DeepEval?
Can I self-host it?
How long does it take to get started?
Is my data used to train models?
How does pricing scale with trace volume?
Where is my data hosted?
Which frameworks and alerting channels are supported?
Is there a minimum age?
Should you pick Confident AI?
Confident AI answers a problem that appears the moment an organisation ships more than one LLM application: every team builds its own evaluation stack, and nobody can compare anything. The platform's response is to turn evaluation, observability, red teaming and governance into a single shared standard and, more unusually, to make that standard usable by people who do not write code. Connecting an application over plain HTTP so a product manager can run an evaluation cycle unaided is the design decision that most separates it from tools built for engineers alone.
The technical foundation is credible. Metrics come from DeepEval, remain open to inspection, and can be statistically aligned against human annotations. Tracing is granular, priced at one dollar per GB-month with retention the customer chooses, and exportable through APIs and OpenTelemetry. The security posture — SOC 2 Type II, HIPAA, custom RBAC, trace masking, US or EU residency, optional on-premises deployment — is more complete than the company's seed stage would suggest.
The reservations are real but mostly ordinary. The platform is not open source, self-hosting and HIPAA sit behind an Enterprise contract, and the pricing ladder jumps from nothing to 200 US dollars a month and then to 2,000, which leaves smaller paying teams with an awkward step. Two points deserve a closer read: Aggregated Data derived from customer data belongs to the publisher and may be shared with third parties including its other customers, and no Article 27 EU representative is named despite the GDPR badge in the footer.
For a team running several LLM applications in production under a compliance obligation, it is a serious candidate. For a single narrow use case, the publisher itself concedes the platform may be more than needed.
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