Confident AI
Enterprise LLM evaluation, observability, and red teaming in one platform.
Confident AI is the rare all-in-one for LLM quality: evals, tracing, adversarial testing, and governance hooks for regulated industries. Its pricing ladder rewards scale, so small teams should start free or with open-source DeepEval. If you need compliance and centralized control, it's worth the climb.
Verified 3d ago · liveness 87/100 · cite: rightaichoice.com/tools/confident-ai
- Enterprise teams deploying multiple LLM products needing consistent quality standards
- Industries with high compliance requirements (healthcare, finance, legal)
- Product managers who want to run evaluations without engineering dependencies
- QA teams needing to automate regression testing on LLM behavior
- Individual developers or small projects needing a quick eval framework (use open-source DeepEval instead)
- Teams already heavily invested in LangSmith or Weights & Biases who don't need red teaming
- Use cases requiring only basic monitoring without governance or red teaming features
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Skip Confident AI if you're an individual developer or small team needing a lightweight eval framework without governance; open-source DeepEval might be enough.
Going past 5 GB-months of trace spans on Starter adds $1 per GB-month, which can escalate if you retain long.
Confident AI's pricing scales from free to enterprise, with transparent per-GB storage costs. It's 3x cheaper than alternatives for tracing, but for smaller teams, open-source DeepEval is far cheaper.
In short
Confident AI — Enterprise LLM evaluation, observability, and red teaming in one platform. Best for Enterprise teams deploying multiple LLM products needing consistent quality standards, Industries with high compliance requirements (healthcare, finance, legal), Product managers who want to run evaluations without engineering dependencies. Free to start; paid plans from $200/mo.
What's new in Confident AI
Checked 3 days agoAcross the latest 5 updates: 5 feature updates.
Test Runs Overview, Personas Beta, Audit Log Export, and More
Test runs get a new overview page with failing topics and metric aggregates. Personas enter beta, full audit log export API added, MCP context on AI connections, model catalog expands.
Introducing Report Templates: Build the report your team actually reads
Report Templates let teams customize generated reports to focus on specific traces, performance, and usage patterns.
Introducing Synthetic Data Generation Pipelines
Synthetic Data Generation Pipelines bring control over data generation into Confident AI, with custom sources and tuning.
Introducing Annotation Forms: Capture any human feedback without leaving Confident AI
Annotation Forms let teams define custom fields for structured human feedback within the platform.
Introducing AI Observability Workflows: Custom automations for every trace
Workflows unify dataset ingestion, queue ingestion, evaluation rules, and classifiers in one interface.
Viability Score
How well maintained and how widely used is Confident AI? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: August 2026
How we score →Key Features
- LLM evaluation with 40+ research-backed metrics
- LLM tracing with latency and cost tracking
- Auto-curation of evaluation datasets from production traces
- OWASP Top 10 for Agentic Applications security testing (2026)
- Chat simulations for multi-turn bots
- Postman-like endpoint testing for non-engineers
- Quality alerting on monitored traces with priority filters
- AI Observability Workflows for post-ingestion automation
- Report Templates for customizable daily reports
- Annotation Forms for structured human feedback
- Synthetic Data Generation Pipelines
- PII leakage vulnerability scanning
- Jailbreaking and prompt injection testing
- Code vulnerability scanning in red teaming
- JSONL trace export
About Confident AI
Confident AI is an enterprise AI quality platform that unifies LLM evaluation, observability, red teaming, and governance in one shared workspace for product, QA, and engineering teams. It's built for industries where AI failures aren't an option—healthcare, finance, legal—where perfectly functional AI isn't good enough; it has to be safe. The platform standardizes how teams turn live traces into test cases, validate with evals, and catch vulnerabilities before they ship, aligning every team to the same quality bar. The platform covers the full AI lifecycle. For evaluation, it benchmarks LLM systems with 40+ research-backed metrics, including single-turn and multi-turn DeepEval metrics, custom G-Eval criteria, and code-based metrics. For observability, it traces every LLM call with latency and cost tracking, auto-curates evaluation datasets from production traces, and alerts on regressions. For security, it stress-tests against adversarial attacks, including OWASP Top 10 for Agentic Applications 2026, PII leakage scanning, jailbreaking, prompt injection, and code vulnerability scanning. Recent launches expand its governance and workflow capabilities. AI Observability Workflows unify dataset ingestion, evaluation rules, and classifiers into a programmable post-ingestion graph. AI Governance enforces eval signals as policies. Report Templates let teams build daily reports with traces and underperformance data. Annotation Forms support structured human feedback with fields like text, numbers, scales, and choices. Synthetic data generation and chat simulations for multi-turn bots round out the platform. Compared to stitching together separate tools like LangSmith, Weights & Biases, and custom red teaming scripts, Confident AI offers a single pane of glass. Its pricing scales from a free tier to enterprise, with transparent per-GB trace storage costs. It's designed for large teams needing governance and compliance, though per-user seats and volume pricing can add up.
Behind the Verdict
Confident AI is built for teams where AI quality is non-negotiable. Its biggest strength is consolidating evals, observability, and red teaming into a single pane of glass—eliminating the patchwork of LangSmith, Weights & Biases, and custom scripts. The 40+ metrics, multi-turn eval support, and CI/CD integration are solid for engineering teams, while PMs and QAs get no-code workflows and annotation queues. The OWASP Top 10 for Agentic Applications 2026 testing is a differentiator for security-conscious enterprises. The pricing is transparent, but the free tier's limits (2 seats, 1 project, 5 test runs/week) make it a trial, not a production option. For regulated industries, HIPAA, custom data residency, and on-prem deployment on Enterprise are compelling. However, small teams or individual devs may find the ramp steep—open-source DeepEval covers basic needs. If you need centralized governance and compliance, Confident AI is worth the investment.
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Real-world workflow fit
Concrete scenarios for the personas Confident AI actually fits — and what changes day-one when you adopt it.
Set up CI/CD integration to run evals on every pull request, catching regressions before merge.
Outcome: Reduced broken deployments and faster release cycles with automated quality gates.
Use no-code eval workflows to evaluate live API endpoints without engineering help.
Outcome: Empowers PMs to iterate on AI features independently, cutting improvement cycles from 10 days to hours.
Run red teaming against the OWASP Top 10 for Agentic Applications 2026 to identify vulnerabilities.
Outcome: Proactively mitigated risks and met compliance requirements before external audit.
Use Cases
- Evaluate LLM responses in CI/CD to catch regressions before deploying to production.
- Trace end-to-end AI agent executions to debug failures and monitor latency and token usage.
- Automatically generate evaluation datasets from existing documents in Google Drive, SharePoint, Notion, or S3.
- Run scheduled evals weekly to ensure AI quality remains consistent across updates.
- Auto-categorize production traces to identify drift in user requests and response quality.
- Stress-test AI applications against adversarial attacks using OWASP Top 10 for Agentic Applications 2026.
- Enforce AI quality policies and compliance tracking across teams using AI Governance.
Models Under the Hood
as of 2026-08-30
Limitations
- The free tier is limited to 2 user seats, 1 project, 5 test runs per week with additional test runs locked, and 1 GB-month of trace spans after which additional spans are dropped.
- Starter ($200/month) includes unlimited user seats, 5 projects, and 5 GB-months of trace spans, with overage at $1 per GB-month and token costs varying by model.
- Team ($2,000/month) offers unlimited user seats and projects with 75 GB-months of trace spans, while Enterprise is custom.
- Full Project API access is available from Starter, and On-Prem Deployment is available in Enterprise.
as of 2026-08-30
Verification history
We have re-verified Confident AI 16 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 16 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Confident AI tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free Forever
$0/mo
Ideal for
Solo developers or small teams exploring LLM evaluation and tracing with minimal needs (2 seats, 1 project, 5 test runs/week).
What this tier adds
Starting tier with full testing suite and tracing, but limited to 2 seats, 1 project, and 5 test runs per week.
Starter
$200/mo
Ideal for
Growing product teams ready to launch reliable AI, needing no-code workflows, custom metrics, and real-time alerting.
What this tier adds
Adds no-code eval workflows, custom metrics, online evals, annotation queues, chat simulations, and full Project API access, with unlimited seats.
Team
$2,000/mo
Ideal for
Organizations scaling AI quality across multiple teams, needing versioning, RBAC, and SOC2 SSO.
What this tier adds
Adds metric & dataset versioning, Git-based prompt workflows, custom RBAC, SOC2 SSO, dedicated support, and 75 GB-months of trace spans.
Enterprise
Custom
Ideal for
Large regulated enterprises needing HIPAA, custom data residency, on-prem deployment, and advanced governance.
What this tier adds
Adds advanced AI authentication, org management API, on-prem deployment, infosec review, custom data residency, HIPAA, and 24x7 support.
Where the pricing makes sense
The company stage and team size where Confident AI's pricing actually pencils out — and where peers do it cheaper.
Confident AI's pricing scales from free to enterprise, with transparent per-GB storage costs. It's 3x cheaper than alternatives for tracing, but for smaller teams, open-source DeepEval is far cheaper.
Setup time & first value
How long it actually takes to get something useful out of Confident AI — broken out by persona, not the marketing-page minute.
For engineers, tracing setup is ~15 minutes with SDK or OTEL. Evals in CI/CD can be live in an hour. PMs using no-code workflows can start evals within a day, with autocurated datasets from live traffic.
Switching to or from Confident AI
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From LangSmith: export traces via API and import into Confident AI using their dataset connectors.
- →From Weights & Biases: use Confident AI's SDK to log evals and traces, then retire wandb sweeps.
- →From custom scripts: use Confident AI's API or SDK to replace ad-hoc eval code.
- ↗To DeepEval: export your datasets and metrics as code, since DeepEval is the open-source core of Confident AI.
- ↗To LangSmith: use Confident AI's JSONL trace export to migrate traces.
Integrations
Resources & Guides
- Documentationconfident-ai.com
Introduction
Get started with Confident AI for LLM evaluation and observability
- Resourceconfident-ai.com
Confident AI Blog - Resources to help teams stay confident in AI
Join our weekly newsletter to stay confident in the AI systems you build. Our articles include tutorials, guides, and essays to safely build and evaluate LLMs.
- Resourceconfident-ai.com
Knowledge Base
Explore our knowledge base to learn about LLM evaluation, observability, and AI reliability.
- Documentationconfident-ai.com
Setup and Installation
Quick setup guide for Confident AI
- Documentationconfident-ai.com
Introduction to LLM Evaluation
Learn how to evaluate AI applications using Confident AI's code-driven or no-code workflows.
- Documentationconfident-ai.com
Introduction to LLM Tracing
Learn about LLM tracing with Confident AI
- Documentationconfident-ai.com
Introduction to Red Teaming
Get started with Confident AI's Red Teaming for AI safety and security assessment
- Documentationconfident-ai.com
Introduction
Welcome to Confident AI's Evals API reference.
Tutorials & Learning
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Frequently Asked Questions
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