Confident AI
Unify LLM evaluation, observability, and red teaming in one shared workspace.
Confident AI is the only platform that packs evaluation, observability, and red teaming into one place—with strong governance hooks for regulated industries. Per-user pricing adds up, so start with the Free tier or open-source DeepEval if you're a small team.
- 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 a solo developer or small team looking for a free, simple eval framework—open-source DeepEval will serve you better without the overhead.
Trace span storage beyond the included allocation costs $1/GB-month, which can add up at high volume.
Confident AI's pricing suits mid-to-large enterprises with per-user plans starting at $9.99/user/mo (Starter). It's cheaper than LangSmith for tracing ($1/GB-month vs LangSmith's higher rates), but per-user costs can exceed competitors like Weights & Biases for large teams. The Free tier is generous for evaluation but limited in users and projects.
In short
Confident AI — Unify LLM evaluation, observability, and red teaming in one shared workspace. 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 $9.99/mo.
What's new in Confident AI
Checked todayAcross the latest 8 updates: 8 feature updates.
Classifier labels now have polarity; Flows page in beta; online evals with sampling; MCP servers as first-class connections.
Classifier polarity shows signal direction. Flows page traces agent tool/model calls. Online evals sample traffic. MCP servers become native connections.
Flows page beta; onboarding scans repo for tracing PR; custom skills; MCP servers; online evals sampling; trace flagging; granular report emails; Hugging Face on evals.
Flows page beta live. Auto tracing PR on onboarding. Custom skills teach AI agents. MCP servers as first-class connections. Online evals with traffic sampling. Traces flaggable in Observatory.
Statistical significance for test runs; full APIs for Dashboards, Red Teaming, Governance; Jira integration; AI Connections auto-setup.
Statistical significance for test runs. Dashboards, Red Teaming, Governance now have full APIs. Jira integration added. AI Connections self-configure.
Introducing Report Templates: Build the report your team actually reads
Report Templates let teams customize daily reports with traces, underperformance areas, usage patterns, and specific sections.
Introducing Synthetic Data Generation Pipelines: Customize how you generate data
Synthetic Data Generation Pipelines bring configurable data generation into Confident AI: select context sources and tune each generation step.
Introducing Annotation Forms: Capture any human feedback without leaving Confident AI
Annotation Forms define structured fields (text, scales, yes/no, multiple choice) for consistent human review feedback.
Introducing AI Observability Workflows: Custom automations for every trace on the platform
Workflows unify dataset ingestion, queue ingestion, eval rules, and classifiers into a single post-ingestion pipeline graph.
Introducing AI Governance: Standardized evals, policies, and controls
AI Governance layer enforces standardized evaluation policies and controls across teams, answering readiness at deploy time.
Viability Score
How likely is Confident AI to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 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
- AI red teaming against OWASP Top 10 for Agentic Applications
- Chat simulations for multi-turn bots
- Postman-like endpoint testing for non-engineers
- Quality alerting on monitored traces
- Auto-categorization of failures and edge cases
- AI Governance policy engine and compliance tracking (June 2026)
- AI Observability Workflows (June 2026)
- Report Templates (June 2026)
- Synthetic Data Generation Pipelines (June 2026)
- Annotation Forms (June 2026)
- PII leakage vulnerability scanning
- Jailbreaking and prompt injection testing
About Confident AI
Confident AI is an enterprise AI quality platform that brings together LLM evaluation, observability, and red teaming into a single workspace for product, QA, and engineering teams. Designed for industries where AI failures aren't an option—like healthcare, finance, and legal—it helps teams align on a single evaluation standard, catch regressions in production, and stress-test against adversarial attacks before shipping. The platform auto-curates evaluation datasets from production traces, letting you validate them with 40+ research-backed metrics like faithfulness and relevancy. Recent 2026 additions include AI Observability Workflows (unifying dataset ingestion and evaluation rules), AI Governance (enforcing eval signals as policies), Report Templates, Synthetic Data Generation Pipelines, and Annotation Forms. Key capabilities include LLM tracing with latency and cost tracking, OWASP Top 10 for Agentic Applications security testing, chat simulations for multi-turn bots, PII leakage scanning, jailbreaking and prompt injection testing, and a Postman-like endpoint tester. Compared to stitching together separate tools like LangSmith, Weights & Biases, and custom red teaming scripts, Confident AI delivers a single pane of glass for eval, monitoring, and security. It's built for large teams needing governance and compliance, though per-user pricing escalates.
Behind the Verdict
Confident AI shines when your organization has multiple AI products and needs a single source of truth for quality. The auto-curation of traces into datasets is a real time-saver, and the OWASP Top 10 for agentic apps is ahead of most competitors. The recent AI Governance module makes it easier to enforce policies across teams, which is a big deal for compliance-heavy sectors. Where it bites: per-user pricing can get expensive fast—Starter is $9.99/user/mo, and Team is custom but likely higher. If you're a solo developer or a tiny team, open-source DeepEval (from the same company) is a better fit. Compared to LangSmith, Confident AI offers built-in red teaming and governance, but LangSmith may have deeper LangChain integration. For teams already on Weights & Biases and happy with just monitoring, Confident's all-in-one approach might feel redundant. In practice, the chat simulations and Postman-like endpoint tester empower non-engineers to run evaluations, which reduces engineering bottlenecks. Just be mindful of trace storage costs—$1/GB-month is cheap, but it adds up at scale.
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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.
You need to catch regressions before shipping a new chatbot feature.
Outcome: Create a dataset from production traces, run automated evals in CI/CD, and get alerts when faithfulness drops below threshold.
You want to compare two prompt versions without engineering help.
Outcome: Use the no-code eval runner to test both prompts side-by-side, review auto-generated reports, and pick the winner.
You need to audit an agentic AI for OWASP Top 10 vulnerabilities.
Outcome: Run the red teaming module with pre-built attack vectors, identify goal hijack or tool misuse risks, and generate compliance reports.
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 2026-07-06
Limitations
- Free tier limited to 2 users, 1 project, 5 test runs per week, and 1-week data retention.
- Trace span storage overage at $1/GB-month beyond included allocation.
- Online eval metric runs metered beyond free monthly allowance.
- Advanced features like chat simulations, no-code workflows, and auto-categorization gated behind Starter and Team plans.
- Red teaming capabilities only in Enterprise plan.
- Per-user pricing can escalate for larger teams.
- Requires learning the DeepEval ecosystem.
as of 2026-07-02
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
$0/mo
Ideal for
Solo developers or small teams exploring LLM evaluation with limited needs.
What this tier adds
Starting tier with basic evals, tracing, and prompt versioning; limited to 2 users and 1 project.
Starter
$9.99/user/mo
Ideal for
Individuals or small teams needing cloud datasets and human annotation.
What this tier adds
Adds cloud datasets, custom metrics, online evals, human annotation, and chat simulations.
Team
Custom
Ideal for
Growing teams requiring scalability, integrations, and governance features.
What this tier adds
Unlimited projects; adds no-code workflows, alert integrations, annotation queues, versioning, and SOC2/SSO.
Enterprise
Custom
Ideal for
Large organizations needing high security, compliance, and advanced modules.
What this tier adds
Adds on-prem deployment, HIPAA, custom SLAs, AI red teaming, and AI governance modules.
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 suits mid-to-large enterprises with per-user plans starting at $9.99/user/mo (Starter). It's cheaper than LangSmith for tracing ($1/GB-month vs LangSmith's higher rates), but per-user costs can exceed competitors like Weights & Biases for large teams. The Free tier is generous for evaluation but limited in users and projects.
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: instrument your app with OpenTelemetry in about 30 minutes using the SDK. QAs can begin running evals on existing datasets within an hour. PMs can use the no-code endpoint tester immediately after setup. Full production observability may take a day to configure.
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 your traces via OTEL and import into Confident AI's tracing pipeline.
- →From custom eval scripts: Replace with Confident AI's Python SDK (DeepEval) for research-backed metrics.
- →From Weights & Biases: Migrate your datasets via CSV/JSON upload and recreate your eval workflows in Confident AI.
- ↗To open-source DeepEval: Export datasets and metrics, then run locally with the open-source framework.
- ↗To LangSmith: Use OTEL-compatible tools to redirect traces.
- ↗To custom monitoring: Export trace data via API for integration with your own dashboard.
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.
Official links
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Common stack mates teams adopt alongside Confident AI, with the specific reason each pairing earns its keep.
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