AI visibility report
AI visibility report for Confident AI in LLM Observability Evals & Gateways.
Outside the top three on 13 of the 25 prompts buyers actually ask.
Braintrust is cited on 5 of those losses.
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Track Confident AI across these prompts daily.
Start free trialStill absent from 86.7% of tracked prompt responses
Top-3 citations across 75 prompt × platform pairs
Peer Ranking
Key Metrics
Platform Breakdown
Gemini Search
ChatGPT
PerplexityHow to read this. Confident AI appears in 13.3% of tracked prompt responses. Presence is absolute coverage; share of voice is relative citation share; sentiment measures tone only when the brand appears.
Where Confident AI is losing
Prompts where competitors are visible and Confident AI is not.
These prompt-level losses are the first prompts to track and repair.
Where Confident AI is winning3
Which observability tools include real-time alerting on quality drops, not just latency?
Avg # 1.0 · 1 platform
What LLM monitoring platforms integrate with PagerDuty, Slack, or Datadog for alerting workflows?
Avg # 2.0 · 1 platform
Which LLM observability platforms scale to billions of traces per month at enterprise volumes?
Avg # 2.0 · 1 platform
Where Confident AI is losing5
Which LLM observability tools work with OpenTelemetry so I don't have to add yet another vendor SDK?
Competitors on 3 platforms
Track this promptWhich AI observability platforms can be self-hosted with one command using Docker Compose?
Competitors on 2 platforms
Track this promptWhat AI eval platforms support on-premise or VPC deployment for regulated industries?
Competitors on 2 platforms
Track this promptWhich evaluation platforms let me convert development-time evals into production guardrails automatically?
Competitors on 2 platforms
Track this promptWhat's the fastest way to start tracing my LLM application calls without rewriting my code?
Competitors on 2 platforms
Track this prompt
Track Confident AI daily before the next report refresh.
Track these gapsResearch dossierCapabilities, use cases, sources, reviews, pricing, and FAQ
Overview
Confident AI is a Y Combinator-backed (W25) AI quality platform founded in 2024 and headquartered in San Francisco. Built by the creators of DeepEval — the open-source LLM evaluation framework with 14K+ GitHub stars and over 150K developers — Confident AI provides a unified cloud platform for engineering, QA, and product teams to evaluate, trace, and monitor LLM applications across the full development lifecycle. Core capabilities include 50+ research-backed evaluation metrics, production LLM tracing, automatic dataset curation from traces, multi-turn conversation simulation, CI/CD regression testing, git-based prompt versioning, and AI red teaming. The platform targets teams building RAG systems, agents, and chatbots in any framework, with enterprise-grade compliance (SOC 2 Type II, HIPAA, GDPR) and on-premises deployment for regulated industries. Trusted by 500+ AI companies including Panasonic, BCG, Samsung, and Epic Games.
Confident AI is the commercial cloud platform built atop DeepEval — the open-source LLM evaluation framework — providing an integrated workspace for LLM evaluation, observability, dataset management, prompt versioning, and AI red teaming. It enables engineering, QA, and product teams to benchmark, safeguard, and continuously improve LLM applications from prototyping through production.
Key Facts
- Founded
- 2024
- HQ
- San Francisco, USA
- Founders
- Jeffrey Ip, Kritin Vongthongsri
- Employees
- 1-10
- Funding
- ~$2M
- Customers
- 500+ AI companies
- Status
- Private
Target users
Key Capabilities10
- 50+ research-backed LLM evaluation metrics (G-Eval, hallucination, answer relevancy, faithfulness, contextual precision/recall, bias, toxicity, task completion, and more)
- Full-stack LLM tracing capturing inputs, outputs, tool calls, latency, token cost, and metadata
- CI/CD regression testing via DeepEval pytest-native integration
- Multi-turn conversation simulation for chatbot and agent testing
- Dataset auto-curation from production traces with automatic failure and edge-case categorization
- Git-based prompt versioning with branch, merge-permission, and eval-gated approval workflows
- AI red teaming and risk assessment reports via DeepTeam open-source framework
- No-code HTTP endpoint evaluation ('Postman for AI') enabling non-engineers to run evals
- Human-in-the-loop annotation and cross-team collaboration workflows
- Enterprise compliance: SOC 2 Type II, HIPAA, GDPR; multi-region data residency (US/EU); RBAC and data masking
Key Use Cases8
- RAG pipeline evaluation and quality benchmarking
- AI agent end-to-end quality assurance
- Multi-turn chatbot testing and simulation
- Pre-deployment regression testing in CI/CD pipelines
- Production LLM monitoring, alerting, and drift detection
- LLM red teaming and safety risk assessment for regulated industries
- Model and prompt A/B experimentation and comparison
- Cross-functional AI quality collaboration between engineering, QA, and product teams
Confident AI customer outcomes
200% faster speed to market; $100K+ engineering costs saved; 20+ annotators enabled in unified workspace
Humach adopted Confident AI to centralize multi-turn voice agent evaluation, annotation, and simulation workflows, replacing fragmented spreadsheet-based processes and eliminating the need to build a custom evaluation system.
Recent Trend
How AI describes Confident AI3
Confident AI (DeepEval-based): Specializes in in-production evaluations with real-time grading, tracing, experiments, and the ability to define custom metrics for your domain, integrating with multiple model providers.
Which LLM eval platforms support running automated evaluations on production traces with custom metrics?
### DeepEval (by Confident AI) * Deployment Model: Open-source framework (local/CI) with a self-hosted enterprise platform ( Confident AI ). * Why it fits: It operates like a "pytest" for AI models.
What AI eval platforms support on-premise or VPC deployment for regulated industries?
DeepEval (by Confident AI) ------------------------------ DeepEval is widely considered one of the easiest frameworks for local unit testing and agent evaluation.
I want to add eval tracking to my agent — which platforms have the simplest Python decorator-style integration?
Most cited sources8
- G7
confident-ai/deepeval: The LLM Evaluation Framework - GitHub
github.com·Documentation
3Best LLM Observability Platforms to Improve AI Product Reliability in 2026 - Confident AI
confident-ai.com·Home
- 3
10 LLM Observability Tools to Evaluate & Monitor AI in 2026 - Confident AI
confident-ai.com·Comparison
- 2
Top 6 AI Agent Observability Platforms for 2026 - Confident AI
confident-ai.com·Comparison
- 2
RAG Evaluation Metrics: Assessing Answer Relevancy, Faithfulness ...
confident-ai.com·Blog Post
- 2
Top 7 LLM Observability Tools in 2026 - Confident AI
confident-ai.com·Listicle
Alternatives in LLM Observability Evals & Gateways6
Confident AI positions itself as the most comprehensive LLM quality platform, differentiated by being built by the creators of DeepEval — the most widely-adopted open-source LLM evaluation framework.
- Unlike pure observability tools, it leads with evaluation depth: 50+ research-backed metrics covering RAG, agents, chatbots, and multi-turn conversations.
- Its core moat is closing the feedback loop between production tracing and evaluation datasets, and making rigorous evals accessible to non-engineers (product managers, QA teams, domain experts) without requiring custom tooling.
- It competes against narrower eval frameworks (Galileo, Patronus AI, Braintrust) by breadth of use-case coverage and open-source credibility, and against observability-first tools (Arize AI, Langfuse, Helicone) by claiming evaluation quality is the harder and more differentiated problem.
Reviews
Praised
- Open-source credibility via DeepEval integration
- Breadth of research-backed evaluation metrics
- Straightforward onboarding without credit card
- Cross-functional collaboration for non-engineers
- Responsive and supportive team
- CI/CD integration for regression testing
- Clean and well-structured dashboard UI
Criticized
- Learning curve for LLM evaluation concepts (faithfulness, answer relevancy)
- Advanced features gated behind higher-tier plans
- Per-user pricing can escalate for large teams
- Limited real-time streaming observability vs. dedicated tools
- Lack of pricing clarity for advanced features
- Early-stage platform with limited third-party review coverage
Confident AI has no verifiable aggregate score on G2 as of early 2026 (profile unclaimed, zero reviews on file). Gartner Peer Insights lists a small number of qualitative reviews praising reliability, smooth implementation, responsive support, and clean dashboard UX, though no numerical aggregate was confirmed. Product Hunt reception at launch was positive, with users praising the DeepEval integration, breadth of metrics, and the shift from subjective to objective LLM output measurement. Independent review commentary highlights straightforward onboarding (no credit card required) and the platform's open-source credibility as strong positives, while flagging a learning curve for LLM evaluation concepts and tier-gated advanced features as drawbacks.
Pricing
Free forever tier: 2 user seats, 1 project, 5 test runs/week, 1 GB-month trace spans, 1-week data retention.
- Starter
from $19.99/user/month (full regression testing, custom metrics, online evaluations, unlimited data retention, 5K online eval metric runs/month).
- Premium
from $49.99/user/month (chat simulations, no-code AI evaluation workflows, auto-curation from traces, real-time alerting, full API access, 10K online eval metric runs/month).
- Team
custom pricing for up to 10 users with unlimited projects, HIPAA/SOC2, SSO, dedicated support channel, and git-based prompt branching.
- Enterprise
custom pricing with unlimited users, on-premises deployment (AWS, Azure, GCP), 99.9% uptime SLA, and 24x7 dedicated technical support. Trace storage billed at $1/GB-month beyond included limits. Annual billing discounts available.
Limitations
- Learning curve for users unfamiliar with LLM evaluation concepts (e.g., faithfulness, answer relevancy).
- Most powerful features (chat simulations, no-code workflows, auto-dataset curation, alerting, API access) are gated behind Premium or higher tiers.
- Per-user pricing can compound costs for large teams.
- Platform is early-stage with a small team (7 employees), limited published third-party review coverage, and a maturing SaaS backend noted as less suited for full real-time streaming observability compared to dedicated observability-first tools.
- Advanced feature pricing noted as lacking clarity.
Frequently asked questions
Topic coverageCoverage by buyer topic
Topic Coverage
Prompt-Level Results
| Prompt | Gemini Search | ChatGPT | Perplexity |
|---|---|---|---|
Evaluation2/5 cited (40%) | |||
Which LLM platforms have the best workflows for human annotation and labeling of model outputs? | |||
What tools provide model-graded evaluation with calibrated reference-free scoring for chatbots? | |||
Which LLM eval platforms support running automated evaluations on production traces with custom metrics? | |||
What are the best tools for detecting hallucinations and faithfulness issues in RAG pipelines? | |||
Which evaluation platforms let me convert development-time evals into production guardrails automatically? | |||
Gateways & Routing0/5 cited (0%) | |||
What gateways have the lowest latency overhead when routing high-volume LLM traffic? | |||
Which LLM gateways are open-source and self-hostable for teams that don't want a SaaS dependency? | |||
Which AI gateways let me route between OpenAI, Anthropic, and open-source models with a single API call? | |||
What LLM gateway platforms support automatic fallbacks, retries, and load balancing across providers? | |||
Which AI proxies handle rate limiting, key rotation, and cost tracking across teams centrally? | |||
Production Readiness4/5 cited (80%) | |||
What AI eval platforms support on-premise or VPC deployment for regulated industries? | |||
What LLM monitoring platforms integrate with PagerDuty, Slack, or Datadog for alerting workflows? | |||
Which observability tools include real-time alerting on quality drops, not just latency? | |||
Which AI guardrail platforms provide pre-execution intervention to block unsafe agent actions before they run? | |||
Which LLM observability platforms scale to billions of traces per month at enterprise volumes? | |||
Setup & First Run1/5 cited (20%) | |||
Which AI observability platforms can be self-hosted with one command using Docker Compose? | |||
Which LLM observability tools work with OpenTelemetry so I don't have to add yet another vendor SDK? | |||
I want to add eval tracking to my agent — which platforms have the simplest Python decorator-style integration? | |||
What's the easiest way to log every LLM call my app makes for debugging without changing my application architecture? | |||
What's the fastest way to start tracing my LLM application calls without rewriting my code? | |||
Tracing & Debugging1/5 cited (20%) | |||
Which LLM observability tools show token usage, latency, and cost per step in an agent pipeline? | |||
What platforms support replaying production traces in development for reproducible debugging? | |||
Which observability platforms offer the best agent execution tracing for multi-step LLM workflows? | |||
What tools let me drill into a single user session to debug exactly what my agent did at each step? | |||
Which AI observability tools surface unknown failure patterns I wouldn't have written tests for? | |||
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Track prompt changesVertical Ranking
| # | Brand | PresencePres. | Share of VoiceSoV | DocsDocs | BlogBlog | MentionsMent. | Avg PosPos | Sentiment |
|---|---|---|---|---|---|---|---|---|
| 1 | Braintrust | 26.7% | 26.4% | 2.7% | 0.0% | 26.7% | #8.5 | +0.39 |
| 2 | Confident AI | 13.3% | 8.0% | 0.0% | 4.0% | 13.3% | #5.0 | +0.37 |
| 3 | LangChain | 13.3% | 6.9% | 5.3% | 0.0% | 13.3% | #9.3 | +0.44 |
| 4 | Langfuse | 13.3% | 18.4% | 6.7% | 2.7% | 13.3% | #12.1 | +0.51 |
| 5 | Galileo | 12.0% | 10.9% | 0.0% | 12.0% | 12.0% | #5.5 | +0.52 |
| 6 | Arize AI | 12.0% | 13.8% | 0.0% | 0.0% | 12.0% | #12.9 | +0.45 |
| 7 | BerriAI (LiteLLM) | 5.3% | 2.3% | 4.0% | 0.0% | 2.7% | #9.0 | +0.40 |
| 8 | Helicone | 5.3% | 10.3% | 1.3% | 5.3% | 5.3% | #18.2 | +0.32 |
| 9 | Traceloop | 4.0% | 1.7% | 0.0% | 4.0% | 4.0% | #3.7 | +0.20 |
| 10 | Portkey | 2.7% | 1.1% | 0.0% | 0.0% | 2.7% | #11.0 | +0.42 |
| 11 | Patronus AI | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
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