AI visibility report
Confident AI ranks #4 in LLM Observability Evals & Gateways AI search.
Outside the top three on 19 of the 25 prompts buyers actually ask.
Braintrust is cited on 11 of those losses.
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Track Confident AI across these prompts daily.
Start free trial#4 among 11 vendors · still absent from 84% of tracked prompt responses
Top-3 citations across 125 prompt × platform pairs
Peer Ranking
Key Metrics
Platform Breakdown
Perplexity
Bing Copilot
Gemini Search
Google AI Mode
ChatGPTVisible, but narrative can improve. Confident AI ranks #4 on presence but #8 on sentiment. The brand appears relatively often, but competitors may be getting more favorable language when they appear.
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 winning1
What platforms support end-to-end tracing of multi-agent pipelines including tool calls, retrieval steps, and sub-agent spawning?
Avg # 3.0 · 1 platform
Where Confident AI is losing5
Which LLM tracing platforms export trace data to a data warehouse so analysts can run custom eval queries alongside product metrics?
Competitors on 4 platforms
Track this promptWhich LLM observability tools work with OpenTelemetry-compatible backends so we can consolidate LLM traces alongside existing service traces?
Competitors on 4 platforms
Track this promptWhich LLM eval platforms have the best prompt playground experience for iterating on system prompts against a saved test dataset?
Competitors on 4 platforms
Track this promptWhat LLM observability tools do ML engineering teams typically use to annotate and review production traces for quality feedback?
Competitors on 4 platforms
Track this promptWhat are the most production-hardened LLM gateway options for an enterprise team needing 99.9% uptime with circuit-breaker support?
Competitors on 3 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
Short answer: Platforms like Confident AI, Braintrust, Future AGI, Langfuse, and Promptfoo integrate directly with version control (Git-style branching, CI/CD hooks, regression gates) so prompt regressions can be tied back to specific commits or...
I'm evaluating LLM eval platforms — which ones integrate with version control to tie prompt regressions back to specific code or config changes?
The best fit for your needs is _Confident AI_ — it’s designed for _cross-functional collaboration_, letting engineers, PMs, and domain experts review and annotate LLM traces without SQL or engineering bottlenecks.
I'm looking for an LLM observability platform with a great team collaboration workflow — where engineers and PMs can both review trace quality without SQL knowledge.
The leading options are Braintrust, MLflow, LangSmith, Langfuse, Galileo, Confident AI, and AgentOps. 🔑 Key Platforms for Multi-Agent Tracing ---------------------------------------- | Platform | Strengths | Open Source?
What platforms support end-to-end tracing of multi-agent pipelines including tool calls, retrieval steps, and sub-agent spawning?
Most cited sources8
32Top 7 LLM Observability Tools in 2026 - Confident AI
confident-ai.com·Comparison
- 15
Best AI Evaluation Tools for Prompt Experimentation in 2026 - Confident AI
confident-ai.com·Comparison
- 15
10 LLM Observability Tools to Evaluate & Monitor AI in 2026 - Confident AI
confident-ai.com·Comparison
- 15
Top 9 LLM Evaluation Tools in 2026 - Confident AI
confident-ai.com·Listicle
- 12
Top 7 AI Agent Observability Platforms for 2026 - Confident AI
confident-ai.com·Comparison
- 6
Best LLM Observability Platforms to Improve AI Product Reliability in 2026 - Confident AI
confident-ai.com·Comparison
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 | Bing Copilot | Google AI Mode | Perplexity | ChatGPT |
|---|---|---|---|---|---|
Capability1/5 cited (20%) | |||||
Which LLM observability tools handle PII redaction and data masking in traces for teams with HIPAA or GDPR compliance requirements? | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Which LLM gateways handle multi-provider fallback and automatic retries while preserving full trace context across the switch? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
What platforms support end-to-end tracing of multi-agent pipelines including tool calls, retrieval steps, and sub-agent spawning? | A competitor was cited | Your brand and a competitor were cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
Which LLM evaluation platforms support custom rubric-based scoring for domain-specific correctness beyond generic faithfulness metrics? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited |
Looking for an eval platform that supports automated safety and toxicity scoring on LLM outputs at scale — what are my options? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Developer Experience4/5 cited (80%) | |||||
Which LLM tracing platforms make it easiest to replay a failed multi-step agent run and pinpoint exactly where reasoning went wrong? | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Your brand and a competitor were cited | A competitor was cited |
Which LLM gateway tools give developers the best real-time cost and token usage visibility across multiple LLM providers during development? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited |
Which LLM eval platforms have the best prompt playground experience for iterating on system prompts against a saved test dataset? | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Your brand and a competitor were cited | A competitor was cited |
I'm looking for an LLM observability platform with a great team collaboration workflow — where engineers and PMs can both review trace quality without SQL knowledge. | A competitor was cited | Your brand was cited | A competitor was cited | Your brand and a competitor were cited | A competitor was cited |
What LLM observability tools do ML engineering teams typically use to annotate and review production traces for quality feedback? | A competitor was cited | Your brand and a competitor were cited | A competitor was cited | Your brand and a competitor were cited | A competitor was cited |
Integrations & Ecosystem3/5 cited (60%) | |||||
I'm evaluating LLM eval platforms — which ones integrate with version control to tie prompt regressions back to specific code or config changes? | Neither your brand nor a competitor was cited | Your brand was cited | A competitor was cited | Your brand and a competitor were cited | A competitor was cited |
What LLM gateway tools integrate best with secret managers and internal auth systems for enterprise teams rolling out to multiple product teams? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited |
Which LLM tracing platforms export trace data to a data warehouse so analysts can run custom eval queries alongside product metrics? | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Your brand and a competitor were cited | A competitor was cited |
Which LLM observability tools work with OpenTelemetry-compatible backends so we can consolidate LLM traces alongside existing service traces? | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited |
Which LLM observability platforms integrate natively with the most popular agent frameworks so traces appear automatically with no manual instrumentation? | Your brand and a competitor were cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited |
Performance & Reliability3/5 cited (60%) | |||||
What are the most production-hardened LLM gateway options for an enterprise team needing 99.9% uptime with circuit-breaker support? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited |
What LLM tracing platforms handle high-throughput production workloads — millions of traces per day — without degrading query performance? | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Your brand and a competitor were cited | A competitor was cited |
Which LLM gateways add the least latency overhead when routing between LLM providers — safe to use in production for sub-500ms SLAs? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited |
Which LLM eval platforms support async evaluation at scale without blocking the inference path or adding latency for end users? | A competitor was cited | Your brand and a competitor were cited | Neither your brand nor a competitor was cited | Your brand and a competitor were cited | A competitor was cited |
Which LLM observability platforms stay reliable under traffic spikes from batch eval jobs running thousands of LLM calls simultaneously? | Neither your brand nor a competitor was cited | Your brand was cited | A competitor was cited | Your brand and a competitor were cited | A competitor was cited |
Setup & First Run2/5 cited (40%) | |||||
I'm evaluating LLM gateway solutions for a startup — which ones have the simplest self-hosted setup with a working UI on day one? | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Which LLM observability platforms can a small team get running against a production RAG pipeline in under a day? | A competitor was cited | Your brand was cited | A competitor was cited | Your brand and a competitor were cited | A competitor was cited |
Which evaluation platforms for LLM outputs are easiest to plug into an existing CI pipeline for a five-engineer team? | Neither your brand nor a competitor was cited | Your brand and a competitor were cited | A competitor was cited | Your brand and a competitor were cited | A competitor was cited |
What's the fastest LLM tracing platform for instrumenting a Python-based agent framework without rewriting existing code? | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited |
What are the best OpenTelemetry-compatible tracing backends for LLM apps that work out of the box without custom span parsing? | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited |
Turn this matrix into daily prompt monitoring.
Track prompt changesVertical Ranking
| # | Brand | PresencePres. | Share of VoiceSoV | DocsDocs | BlogBlog | MentionsMent. | Avg PosPos | Sentiment |
|---|---|---|---|---|---|---|---|---|
| 1 | Braintrust | 31.2% | 28.3% | 2.4% | 0.0% | 43.2% | #4.0 | +0.43 |
| 2 | LangChain | 20.8% | 13.7% | 3.2% | 0.0% | 51.2% | #4.4 | +0.45 |
| 3 | Langfuse | 16.8% | 13.3% | 4.8% | 3.2% | 56.8% | #2.8 | +0.48 |
| 4 | Confident AI | 16.0% | 17.3% | 0.0% | 0.0% | 12.8% | #5.7 | +0.42 |
| 5 | Galileo | 12.8% | 7.5% | 0.0% | 12.8% | 13.6% | #3.5 | +0.38 |
| 6 | Arize AI | 8.8% | 8.0% | 0.0% | 4.0% | 8.0% | #4.3 | +0.53 |
| 7 | Traceloop | 5.6% | 4.0% | 0.0% | 4.8% | 6.4% | #4.4 | +0.37 |
| 8 | Portkey | 4.0% | 2.7% | 0.8% | 0.0% | 18.4% | #3.0 | +0.26 |
| 9 | LiteLLM | 4.0% | 2.7% | 0.8% | 0.0% | 0.0% | #3.2 | +0.56 |
| 10 | Helicone | 1.6% | 0.9% | 0.8% | 0.8% | 24.8% | #5.0 | +0.45 |
| 11 | Patronus AI | 1.6% | 1.8% | 1.6% | 0.8% | 3.2% | #8.5 | +0.79 |
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