Langfuse
Best for self-hosting
Why teams pick it
Control your scheduling stack on your own infrastructure.
Open-source alternatives to Confident AI
Compare community-driven replacements for Confident AI in llm evaluation & observability workflows. We curate active, self-hostable options with transparent licensing so you can evaluate the right fit quickly.
Confident AI
Confident AI (from the creators of DeepEval) provides metrics, regression testing, tracing, and guardrails to compare prompts/models, catch regressions, and monitor LLM applications.Read more
Key stats
- 12Alternatives
- 3Support self-hosting
Run on infrastructure you control
- 12Active development
Recent commits in the last 6 months
- 8Permissive licenses
MIT, Apache, and similar licenses
Counts reflect projects currently indexed as alternatives to Confident AI.
Start with these picks
These projects match the most common migration paths for teams replacing Confident AI.
Opik
Privacy-first alternative
Why teams pick it
Organizations that prefer self‑hosted observability for data privacy
All open-source alternatives
Phoenix
AI observability platform for tracing, evaluation, and prompt management
Active developmentFast to deployIntegration-friendlyJupyter Notebook
Why teams choose it
- Unified tracing via OpenTelemetry
- LLM‑specific evaluation suite
- Versioned dataset and experiment tracking
Watch for
Requires instrumentation of your code
Migration highlight
Prompt Optimization
Iteratively test prompt variations, compare model responses, and select the best performing version.
Langfuse
Collaborative platform for building, monitoring, and debugging LLM applications.
Self-host friendlyActive developmentPrivacy-firstTypeScript
Why teams choose it
- Unified tracing of LLM calls, retrievals, embeddings, and agent actions
- Prompt management with version control and low‑latency caching
- Flexible evaluation pipelines supporting LLM‑as‑judge and human feedback
Watch for
Production self‑hosting may require container or Kubernetes expertise
Migration highlight
Debugging a multi‑step agent workflow
Trace each LLM call, retrieval, and tool use to pinpoint failures and iterate via the integrated playground.
Opik
Open-source platform for tracing, evaluating, and optimizing LLM applications
Active developmentPermissive licensePrivacy-firstPython
Why teams choose it
- Deep tracing of LLM calls and agent activity
- LLM‑as‑a‑judge evaluation with custom metrics
- Scalable production monitoring (40M+ traces/day)
Watch for
Self‑hosting adds operational overhead and requires container expertise
Migration highlight
RAG chatbot performance tuning
Iteratively refine prompts and retrieval strategies, reducing hallucinations as measured by LLM‑as‑a‑judge metrics.
OpenLIT
Unified observability and management platform for LLM applications
Active developmentPermissive licenseFast to deployPython
Why teams choose it
- OpenTelemetry‑native SDKs for vendor‑agnostic tracing and metrics
- Analytics dashboard with cost, performance, and exception monitoring
- Prompt Hub and Vault for secure prompt versioning and API key management
Watch for
Requires running the OpenLIT stack (ClickHouse, collector) adding infrastructure overhead
Migration highlight
Monitor LLM latency and token usage in production
Identify performance bottlenecks and optimize model selection, reducing response times by up to 30%.
Laminar
Trace, evaluate, and scale AI applications with minimal code.
Self-host friendlyActive developmentPermissive licenseTypeScript
Why teams choose it
- OpenTelemetry‑based automatic tracing for major LLM frameworks
- Built‑in observability of latency, cost, and token usage
- Parallel evaluation SDK with dataset integration
Watch for
Self‑hosting requires managing multiple services (Postgres, ClickHouse, RabbitMQ)
Migration highlight
Real‑time latency monitoring for a chatbot
Detect and alert on response slowdowns, reducing user‑perceived latency.
Langtrace
Observability platform for LLM applications with real‑time tracing
Active developmentPrivacy-firstFast to deployTypeScript
Why teams choose it
- OpenTelemetry‑compliant tracing across LLM providers and vector databases
- Real‑time monitoring of API calls, latency, and cost
- Performance analytics with visual dashboards
Watch for
Some frameworks lack TypeScript SDK coverage (e.g., Langchain, Langgraph)
Migration highlight
Debugging LLM API latency spikes
Identify slow calls, reduce response times, and lower usage costs
TruLens
Systematically evaluate, track, and improve your LLM applications
Active developmentPermissive licenseIntegration-friendlyPython
Why teams choose it
- Stack‑agnostic instrumentation for prompts, models, retrievers, and knowledge sources
- Customizable feedback functions covering honesty, harmlessness, helpfulness, and RAG metrics
- Interactive UI for comparing experiment runs and visualizing evaluation results
Watch for
Requires a Python environment; not language‑agnostic
Migration highlight
RAG pipeline benchmarking
Identify which retriever‑model combination yields highest relevance and factuality scores
Evidently
Evaluate, test, and monitor ML & LLM systems effortlessly
Active developmentPermissive licenseIntegration-friendlyJupyter Notebook
Why teams choose it
- 100+ built‑in metrics for tabular and generative tasks
- Modular Reports that can be converted into pass/fail Test Suites
- Self‑hosted monitoring UI with optional managed Cloud service
Watch for
Requires a Python environment; not native to other languages
Migration highlight
Detect data drift between training and production
Early alerts when feature distributions shift, preventing model degradation
Helicone
Open-source LLM observability and developer platform for AI applications
Self-host friendlyActive developmentPermissive licenseTypeScript
Why teams choose it
- One-line integration with 20+ LLM providers including OpenAI, Anthropic, and Gemini
- Agent and session tracing with cost, latency, and quality metrics
- Prompt versioning and playground for rapid iteration with production data
Watch for
Self-hosting requires managing five separate services (Web, Worker, Jawn, Supabase, ClickHouse, MinIO)
Migration highlight
Multi-Agent System Debugging
Trace complex agent interactions across sessions to identify bottlenecks, track costs per agent, and optimize prompt chains using production data in the playground.
Coze Loop
Full‑life‑cycle platform for building, testing, and monitoring AI agents
Active developmentPermissive licenseFast to deployGo
Why teams choose it
- Playground for interactive prompt debugging and version management
- Automated, multi‑dimensional evaluation of prompts and agents
- SDK‑based trace reporting with end‑to‑end execution observability
Watch for
Advanced features from the commercial edition are not included
Migration highlight
Prompt Iteration
Developers quickly test, compare, and version prompts across multiple LLMs, reducing debugging time.
OpenLLMetry
Full‑stack observability for LLM applications via OpenTelemetry
Active developmentPermissive licenseIntegration-friendlyPython
Why teams choose it
- Native OpenTelemetry compatibility
- Instrumentation for major LLM providers, vector DBs, and AI frameworks
- Plug‑and‑play SDK with one‑line initialization
Watch for
Instrumentation limited to providers listed in documentation
Migration highlight
Debug LLM prompt failures
Trace each prompt, response, and token usage across providers, pinpointing latency spikes or error patterns.
Agenta
Accelerate production LLM apps with integrated prompt, evaluation, observability
Active developmentFast to deployIntegration-friendlyTypeScript
Why teams choose it
- Interactive prompt playground with versioned branching for SME collaboration
- Built‑in evaluation framework offering 20+ LLM‑as‑judge evaluators and custom test sets
- Real‑time observability of cost, latency, and traces via OpenTelemetry standards
Watch for
Self‑hosting requires Docker and environment configuration
Migration highlight
Customer support chatbot refinement
SMEs iteratively improve prompts, run evaluations against real tickets, and monitor latency to ensure SLA compliance.
Choosing a llm evaluation & observability alternative
Teams replacing Confident AI in llm evaluation & observability workflows typically weigh self-hosting needs, integration coverage, and licensing obligations.
- 3 projects let you self-host and keep customer data on infrastructure you control.
- 12 options are actively maintained with recent commits.
Tip: shortlist one hosted and one self-hosted option so stakeholders can compare trade-offs before migrating away from Confident AI.