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Nested traces for every LLM call, tool run, and agent turn
The top alternatives to Patronus AI in the Observability, Prompts & Evals space, compared on features, pricing, and what they're best at.
Updated March 10, 2026
Patronus AI is a San Francisco startup founded by former Meta machine learning experts Anand Kannappan and Rebecca Qian, focused on automatically detecting costly and dangerous LLM mistakes at scale. The company raised USD 17 million in Series A funding led by Notable Capital, bringing total funding to USD 20 million. Patronus AI developed a first-of-its-kind automated evaluation platform that identifies errors like hallucinations, copyright infringement, and safety violations in LLM outputs. The platform uses pay-as-you-go pricing starting at USD 10-20 per 1,000 API calls, with USD 5 in free credits for new users. Trusted by companies like OpenAI, HP, Pearson, AngelList, and Etsy, Patronus AI has processed millions of requests, catching hundreds of thousands of hallucinations. Customers praise the research-first approach and 20% better evaluation performance than competing methods, though as a startup-stage company, many processes are still being built.
Respan
Nested traces for every LLM call, tool run, and agent turn
LangSmith
Native tracing for LangChain and LangGraph
MLflow
OpenTelemetry-based tracing for LLMs and agents
Weights & Biases
ML experiment tracking
Arize AI
ML observability with LLM support
Langfuse
Tracing for LLM calls, tool runs, and agent steps
Datadog LLM
LLM monitoring within Datadog platform
Traceloop
OpenTelemetry
Helicone
Braintrust
Experiments comparing prompts and models
HoneyHive
Prompt management
Promptfoo
Phoenix
OpenTelemetry-based LLM and agent tracing
Portkey
Humanloop
DeepEval
Ragas
RAG-specific evaluation framework
Sentry
Galileo AI
LLM output quality evaluation
LangWatch
Multi-turn agent simulation testing
PromptLayer
Confident AI
DeepEval open-source evaluation framework
Maxim AI
Distributed tracing for LLM and agent apps
Opik
Agenta
Future AGI
Multimodal evaluation (text, image, audio, video)
Lunary
Parea AI
Sentrial
Agent failure root cause analysis
Athina AI
Chamber
ML infrastructure automation
Moda
Hallucination detection
Ashr
Multi-modal synthetic testing
AgentOps
Session replay with time-travel debugging
Fiddler AI
Agentic observability across multi-step agent workflows
Laminar
Agent tracing with a readable transcript view
OpenObserve
Logs, metrics, traces, RUM, and session replay
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