Reliability
Success rates, failed requests and provider degradation.
AI OBSERVABILITY FOR PRODUCTION
Know when your LLM is failing—and what to do next.
AgentLens is a privacy-first AI observability and LLM monitoring platform. Compare providers before launch, then track reliability, latency, errors and cost across production AI calls.
- OpenAI, Anthropic, Gemini, Groq and more
- No production prompt content required
- Open-source and self-hostable
production / live MONITORING
INCIDENT 412
Last 60 minProvider degradation
Success rate61.2%↓ 37.2%
p95 latency2.8s3.1× slower
RECOMMENDED ACTIONRoute new traffic to Anthropic99.1% success · 410ms
Operational telemetry only. Prompts stay private.
WHAT AGENTLENS MONITORS
One clear view of production AI health.
See the operational signals that matter without turning your team into observability experts.
Latency
Response times and slowdowns before they become complaints.
Cost
Estimated model spend and unexpected usage changes.
Root cause
A plain-language incident story with evidence attached.
BEFORE AND AFTER LAUNCH
Choose with evidence. Keep monitoring the choice.
AgentLens connects provider comparison and production monitoring in one workflow, so the recommendation you test before launch can be checked against real behavior after launch.
Compare AI providers- 01
Describe the AI job
Explain the result you need in everyday language.
- 02
Run fair local tests
Selected providers receive the same visible synthetic checks.
- 03
Connect the best fit
Use one guided command without replacing your provider SDK.
- 04
Monitor production health
See incidents, evidence and the safest next action.
PRIVACY-FIRST BY DESIGN
Observe performance without collecting conversations.
Provider and model
Success and response time
Error and cost signals
No provider API keys
No production prompts
No production responses
FREQUENTLY ASKED QUESTIONS
AI observability, explained simply.
What is AI observability?+
AI observability shows how an AI application behaves in production. It connects provider health, request success, latency, errors and cost so a team can understand what changed and what to do next.
How is LLM monitoring different from normal application monitoring?+
Traditional monitoring can show whether an API returned a response. LLM monitoring also follows provider, model, latency, retries, estimated cost and AI-specific failure patterns across the calls that power a feature.
Which AI providers can AgentLens monitor?+
AgentLens is designed for multi-provider AI applications, including OpenAI, Anthropic, Google Gemini, Groq, Mistral and Azure AI paths.
Does AgentLens store prompts or model responses?+
AgentLens is designed around operational telemetry. Production prompts, model responses and provider API keys are not required for the monitoring workflow described here.
Can AgentLens help choose an LLM provider before launch?+
Yes. The local provider comparison runs the same visible synthetic checks across selected providers and explains the best fit for quality, reliability, speed and cost priorities.
START WITH ONE AI FEATURE
Start monitoring free See the failure before your users do.
Create a free project and connect AgentLens with one guided command.