Built for LLMs. Not retrofitted from ML monitoring.
Statistical drift was the old story. Agent behaviour is the new one.
Arize started in classic ML monitoring and bolted on LLM features. LangWatch is LLM-native end to end: scenario simulation, conversation-aware evals, prompt optimization, and a workflow domain experts can run.
Join thousands of AI developers shipping reliable agents with LangWatch.
How LangWatch compares to Arize.
Five things teams care about when picking a quality layer for agents. Each row shows what Arize ships today and what LangWatch gives you on day one.
Scenario-based testing simulates real users against your full agent stack with tools, state, and a judge.
Traditional evaluation on input/output pairs with statistical analysis. Limited for multi-turn agent behaviour.
Purpose-built for conversation flows, prompt engineering, and agent-specific evaluation patterns.
Drift detection and statistical analysis remain the spine. LLM features are extensions of that worldview.
Friendly platform UI for domain experts. Powerful APIs and SDKs for engineers building complex workflows.
Advanced statistical tools designed for technical teams. Steep ramp for product or business users.
Adversarial safety and security testing, an AI gateway for every model and key, and a governance layer with control over every agent in your org, plus quality-aware alerts on eval-score drops.
Arize comes from classic ML observability. No built-in agent red teaming, AI gateway, or org-wide agent governance.
Full library of LLM and agent-specific evaluators, plus a one-line API to attach your own metrics to traces.
Strong on statistical evals and drift, lighter on conversation-level quality and agent-specific scoring.
Three reasons agent teams choose LangWatch.
Scenario-based testing finds workflow failures and edge cases during development, before production sees them.
AI-powered Ask finds errors and anomalies for you, across flame charts, span lists, topology and graph views, waterfall traces with a full audit trail, and your own saved lenses.
Domain experts create test scenarios in the UI. Engineers implement advanced evaluation logic in code.
“Drift charts told us something changed. Scenario tests told us what would break. We finally had agent-level quality, not ML metrics.”
Move from ML monitoring to agent quality.
Connect in five minutes. Any framework, any model. Agent simulation included on day one.