Arize alternative | LangWatch
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.
ML-monitor view
statistical drift
kl-div ↑ 0.18 · what now?
LLM-native view
agent conversation
- user: refund unused minutes?
- agent: lookup_account()
- judge: policy-violation
caught at turn-03 · pre-prod
from drift to dialogue LLM-native
The Arize alternative.
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.
| Capability | LangWatch | Arize |
|---|---|---|
| 01 | Pre-production testing | Input/output evaluation |
| Agent simulation suite | Traditional evaluation on input/output pairs with statistical analysis. Limited for multi-turn agent behaviour. | |
| 02 | Platform origin | ML monitoring, LLM bolted on |
| LLM-native architecture | Drift detection and statistical analysis remain the spine. LLM features are extensions of that worldview. | |
| 03 | Who can use it | ML engineers, data scientists |
| 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. | |
| 04 | Red teaming, governance, and gateway | Retrofitted from ML monitoring |
| 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. | |
| 05 | Evaluation library | Statistical evals |
| 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.
- Simulate real users: Scenario-based testing finds workflow failures and edge cases during development, before production sees them.
- Deep agent tracing and debugging: 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.
- Hybrid collaboration: 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.”
Director of AI· Search and recommendation platform
- 94% regressions caught pre-prod
- 5 min time-to-first-eval
- any frameworks supported
- $0 cost to start
Move from ML monitoring to agent quality.
Connect in five minutes. Any framework, any model. Agent simulation included on day one.
Platform
- Agentic AI Testing Run realistic user scenarios against your agent to catch issues before production
- LLM Evaluation Measure response quality and accuracy so you ship agents that hold up in production
- LLM Observability Trace every agent step and monitor cost and latency with full production visibility
- AI Governance New Govern every model, key, and tool. Virtual keys with budgets, routing policies, and a full audit trail
- Prompt Management Version, deploy, and A/B test prompts as code, with full history and GitHub sync
- Voice AI Test and simulate your voice AI agents at scale, before they talk to customers
- LLM Red-teaming Simulated attacks that uncover safety and security gaps in AI agents
- Track your Claude Code Usage Full trace history and token spend for Claude Code, Codex, and every coding agent