What Arize does well: Arize's roots are in traditional ML observability — data drift, embedding drift, and model performance monitoring — extended to cover LLM and agent tracing (including via its open-source Phoenix project). If you're running both classical ML models and LLM-based systems and want one observability platform across both, or need embedding-level drift analysis, that lineage is a real strength.
What Dunetrace adds: Dunetrace is purpose-built for one thing — catching agent runtime failures as they happen and, where possible, stopping them before they finish. It's self-hosted by default (your own Postgres, no enterprise platform to stand up), needs no embedding infrastructure or drift baselines to start catching structural failures, and its runtime prevention policies act inside the agent's own process — something a general observability platform, built around ingesting and analyzing telemetry after the fact, isn't architected to do.
Side by side
| Dunetrace | Arize | |
|---|---|---|
| Primary origin | Purpose-built for agent runtime failure detection | ML observability (drift, data quality) extended to LLM/agent tracing |
| Can stop a run mid-flight | Yes — runtime prevention policies | No — observability platform, not in-path |
| Structural failure detection | 23 detectors, zero LLM calls, automatic, no setup | Not the primary design center — tracing and eval focused |
| Embedding/drift analysis | No | Yes — a core strength |
| Deployment | Self-hosted, your own Postgres, Apache 2.0 | Hosted platform or enterprise deployment |
| Setup overhead | Two lines of instrumentation, detectors run immediately | Typically more involved — designed for broader ML platform needs |
Use both
If you're already invested in Arize for classical ML monitoring, Dunetrace can run alongside it purely for agent-specific structural detection and runtime prevention — the two aren't solving the same problem, and neither replaces what the other is actually good at.