The Arize AI alternative
built for AI engineers
Arize is excellent for enterprise ML ops teams. If you're an AI engineer who wants to gate PRs on agent behavioral regressions without a data pipeline and a sales call, Refine AI is built for you.
At a glance
Why teams choose Refine AI over Arize
Arize is purpose-built for enterprise ML observability. These are the moments it's the wrong tool.
Built for ML ops, not AI engineers
Arize's mental model comes from traditional ML: model versions, feature drift, prediction distribution. These concepts don't map to agents. AI engineers need to think in tool calls, step counts, and loop risk — not feature drift. The UI and workflow aren't built for how agent teams work.
Monitoring after the fact, not gating before
Arize shows you production dashboards. By the time you see a regression in Arize, it has already shipped and your users have already hit it. Refine AI catches the regression at the PR — before it ever reaches production.
Enterprise complexity for a developer problem
Arize is designed for enterprise procurement: data connectors, schema configuration, ML pipeline integrations, a sales call. A GitHub Action that asserts on agent behavior needs none of that. Refine AI is configured in YAML and deployed in 5 minutes.
How Refine AI is different
5-minute setup
A GitHub Action and a YAML config file. No data pipelines, no connectors, no infrastructure to maintain.
Agent-native checks
step_count, tool_calls, loop_risk, cost_per_run, latency_p95 — the metrics agents actually regress on.
PR gating, not monitoring
Refine AI's output is a GitHub check status. Not a dashboard — a gate. The PR fails or it doesn't.
Developer self-serve
No sales call, no enterprise onboarding. Free to start, usage-based pricing for CI assertions.
Who each tool is built for
Use Arize AI if…
- →You manage many production ML models across a data/ML ops org
- →You need SOC 2, RBAC, and enterprise compliance features
- →You want Phoenix open source self-hosted observability
Use Refine AI if…
- →You're an AI engineer shipping agents and want fast CI setup
- →You want PRs auto-blocked on behavioral regressions
- →You need developer self-serve tooling without enterprise overhead
Get started in 5 minutes
No data pipelines. No sales call. Just a GitHub Action.
- name: Assert agent behavior
uses: agentdbg/agentdbg-action@v1
with:
baseline: main
checks: step_count,tool_calls,loop_risk,cost,latencySkip the ML ops overhead
Behavioral CI gates for AI engineers. 5-minute setup, no data pipeline, automatic PR enforcement.
Add to GitHub Actions