Arize is a credible product with a real production track record — built on years of operating ML monitoring at scale. The reason teams shopping for an alternative usually aren't unhappy with Arize specifically; they're shopping because their workflow is LLM-only, and Arize's ML-first architecture creates seams that don't matter to ML platform teams but do matter to product engineering teams shipping LLM features.
Specifically: no deployment-blocking on eval regressions, dataset management that lives separately from tracing, and limited pre-production simulation. If those gaps don't bite your workflow, Arize is fine. If they do, here are five alternatives worth your shortlist.
Eval-driven dev platform combining traces, datasets, scorers, and a playground in one product.
The best alternative for the typical Arize-shopping team — engineers shipping LLM features who want monitoring, evaluation, and CI gates in one product. CI/CD-native deployment blocking is the differentiator: prompts that fail eval in CI don't merge, full stop. That's a workflow Arize doesn't offer at any tier.
Production traces become test cases with one click; the same scorers run in CI and on production traffic; quality drops surface as alerts and feed back into datasets. The whole system is designed for "ship LLM changes the way we ship code changes," and it shows.
Open-source LLM observability with evals, prompt management, and best-in-class tracing.
The pick if open-source and self-hosting are non-negotiable. MIT-licensed, OpenTelemetry-native tracing, prompt management, and basic evaluation — all running in your own infrastructure with full data ownership.
The tradeoff is depth: Langfuse logs traces well, but you'll build the eval pipelines and CI integrations yourself. For teams with strong DevOps capacity and strict data residency requirements, that work is worth it.
Enterprise ML governance platform extended to LLMs and generative AI, with audit-ready traces and in-environment evaluations.
The pick if you're moving off Arize but staying inside the "ML governance" use case — regulated industries, both classical ML and LLMs in production, real audit-trail requirements. Fiddler covers that scope deliberately and well.
If you're moving off Arize because you don't actually need the ML governance feature set, Fiddler isn't the answer. Braintrust or Langfuse will fit your workflow better.
Observability and evaluation built by the LangChain team — best-in-class if your stack is LangChain or LangGraph.
The pick if your stack is LangChain or LangGraph. Zero-config tracing, native eval workflows, and a Hub for prompt versioning — all designed by the team that designs LangChain itself. Switching costs are real, but so is the depth of integration.
Outside LangChain, the lock-in tradeoff stops paying off.
Proxy-based LLM observability — drop in by changing the base URL, no SDK changes needed.
The pick if your needs are narrower than Arize's: just OpenAI traffic, just basic logging and cost tracking, no evaluation. Set up in five minutes by changing a base URL.
Don't pick Helicone as a one-for-one Arize replacement — the depth gap is huge. Pick it if your real requirement is "we want monitoring without the project," and accept that you'll graduate when the requirement grows.
When to keep Arize
The honest case for staying: you're operating both classical ML and LLM apps in production, and the unified observability is worth more than the LLM-specific workflow polish you'd gain by moving. Arize's ML monitoring is mature, and stitching together two platforms — one for ML, one for LLMs — usually costs more than the ML-first seams cost you.
If LLM is the entire scope, the alternatives above are sharper.
How to choose
- Default answer: Braintrust. The CI-gated workflow is the gap most Arize teams cite, and Braintrust closes it directly.
- Open-source / self-host? Langfuse.
- Regulated industry, ML + LLM? Fiddler (or stay on Arize).
- All-in on LangChain? LangSmith.
- Just need basic monitoring? Helicone.