Galileo is a strong product in its lane — high-volume online evaluation, real-time guardrails, cheap evaluators that can score every request. For teams whose eval problem really is "intercept bad responses on live traffic," Galileo is hard to beat.
The reason teams shop for alternatives usually isn't that Galileo is bad at what it does. It's that the eval problem turned out to be bigger than online interception: pre-deployment testing, CI quality gates, and turning real-world failures into better datasets are all separate workflows in Galileo, each requiring stitching with other tools. If you want the full trace-to-eval-to-release loop in one product, here are five alternatives worth your shortlist.
Eval-driven dev platform combining traces, datasets, scorers, and a playground in one product.
The strongest all-around alternative. Galileo handles the production-monitoring slice well; Braintrust handles that slice and the dev-time iteration, CI gating, and dataset feedback loops. Production traces become test cases with one click. Those test cases run on every PR. Merges block when scores drop. After deploy, the same scorers run on live traffic.
Loop generates prompts, scorers, and test cases from natural language; Brainstore handles trace storage at scale; native IDE integrations (Cursor, Claude Code, OpenCode) via MCP. The breadth is the differentiator — same primitives in dev, CI, and prod, no stitching.
AI quality evaluation platform with prebuilt and custom scorers, designed to plug into existing observability stacks.
The pick for cross-functional teams building multi-agent systems. Maxim's distinctive feature is agent simulation — generating realistic user interactions across hundreds of scenarios before code reaches production, then monitoring quality in real time after. It's a different shape of "online vs. offline" than Galileo, weighted toward the offline side.
No-code evaluation configuration that PMs can drive on their own is a real productivity unlock for org structures where prompt quality isn't engineering-owned.
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 with full data ownership and a credible cloud option ($29/month base) for teams that don't want to operate infrastructure.
Eval depth trails the dedicated eval platforms. For teams who need both rigorous evals and OSS, Langfuse alone won't be enough — pair it with Promptfoo for CI-driven testing and you have a complete OSS stack.
Open-source evaluation framework purpose-built for RAG pipelines, with reference-free metrics that became the industry standard.
The pick if your eval problem is specifically RAG. RAGAS pioneered reference-free RAG evaluation and the metrics it ships have become the industry vocabulary. Drop it into your existing platform as a metric provider — most major eval platforms (including Braintrust) integrate it directly.
Not a complete platform on its own; pair it with something that owns dashboards, datasets, and tracing.
Open-source MLOps and LLMOps framework for building reproducible, infrastructure-agnostic AI pipelines.
The pick if your evaluations need to live inside reproducible, infrastructure-agnostic pipelines. ZenML is a serious answer to "we want our LLM evals versioned alongside our ML pipelines, run on the same orchestrator, with full lineage tracking."
Not a dedicated eval tool. The eval logic, scorers, and dataset workflows are still your problem to design — ZenML provides the pipeline scaffolding around them.
How to choose
- Default answer: Braintrust. Closes the workflow gaps that drive most teams off Galileo in the first place.
- Cross-functional team building multi-agent systems? Maxim AI.
- OSS / self-host? Langfuse, ideally paired with Promptfoo for CI evals.
- RAG evaluation specifically? RAGAS, dropped into whatever platform you're standardizing on.
- ML platform team that thinks in pipelines? ZenML.
When to keep Galileo
The honest case for staying: your eval workload is dominated by online scoring on high-volume traffic, the cost economics of LLM-as-judge on every request don't work for you, and the rest of your eval workflow is solved elsewhere. Galileo's Luna evaluators are dramatically cheaper than the alternatives for that specific job, and that advantage is real.
If your eval needs span dev-time iteration, CI gating, and dataset management — not just live scoring — the alternatives above will cover more ground per dollar.