LangfuseOpen Source Agent Evals & Observability
vs
Ray ServeScalable model serving library for building online inference APIs
Comparison · MLOps Platforms · weekly run Nov 2
Langfuse vs Ray Serve
Which one do AI engines recommend, and how do they differ on price, fit and features?
The short answer
Langfuse is the stronger AI pick in MLOps Platforms: it has a visibility score of 0.3 versus 0 for Ray Serve.
Choose Langfuse if…
- You want the brand AI engines rank higher (0.3 vs 0)
- You need a self-hosted or on-premises option
- You need ISO 27001 or HIPAA-ready region or GDPR coverage
- Discounted cloud pricing for qualifying early-stage startups
Choose Ray Serve if…
- You need ISO/IEC 27001 coverage
AI visibility side by side
Latest weekly run · Nov 2
| Metric | Langfuse | Ray Serve |
|---|---|---|
| Visibility score | 0.3% | 0% |
| Change vs last week | New | New |
| Rank in MLOps Platforms | #51 | #61 |
| Share of voice | 0.2% | 0% |
| Mentions this run | 1 | 0 |
| Positive in AI answers | 50% | — |
The stronger value in each row is marked with a ✓.
Visibility score trend
Weekly runs over the last 60 days
Langfuse 0pp · Ray Serve 0pp over 0 weekly runs.
Engine by engine
Langfuse leads on 1 of 3 engines
ChatGPT
AI Mode
Perplexity
LangfuseRay Serve
Head to head in AI answers
0 engine answers name both brands
0Langfuse ranked higher
0Ray Serve ranked higher
Features, pricing & fit
| Dimension | Langfuse | Ray Serve |
|---|---|---|
| Pricing model | Usage-based | — |
| Entry price | Free; paid from $29 / mo | — |
| Free plan | Yes | — |
| Deployment | Langfuse Cloud in US, EU, Japan and HIPAA regions, or self-hosted under the MIT license | Self-managed on a laptop, in a Docker container, on a managed Ray cluster, or on Kubernetes via KubeRay |
| Integrations | 100+ | — |
| Compliance | SOC 2 Type II, ISO 27001, HIPAA-ready region, GDPR | SOC 2 Type II, ISO/IEC 27001 |
| Support | Community support via GitHub, In-app support, Private Slack or MS Teams channel on Pro and Enterprise plans, Named lead support engineer on Enterprise plans | Ray Community Slack, Hands-on training and expert support from the Anyscale team that created Ray |
| Headquarters | — | San Francisco, California |
| Funding | Seed · $4M raised | Series C |
| Best for | Engineering teams building LLM applications and AI agents that want open-source, self-hostable tracing and evaluation | Python teams serving machine learning models, including LLMs, as online inference APIs that need independent scaling |
Plans side by side
Ray Serve
Pricing not available.
Pros and cons
Themes from the AI answers that mention each brand
Langfuse
- Discounted cloud pricing for qualifying early-stage startups
- Well suited to evaluating and debugging LLM or agent behavior
Ray Serve
Profile in progress.
Data collected ·ChatGPT, Perplexity, Google AI Mode·coverage 100%·next run
Cite as: SlateIndex, “Langfuse vs Ray Serve”, https://slateindex.ai/compare/langfuse-vs-ray-serve, data of Nov 2, 2026. Licence: CC BY 4.0.