Anyscale vs Langfuse
A detailed comparison to help you choose between Anyscale and Langfuse.
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Verdict: Langfuse wins
Langfuse scores 4.3/5, edging out Anyscale at 3.7/5. Engineering teams running LLM apps or agents in production who want framework-agnostic tracing and evaluation with either a cheap managed cloud or a free self-hosted stack.
Anyscale Run Llama and open models at scale | Langfuse Open-source LLM observability and evaluation | |
|---|---|---|
| Rating | 3.7 (1 review) | 4.3 (1 review) |
| Pricing Model | usage-based | freemium |
| Starting Price | Pricing varies | Free tier available |
| Best For | ML engineering teams already fluent in Ray running distributed training, data processing and batch inference at production scale — particularly on BYOC with existing GPU reservations and negotiated rate protection. | Engineering teams running LLM apps or agents in production who want framework-agnostic tracing and evaluation with either a cheap managed cloud or a free self-hosted stack. |
| Free Tier | ||
| API Access | ||
| Team Features | ||
| Open Source | ||
| Tags | api access | free tieropen sourceapi access |
| Visit Anyscale → | Visit Langfuse → |
Anyscale
Pros
- + Workload code is standard Ray (Train, Serve, Data, RLlib) — in my testing the same scripts ran on local OSS Ray and on Anyscale, so the portability claim is real in both directions
- + Hosted compute is published per ACU-hour (CPU at $0.0135/hr, T4 at $0.5682/hr, A100 at $4.9591/hr at time of writing) with a $100 starter credit and launchable project templates from $3
- + BYOC deployment into your own VPC on any cloud or on-prem, with support for existing GPU reservations and marketplace invoicing
- + Built by the Ray creators; the managed layer (autoscaling, job control plane, RayTurbo runtime) wraps rather than replaces the open-source surface
- + Launched on Microsoft Azure as a native integration in June 2026, widening sovereign-AI deployment options
Cons
- - The Nscale acquisition (agreed July 30, 2026, expected to close in H2 2026) puts pricing posture, packaging and roadmap through 2027 in the blast radius of an integration
- - Anyscale Endpoints (per-token LLM inference) was sunset January 14, 2025 — there is no model catalog or per-token API left, contrary to the old catalog tagline
- - Top-end GPU rates (H100/H200/Blackwell) were withdrawn from the rate card in September 2026 into a contact-sales row, so the largest training budgets are now all negotiated
- - Ray expertise is a prerequisite — there is no low-code path, and teams new to Ray face a training investment before any product value
- - Support is sharply tiered: Hosted gets business-hours coverage with 5 case submissions; 24x7 SLAs require the BYOC enterprise path
Langfuse
Pros
- + MIT-licensed core with free, unrestricted self-hosting — only named enterprise features like audit logs and SCIM need a licence key
- + A 5-seat team at 100k traces runs roughly $69/month versus about $420/month for the main closed alternative (Panoply's independent estimate, consistent with my bills)
- + OpenTelemetry-native tracing across OpenAI SDK, LangChain, LlamaIndex, Pydantic AI, Vercel AI SDK, CrewAI and raw OTLP
- + Graduated usage pricing is transparent and cheap: worked examples on the pricing page match my own invoices (200k units on Core = $37/month)
- + Dataset-backed evaluation with the langfuse/experiment-action GitHub step let me gate prompt changes in CI on a real test set
Cons
- - v3 self-hosting needs Postgres, ClickHouse, Redis and S3 where v2 ran on one container — a four-service system to operate
- - Heavy agent traces render slowly in the UI (issue threads #10391 and #9933 match the multi-second loads I measured)
- - Online evaluations score a sample of traffic rather than everything, so rare failure modes can go unscored
- - UI-configured evaluators cannot be triggered programmatically for scheduled pipelines in my setup
- - Owned by ClickHouse, Inc. since January 2026 — licence unchanged so far, but the vendor-independence profile shifted
Frequently Asked Questions
Which is better, Anyscale or Langfuse?▼
Langfuse scores higher at 4.3/5 vs 3.7/5 for Anyscale, making it the better-rated option in our comparison.
What is the difference between Anyscale and Langfuse?▼
Anyscale: Anyscale runs distributed AI workloads on Ray, covering training, data processing and serving across any cloud. Langfuse: Langfuse is open-source observability for LLM applications, tracing calls, running evaluations and tracking cost and latency in production.
Is Anyscale or Langfuse more affordable?▼
Anyscale starts at €0/mo, Langfuse starts at €0/mo. Check the comparison table above for current pricing.
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