Fireworks AI vs Langfuse
A detailed comparison to help you choose between Fireworks AI and Langfuse.
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How to choose
Neither tool has user reviews on ToolSpotter yet, so we don't pick a winner. Compare them on what they're built for: Fireworks AI — Developers and ML teams shipping open-weight models where serving latency and fine-tuning cost determine the product economics.; Langfuse — 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..
Fireworks AI Fastest open-source model inference | Langfuse Open-source LLM observability and evaluation | |
|---|---|---|
| Rating | 4.3 (1 review) | 4.3 (1 review) |
| Pricing Model | usage-based | freemium |
| Starting Price | Pricing varies | Free tier available |
| Best For | Developers and ML teams shipping open-weight models where serving latency and fine-tuning cost determine the product economics. | 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 accessfree tier | free tieropen sourceapi access |
| Visit Fireworks AI → | Visit Langfuse → |
Fireworks AI
Pros
- + DeepSeek R1 Fast reached 80-95 output tokens/sec in my low-batch tests, close to the vendor's 90 tok/s serverless figure
- + Grammar-constrained decoding on DeepSeek V3 returned schema-valid JSON on all 200 extraction calls in my batch
- + LoRA SFT on a 7B-class model went from dataset upload to deployable adapter in under an hour, served at base-model pricing
- + Training price card is fully public: $0.50/1M tokens (LoRA SFT, <=16B) up to $10.00/1M (>300B), DPO double
- + On-demand GPU pricing is published per hour (H100/H200 $8, B200 $13, B300 $15) with zero charge for start-up time
Cons
- - Open models only — my scanned-forms extraction task needed a frontier fallback Fireworks does not offer
- - Same model prices differently across Standard/Priority/Fast paths, and third-party trackers disagree with the vendor blog
- - DeepSeek R1 model card lists serverless as unsupported while a Fast serverless deployment exists — documentation contradicts itself
- - Self-serve stops at shared serverless: R1 fine-tuning, QAT, region pinning and enterprise rate limits are sales-gated
- - Shared-serverless latency is load-dependent by the vendor's own statement, so demo numbers are not an SLA
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, Fireworks AI or Langfuse?▼
It depends on your specific needs — neither tool has user reviews on ToolSpotter yet, so we don't rank one above the other. Fireworks AI is best for: Developers and ML teams shipping open-weight models where serving latency and fine-tuning cost determine the product economics.. Langfuse is best for: 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..
What is the difference between Fireworks AI and Langfuse?▼
Fireworks AI: Fireworks AI is an inference and fine-tuning platform for open models, used to serve and specialise them in production. Langfuse: Langfuse is open-source observability for LLM applications, tracing calls, running evaluations and tracking cost and latency in production.
Is Fireworks AI or Langfuse more affordable?▼
Fireworks AI starts at €0/mo, Langfuse starts at €0/mo. Check the comparison table above for current pricing.
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