Qdrant vs Langfuse
A detailed comparison to help you choose between Qdrant and Langfuse.
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Verdict: Langfuse wins
Langfuse scores 4.3/5, edging out Qdrant at 4.1/5. 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.
Qdrant Vector database for semantic search and AI applications | Langfuse Open-source LLM observability and evaluation | |
|---|---|---|
| Rating | 4.1 (1 review) | 4.3 (1 review) |
| Pricing Model | freemium | freemium |
| Starting Price | Free tier available | Free tier available |
| Best For | Engineers building semantic search, recommendation systems or RAG retrieval layers where metadata filtering must not degrade search quality, and who want an Apache-2.0 core with a managed cloud escape hatch. | 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 | free tieropen sourceapi access | free tieropen sourceapi access |
| Visit Qdrant → | Visit Langfuse → |
Qdrant
Pros
- + Filtered ANN is the real differentiator: filters participate in the HNSW traversal, and in my testing recall under mandatory metadata filters stayed high where post-filter approaches collapsed.
- + Apache-2.0 core with a production-grade self-host path (Docker, Kubernetes, snapshots, replication) and client libraries for Python, Rust, Go, Java and TypeScript.
- + Free cloud tier is usable — 0.5 vCPU / 1 GB RAM / 4 GB disk, about one million 768-dim vectors, no credit card (qdrant.tech/cloud, at time of writing).
- + Quantization options (scalar, product, binary, asymmetric since 1.15) cut memory substantially with negligible recall loss in my testing when originals are kept for reranking.
- + Built-in inference API means applications can send raw text for embedding instead of managing a separate embedding service — older 'no built-in embeddings' complaints are out of date.
Cons
- - It is a retrieval engine, not a RAG platform: chunking, parsing, prompting and reranking are all your own assembly, even with the inference API.
- - Distributed deployments need real capacity planning — RAM is the binding constraint for HNSW working sets, and the pricing calculator will not compute that for you.
- - Free cloud clusters suspend after one week of inactivity, which catches demo and staging users off guard (qdrant.tech/cloud).
- - Per-hour cloud pricing means idle clusters keep billing; small dev clusters forgotten over a month cost as much as the entry paid tier.
- - Teams already running pgvector well may not justify a new stateful system — the case is strongest for filter-heavy, latency-critical search.
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, Qdrant or Langfuse?▼
Langfuse scores higher at 4.3/5 vs 4.1/5 for Qdrant, making it the better-rated option in our comparison.
What is the difference between Qdrant and Langfuse?▼
Qdrant: Qdrant is an open-source vector search engine written in Rust, used for semantic search and retrieval in AI applications. Langfuse: Langfuse is open-source observability for LLM applications, tracing calls, running evaluations and tracking cost and latency in production.
Is Qdrant or Langfuse more affordable?▼
Qdrant starts at €0/mo, Langfuse starts at €0/mo. Check the comparison table above for current pricing.
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