Qdrant

App in the BluixApps catalog

What it is

Qdrant is a high-performance vector database written in Rust, designed for AI-powered search and recommendation at production scale. Open-source (Apache 2.0), single-binary deployment, gRPC + REST APIs, with hybrid (dense + sparse) search, payload filtering, and quantization for memory efficiency.

It's the backbone of RAG pipelines that need to scale beyond toy projects — million-vector collections, sub-100ms p99 latencies, horizontal sharding.

What it's for

  • RAG retrieval at scale — embed your knowledge base, retrieve top-k passages for LLM context
  • Semantic search — replace keyword search on docs, products, support tickets
  • Recommendation systems — find similar items, users, content via vector similarity
  • Multi-modal search — image, text, audio embeddings co-located in one collection
  • Anomaly detection — outlier detection via vector distance thresholds

Who it's for

  • AI engineers building production RAG and semantic search beyond proof-of-concept scale
  • ML platform teams replacing Pinecone with self-hosted Qdrant for sovereignty + per-month cost predictability
  • E-commerce engineering powering "find similar items" / personalized recommendations on millions of SKUs
  • Search teams upgrading keyword-only to hybrid (dense + BM25) for relevance gains without re-indexing
  • Researchers & academics working with multi-million vector datasets and needing reproducible local infra

Why teams pick Qdrant over alternatives

  • Rust performance — sub-10ms query latency on million-vector collections
  • Hybrid search — dense + sparse (BM25-style) combined natively
  • Payload filtering — pre-filter by metadata before similarity, no Python re-scoring
  • Quantization — INT8 + binary encoding cuts RAM 32× with minimal recall loss
  • First-class clients — Python, JS, Rust, Go, Java, .NET, all type-safe
  • Apache 2.0 — no commercial restrictions
  • Snapshot + restore built into the binary

Integrations

  • Client libraries — typed SDKs for Python, JS, Rust, Go, Java, .NET, PHP, Ruby
  • LLM frameworks — LangChain, LlamaIndex, Haystack, Semantic Kernel ship Qdrant adapters
  • Embedding providers — OpenAI, Cohere, Hugging Face, sentence-transformers, FastEmbed (built into Qdrant)
  • Streaming ingestion — Apache Kafka / Pulsar via custom workers
  • Backup — snapshot to local disk or S3-compatible object storage
  • Observability — Prometheus metrics endpoint, distributed tracing via OpenTelemetry
  • Protocols — gRPC (fast) + REST (universal); both auth-protected with API key

Notable users & community

  • 20k+ GitHub stars
  • Used by Disney, Visa, Bayer, X (Twitter), and many AI startups for production retrieval
  • Strong Discord, monthly community calls, active engineering blog
  • Common pairing with Flowise, AnythingLLM, n8n in self-hosted AI stacks
  • Backed by Qdrant company (DE-based) — strong European OSS company with sustainable open-core model

Tips & operations

  • Enable quantizationquantization_config.scalar.type=int8 cuts RAM 4×, binary cuts 32× with <2% recall loss
  • Create payload indexes before bulk insertcreate_payload_index on filter fields speeds queries 10× post-insert
  • Run with replicas=2 even on a single VPS — protects against snapshot/data corruption without cross-node setup
  • Snapshot weekly to S3 — built-in /snapshots endpoint + cron + S3 upload = cheap off-site backup
  • Use FastEmbed for built-in embedding — runs inside Qdrant; saves an external OpenAI Embeddings API round-trip
  • Mind sharding above 10M vectors — single collection limits exist; design with shard_number from the start

What we ship in BluixApps

  • Docker compose: Qdrant single-node (cluster mode available for Enterprise tier)
  • Pinned qdrant/qdrant:v1.13.0, weekly upstream tracking
  • API key auth enabled by default (random key shown in install report)
  • Persistent storage volume at /qdrant/storage for collections + snapshots
  • gRPC + REST both exposed; HTTPS via Let's Encrypt on REST endpoint
  • Pairs naturally with Flowise / AnythingLLM / n8n on same VPS for one-click RAG stack
  • Backup hook captures storage volume + snapshot exports
Read this app's deep dive on bluix.app ↗

Get this app — pick a BluixApps plan

Same catalog. Scaling tenant isolation, white-label and support tier.

TierTenantsCatalogSupportWhite-labelMonthly
Stacks119 curated stacksStandard$19/moDetailDeploy
Starter10Full catalogStandard+$15–25/mo$49/moDetailDeploy
Pro25Full catalogPriority bugfix+$15–25/mo$149/moDetailDeploy
Growth100Full catalogPriority bugfix+$15–25/mo$349/moDetailDeploy
Scale500Full catalog7-day window+$15–25/mo$799/moDetailDeploy
EnterpriseUnlimitedFull catalogPriority 7-dayBundled$1,499/moDetailDeploy

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