HNSW
Approximate nearest neighbour with high recall
DatabasesFrom $29/app/month
Managed Qdrant Hosting
Vector database for AI and similarity search
What is Qdrant on ManageStacks?
Qdrant on ManageStacks is the Rust-built open-source vector database deployed to your own AWS, Azure, or GCP region — priced flat at $29 per instance per month regardless of vector count or QPS, with HNSW indexing, payload filtering, quantization for memory efficiency, and distributed sharding. Materially cheaper than Pinecone or Weaviate Cloud at scale, and the embeddings that power your RAG/semantic-search pipelines stay in your cloud region.
Qdrant on ManageStacks is the Rust-built open-source vector database deployed to your own AWS, Azure, or GCP region — priced flat at $29 per instance per month regardless of vector count or QPS, with HNSW indexing, payload filtering, quantization for memory efficiency, and distributed sharding. Materially cheaper than Pinecone or Weaviate Cloud at scale, and the embeddings that power your RAG/semantic-search pipelines stay in your cloud region.
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About Qdrant
What Qdrant does, and why teams deploy it.
Qdrant is a purpose-built vector database designed for AI applications — semantic search, retrieval-augmented generation (RAG), recommendation systems, image similarity, and anomaly detection. It's written in Rust for performance and reliability, and it consistently ranks at or near the top of vector-search benchmarks for both latency and recall.
Key capabilities: HNSW-based approximate nearest neighbour indexing, payload storage for metadata (filter by tenant/date/category alongside vector similarity), scalar and product quantization for memory efficiency (fit more vectors in RAM), distributed mode with sharding and replication for scale + HA, and native integrations with LangChain, LlamaIndex, Haystack, and every major LLM framework.
Compared to using pgvector in Postgres, Qdrant is significantly faster and more memory-efficient at scale (10M+ vectors) and has richer vector-specific features (multiple named vectors per point, sparse+dense hybrid search, geo indexing). Compared to Pinecone (the market leader), Qdrant is open source and self-hostable — meaning your embeddings and your users' queries never touch a third-party.
DIY vs ManageStacks
What running Qdrant yourself looks like — and what it looks like with us.
DIY self-hosting
- Install Qdrant on a VM; tune HNSW parameters + quantization for your dataset
- Configure TLS, admin API keys, and payload indexes by hand
- Set up snapshot backups to object storage
- Build distributed cluster + replication yourself
- Track Qdrant releases + manage version upgrades
On ManageStacks
- Subscribe through your AWS, Azure, or GCP marketplace
- Qdrant comes up with HNSW, quantization, TLS, and monitoring
- Snapshot backups + collection cloning available in the dashboard
- Distributed mode with sharding + replication on Business+
- Rolling version upgrades handled by us
Qdrant on ManageStacks — key numbers
$29/mo
Flat per instance, unlimited vectors + queries
Rust
Consistently top-ranked in vector-DB benchmarks
Quantization
4-32x memory reduction for large collections
Key features
Everything Qdrant ships with, running on our stack.
- HNSW-based approximate nearest neighbour search with filtering
- Multiple named vectors per point (e.g. text + image + metadata)
- Payload storage — filter by any metadata field alongside vector search
- Scalar + product quantization for 4-32x memory reduction
- Sparse + dense hybrid search (BM25-style + semantic combined)
- Distributed mode with sharding and replication (Business+)
- gRPC + REST APIs; SDKs for Python, TypeScript, Go, Java, Rust
- Native integrations: LangChain, LlamaIndex, Haystack, DSPy
- Snapshot backups + collection cloning for zero-downtime migrations
- Full data export — snapshots are portable to any Qdrant instance
How it deploys
From subscribe to live in minutes.
Subscribe
Subscribe to ManageStacks through your AWS, Azure, or GCP marketplace.
Provision
Qdrant spins up with HNSW indexing, quantization, TLS, and Grafana monitoring — typically 3-5 minutes.
Create collection
Define your vector dimensions, distance metric, and payload schema. Configure quantization if needed for memory efficiency.
Index + query
Upsert vectors via SDK or REST. Query with vector + optional payload filter. Integrate with LangChain, LlamaIndex, or your own retrieval pipeline.
Who this is for
Built for teams that want Qdrant to just work.
RAG builders
You're building retrieval-augmented LLM apps. Qdrant is the retrieval layer; Ollama or OpenAI is the generation layer. On ManageStacks both live in your cloud region.
Semantic search for SaaS
You want "search for docs that mean X, not just contain X". Embed docs into Qdrant, embed queries at read time, retrieve top-k by cosine similarity.
Recommendation systems
User embeddings + item embeddings → nearest-neighbour lookup. Qdrant's filtering by payload lets you enforce business rules alongside vector similarity.
Compliance & compatibility
What we handle, what Qdrant runs on.
Compliance & operations
- TLS-encrypted API traffic with API-key or JWT authentication
- Daily encrypted snapshots stored in a separate region
- Payload-level access control via filter-scoped keys
- GDPR data-residency — deployment stays in your chosen cloud region
- OS-level and Qdrant security patches applied during your maintenance window
- Full data export via snapshot API
Compatibility
- Version
- Latest Qdrant stable (validated before release)
- Runtime
- Qdrant Rust binary on containerised infrastructure
- Dependencies
- Persistent storage for vectors; object storage for snapshots
- Min. resources
- 1 vCPU / 2 GB RAM (dedicated); scales with vector count
How ManageStacks helps
We handle the parts you shouldn't be writing yourself.
ManageStacks deploys Qdrant with HNSW indexing, quantization tuned to your memory budget, TLS between clients and cluster, snapshot-based backups, and Prometheus-based monitoring. On Business+ we handle distributed sharding and replication. Pair with Ollama for a fully self-hosted embed-and-retrieve RAG stack.
How it compares
Qdrant on ManageStacks vs the alternatives.
How Qdrant on ManageStacks compares to the market-leader vector DB and the two most-cited open-source alternatives.
| Property | Qdrant on ManageStacksUs | Pinecone | Weaviate Cloud | pgvector on Postgres |
|---|---|---|---|---|
| Deployment | Managed on your AWS, Azure, or GCP | Vendor-hosted (multi-cloud) | Vendor-hosted (multi-cloud) | Any Postgres deployment |
| Data residency | Your cloud region | Vendor region choice | Vendor region choice | Wherever your Postgres is |
| Pricing basis | Flat per instance | Per pod-hour + per RU/WU (serverless) | Per SUR-hour + per query | Postgres cost |
| Vector-DB-specific features | HNSW + quantization + hybrid + multi-vector | Rich vector-DB features | Schema-heavy + hybrid + generative modules | Basic vector search on relational schema |
| Open source | Yes (Apache 2.0) | No (proprietary) | Yes (BSD-3, hosted) | Yes (PostgreSQL licence) |
| Recall / performance | Top-tier | Very high | Very high | Good for < 1M vectors |
Comparison focuses on architectural properties (deployment model, pricing basis, open-source status) that don't change with vendor pricing pages. Verify current pricing on each vendor's own site.
FAQ
Common questions about Qdrant on ManageStacks.
How does this compare to Pinecone?
Pinecone is the market-leader vector DB, hosted only, priced per pod-hour + per read/write unit. ManageStacks Qdrant is flat $29 per instance regardless of vector count. For applications past a few million vectors, self-hosted Qdrant is materially cheaper. Pinecone wins on the serverless-scale story and vendor-managed SLA; Qdrant on cost predictability, data ownership, and self-hostability.
How does this compare to pgvector on Postgres?
pgvector is fine for small-scale vector workloads (< 1M vectors, simple use cases). Once you're past that, or if you need multi-vector-per-record, hybrid sparse+dense search, or aggressive quantization, Qdrant is materially better. For AI/ML production workloads, most teams end up on a dedicated vector DB. That said — if you're already on Postgres and just want a basic RAG lookup, pgvector avoids the operational overhead.
How does this compare to Weaviate?
Both are excellent open-source vector DBs. Qdrant has stronger raw performance in most benchmarks and a simpler operational model. Weaviate has richer schema and hybrid-search features baked in and stronger ML-model orchestration. Pick based on your team's specific needs — for straight vector search, Qdrant is often the pick; for schema-heavy hybrid AI apps, Weaviate.
How is Qdrant configured for large vector collections?
ManageStacks configures Qdrant with appropriate memory allocation, HNSW graph parameters (m, ef), quantization settings (scalar or product), and disk-backed storage for cold vectors. Business+ plans include distributed mode with sharding across nodes for collections beyond a single-node's memory.
Can I use Qdrant for RAG pipelines?
Yes — this is the primary use case. Qdrant + Ollama + LiteLLM + Dify (all on ManageStacks) is a complete self-hosted RAG stack. Or use Qdrant with cloud LLMs (OpenAI, Anthropic) via LangChain/LlamaIndex.
Does ManageStacks handle Qdrant version upgrades?
Yes. Qdrant releases frequently; we test each release, validate against a clone of your collections, and roll forward. Zero-downtime upgrades on replicated setups (Business+).
How is backup handled?
Native Qdrant snapshots (per-collection) with automated daily backups to encrypted object storage. Restore any snapshot to any Qdrant instance. Snapshot format is portable.
What if I want to move to Pinecone or Weaviate later?
Export collections via the snapshot API or re-embed into your target vector DB. Vectors are just floating-point arrays with metadata — portable to any store. Qdrant is Apache-2.0 licensed. Migration off is a supported operation.
Deploy Qdrant in under 5 minutes.
Subscribe through your AWS, Azure, or GCP marketplace. We handle provisioning, SSL, monitoring, backups, updates, and security. From $29/app/month.