Key facts
| Managed option | Plugsky RAG collections with automatic chunking, embedding and indexing |
| Retrieval modes | Keyword, vector and hybrid search with optional reranking |
| Standalone vectors | POST /v1/embeddings works with Qdrant, Pinecone or any store |
| Citations | Every RAG query returns ranked chunks with source attribution |
| Data handling | Per-collection encryption at rest; API data is not used to train models |
| Deployment | Managed, VPC, on-prem and air-gapped options |
| Free tier | plugsky-micro and plugsky-lite, no card required |
| Product status | Live |
TL;DR
- Qdrant is software you operate; Pinecone is a service you consume.
- Qdrant fits residency and control requirements; Pinecone fits teams without ops capacity.
- Both support metadata filtering, so test filter performance on your own data.
- Cost curves differ: infrastructure versus usage-based pricing.
- Plugsky can provide retrieval or just embeddings, depending on your stack.
How it works, step by step
- State the primary constraint: data locality, operations capacity or time to production.
- Prototype with your corpus and embedding model on the leading candidate.
- Test filtered search, deletes and index rebuild time at realistic scale.
- Model three-year cost for infrastructure versus usage-based pricing.
- Confirm residency, backup and access-control requirements with security.
- Decide whether to own the store or adopt managed RAG collections.
Try it yourself
Open the vector database comparison →
Qdrant: the self-hosted engine
Qdrant is an open-source vector search engine with a strong filtering system, support for dense and sparse vectors, and configurable indexes and quantisation. You can run it in your own cloud, on-prem or in a private network, which is decisive when vectors and metadata are not allowed to leave your perimeter. It also offers a managed cloud if you want the same engine without operating it.
The cost is operational ownership: deployment, sizing, upgrades, backups and monitoring are yours. For teams with existing platform engineering, that is a reasonable trade for control. For teams without it, the store becomes another service competing for attention.
Pinecone: the managed service
Pinecone provides vector indexes as a managed API. You create an index, upsert vectors with metadata, and query with filters; scaling, replication and maintenance are handled by the vendor. Time to first query is short, and there is no cluster to size or patch.
The considerations are data path and cost model. Vectors and metadata reside in the vendor cloud under their terms, which may conflict with strict residency policies, and spend scales with usage rather than a fixed infrastructure line. For teams that value zero operations, that trade is often acceptable.
How to compare them fairly
Use one corpus, one embedding model and one question set. Measure recall at your target k, latency with your real filter combinations, and the time to rebuild the index from scratch. Include delete and update behaviour, because stale chunks are a common source of wrong answers and hard-to-debug incidents.
Then compare the whole cost of ownership: infrastructure and engineering for Qdrant versus usage-based spend for Pinecone. Neither number is universal, and both change with corpus size, query volume and retention policy.
The managed retrieval option
If you want the outcome without operating a vector database, Plugsky RAG collections ingest documents, chunk and embed them automatically, and answer queries with keyword, vector or hybrid retrieval, optional reranking and source attribution. If you prefer to keep your current store, use POST /v1/embeddings only and leave retrieval where it is.
Compare the architectures with the vector database comparison, then start on the free plan with plugsky-micro and plugsky-lite or the 14-day full-access trial. Current plans are on the live pricing page.
Honest comparison
| Factor | Qdrant | Pinecone | Plugsky RAG collections |
|---|---|---|---|
| Model | Open-source engine, self-host or managed cloud | Fully managed service | Managed retrieval with private deployment options |
| Data location | Your infrastructure by default | Vendor cloud | Your chosen deployment plane |
| Filtering | Rich typed payload filters | Metadata filters | Metadata attached to documents |
| Operations | You deploy and upgrade | Handled by the vendor | Handled by Plugsky |
| Cost shape | Infrastructure and people | Usage-based spend | Flat self-serve plans |
| Best for | Residency and control | Zero-ops teams | Answer quality over store ownership |
Frequently asked questions
Is Qdrant free to use?
The engine is open source, so there is no licence fee, but you pay for the infrastructure and engineering time needed to run it reliably.
Can Pinecone run on-premises?
It is a managed service rather than a self-hosted product. For on-prem or air-gapped requirements, choose a self-hosted engine or a private managed deployment.
Which has better filtering?
Both support metadata filtering. The practical answer depends on your filter complexity, index configuration and workload, so benchmark with your own combinations.
Can I use Plugsky with either database?
Yes. POST /v1/embeddings is standalone, so you can generate vectors with Plugsky and store them in Qdrant, Pinecone or another database.
Is there a free plan?
Yes. The free plan includes plugsky-micro and plugsky-lite with 2 API keys and no credit card, and a 14-day full-access trial is available.
How is pricing structured?
Self-serve plans are flat monthly with unlimited fair-use usage and no per-token charges or overage fees. See the live pricing page for current plans.