Key facts
| Managed alternative | Plugsky RAG collections with chunking, embedding, search and citations |
| Retrieval modes | Keyword, vector and hybrid search with optional reranking |
| Endpoints | POST /v1/rag/collections and POST /v1/rag/query |
| Embeddings | POST /v1/embeddings with plugsky-embed-v1 (1536d) and plugsky-embed-large (3072d) |
| 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
- Chroma optimises for developer speed and local persistence.
- Qdrant optimises for standalone deployment, filtering and scale.
- Both are open source; the real difference is operational shape, not ideology.
- Test deletes, filters and index rebuilds as carefully as search latency.
- If retrieval is not a core product, a managed RAG service is the shortest path.
How it works, step by step
- Prototype with the embedded option to validate chunking and queries quickly.
- Move to a standalone engine when multiple services or users need shared retrieval.
- Load a realistic corpus and measure recall with your own filter combinations.
- Test document deletion, re-indexing and backup restore paths.
- Decide whether your team wants to own upgrades, capacity and on-call for the store.
- If not, evaluate managed RAG collections before building the operational surface.
Try it yourself
Open the vector database comparison →
Where Chroma fits
Chroma is an open-source vector store with a deliberately small surface area. It can run embedded in a Python or JavaScript process with local persistence, or as a client-server deployment. Collections hold documents, embeddings and metadata, and queries support metadata filters. That makes it a strong fit for notebooks, prototypes, local RAG experiments and applications with modest corpus sizes.
The same simplicity becomes a constraint at scale. When several services need shared access, when filtering logic becomes complex, or when index maintenance and replication enter the requirements, teams typically graduate to a standalone engine or a managed service.
Where Qdrant fits
Qdrant is an open-source vector search engine written in Rust and designed for standalone deployment. It supports payload filtering with a typed query language, sparse vectors for hybrid retrieval, multiple distance metrics and index configurations, and it can be run in your own cloud, on-prem or through its managed offering.
That makes Qdrant a better default for production retrieval with shared services, multi-tenant filters and a growing corpus. The cost is operational: you deploy it, size it, back it up and upgrade it, and the team owns capacity planning when the index grows.
How the two compare in a RAG pipeline
In a RAG flow both stores play the same role: hold chunk embeddings plus metadata, return the top candidates for a query, then optionally rerank. Chroma's advantage is time to first query; Qdrant's is headroom and filtering depth as the system grows. Neither changes what your embedding model can find, so chunking and embedding quality still dominate answer quality.
Run the same corpus and question set through both, then measure recall at your target k, latency at realistic concurrency, and the effort to update or delete a document. Include access-control filters if your users see different slices of the same corpus, because that is where standalone engines usually pull ahead.
The managed route
If the vector store is infrastructure you would rather not own, Plugsky RAG collections provide ingestion, chunking, embedding, indexing and querying as a managed service. Documents are chunked and indexed automatically, queries support keyword, vector and hybrid retrieval with optional reranking, and every response returns ranked chunks with source attribution.
You can also keep Chroma or Qdrant and use Plugsky only for POST /v1/embeddings. Compare them with the vector database comparison, then start on the free plan with plugsky-micro and plugsky-lite. Current plans are on the live pricing page.
Honest comparison
| Factor | Chroma | Qdrant | Plugsky RAG collections |
|---|---|---|---|
| Deployment shape | Embedded or lightweight server | Standalone engine | Fully managed; VPC and on-prem available |
| Best fit | Prototypes and local apps | Production and multi-tenant retrieval | Teams that want answers, not infrastructure |
| Filtering | Metadata filters | Rich payload filtering | Metadata attached to documents |
| Hybrid search | Limited | Dense and sparse vectors | Keyword, vector and hybrid built in |
| Operations | Minimal at small scale | You own capacity and upgrades | Managed by default |
Frequently asked questions
Is Chroma or Qdrant better for beginners?
Chroma is usually faster to start with because it runs embedded with local persistence. Qdrant is not difficult either, but it assumes a standalone service to deploy and operate.
Can both handle metadata filtering?
Yes. Chroma supports metadata filters and Qdrant has a richer typed payload filtering system suited to multi-tenant and complex conditions.
Which scales further?
Qdrant is designed for standalone production scale. Chroma is commonly used for smaller corpora and graduation to another store happens as requirements grow.
Do I have to choose one of them?
No. Plugsky RAG collections provide managed ingestion and retrieval, and POST /v1/embeddings can feed either store if you prefer to keep your own 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.