Key facts
| Managed option | Plugsky RAG collections with automatic chunking, embedding and indexing |
| Retrieval modes | Keyword, vector and hybrid search with optional reranking |
| Citations | Every query returns ranked chunks with source attribution |
| Standalone vectors | POST /v1/embeddings works with any vector database |
| Data handling | Per-collection encryption at rest; API data is not used to train models |
| Deployment | Managed, VPC, on-prem and air-gapped options |
| Formats | PDF, DOCX, TXT, MD and HTML ingestion |
| Product status | Live |
TL;DR
- Pick the alternative by reason: control, cost model, residency or operational load.
- Open-source engines give self-hosting; Postgres extensions keep one database.
- Migration is mostly a re-embedding exercise, not just moving vectors.
- Dual-run old and new indexes and compare retrieved chunks before cutover.
- Plugsky can be the RAG service or just the embeddings provider.
How it works, step by step
- Write down the specific limitation that motivates the move: cost, residency, filtering or ops.
- Shortlist two alternatives that address that limitation directly.
- Re-embed a representative sample with the target stack and rebuild a test index.
- Compare recall, filtered queries and deletes against the current deployment.
- Plan a dual-run window where both systems receive the same queries.
- Cut over, keep the old system for rollback, then decommission it deliberately.
Try it yourself
Open the vector database comparison →
The main classes of alternative
Alternatives fall into four groups. Open-source engines such as Qdrant, Weaviate and Milvus can run in your own cloud or on-prem and give full control over data and configuration. Embedded stores such as Chroma are simple for prototypes and small corpora. Postgres extensions such as pgvector keep vectors next to relational data with SQL filtering and transactions.
Managed RAG services such as Plugsky collections remove the vector database from your architecture altogether: you upload documents and query them, and the service handles chunking, embedding, indexing and retrieval. The right class follows from the constraint you care about most.
Migration is a re-embedding project
Vectors are only meaningful inside the model that produced them, so moving stores often means re-embedding. Export the source text and metadata, not just the vectors, and keep the document identifier stable so citations and permissions still resolve. Plan batch sizes around the embeddings endpoint limits: Plugsky accepts up to 2,048 inputs per request with a maximum of 8,191 tokens per input.
Build the new index in parallel while the old one serves traffic. Then dual-query both with real questions and compare the chunks retrieved, not only the final answers. This is also the moment to fix accumulated chunking problems, since you are reindexing anyway.
What to verify before committing
Check the operational details that demos hide: filtered search under load, deletion semantics, index rebuild time for your corpus size, backup and restore, and how access control maps to your tenant model. If residency matters, confirm where vectors and metadata are stored and whether a private deployment is available.
Cost deserves the same scrutiny. A usage-based service and a self-hosted engine have different cost curves as the corpus grows, and re-embedding a large corpus has its own one-time price. Model both at your expected scale instead of extrapolating from a small test.
Why teams choose Plugsky
Plugsky can replace either layer. Use RAG collections to get managed ingestion and retrieval with citations, or use POST /v1/embeddings alone and keep your preferred vector store. Self-serve plans are flat monthly with no per-token billing, and 30+ models sit behind the same OpenAI-compatible API, so the generation layer can change without touching retrieval.
Compare architectures with the vector database comparison, then start free with plugsky-micro and plugsky-lite or the 14-day full-access trial. Current plans are on the live pricing page.
Honest comparison
| Alternative | Hosting | Operations | Best for | Data path |
|---|---|---|---|---|
| Qdrant, Weaviate, Milvus | Your cloud or on-prem | You run and upgrade | Control and residency | Stays in your infrastructure |
| Chroma | Embedded or small server | Minimal | Prototypes and local apps | Local by default |
| pgvector | Your PostgreSQL | Standard database ops | Postgres-centric teams | Inside your database |
| Plugsky RAG collections | Managed or private deployment | None for retrieval | Answers without store ownership | Your chosen deployment |
| Plugsky embeddings only | Managed | None for vectors | Keep your existing store | Vectors returned to you |
Frequently asked questions
Can I migrate from Pinecone without downtime?
Yes, with a parallel-index approach: build the new index, dual-query both systems, and switch traffic when retrieval quality matches or improves.
Do I need to re-embed my documents?
If the new provider uses a different embedding model, yes. Vectors from different models are not comparable and cannot share an index.
Are open-source vector databases free?
The software licences are open source, but you still pay for the servers, storage and engineering time to run them reliably.
Can Plugsky replace Pinecone entirely?
For many RAG workloads, yes: collections store, index and retrieve documents with citations. Teams with an existing store can also use Plugsky embeddings only.
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.