Pinecone stores your index. ManyVector stores it in your bucket.
Pinecone is SaaS-only: your vectors live on their infrastructure, in their format, with their storage markup on top of per-query fees. ManyVector writes directly to your S3, GCS, or R2 bucket — your data stays in your cloud account, self-hostable, with no per-vector storage markup and a drop-in OpenAI-compatible API.
$0 markup
no per-vector storage fee on top of cloud rates
Your bucket
your data, your cloud account
Drop-in
OpenAI-compatible API
ManyVector vs Pinecone, side by side.
Feature
ManyVector
Pinecone
Storage model
✓ Object storage (S3 / GCS / R2) — your bucket
✗ Pinecone-managed cloud storage
Pricing
✓ Per-query pricing + S3/GCS/R2 storage rates — no per-vector markup, no proprietary storage fee
✗ ~$0.33/1M vectors/month proprietary storage fee + read units per query (~$0.08–0.10/1M RUs)
Data sovereignty
✓ Data never leaves your cloud account
✗ Data stored on Pinecone infrastructure
Self-hosting
✓ Single binary — run on any VM, container, or Kubernetes
✗ SaaS only — no self-hosted option
Namespace branching
✓ Copy-on-write instant branching built in
✗ Not available
Full-text search
✓ BM25 inverted index included
✗ Not included
Hybrid search
✓ BM25 + vector with RRF fusion
✓ Sparse-dense hybrid available (proprietary format)
OpenAI-compatible API
✓ Drop-in /v1/vectors endpoint
✗ Proprietary Pinecone SDK required
Setup
✓ Connect your bucket, deploy binary — ready in minutes
✓ Managed — create index via console or API
Why teams switch from Pinecone.
Cost at scale
Query fees scale with usage.
Pinecone charges read units per query, scaled by vector dimensions. At high query volume — common in production RAG or recommendation workloads — monthly bills grow independently of data size. ManyVector has no per-query fee.
Deployment
No self-hosting option.
Pinecone is SaaS-only. Teams with data-residency requirements, air-gapped environments, or a preference for infrastructure control have no path to self-hosted Pinecone. ManyVector ships as a single binary with no external dependencies.
Portability
Vendor lock-in via proprietary API.
Pinecone uses its own SDK and index format. Migrating away requires rewriting client code and re-indexing all vectors. ManyVector exposes an OpenAI-compatible API so client libraries work without modification.
Common questions
Is ManyVector API-compatible with Pinecone?
ManyVector exposes an OpenAI-compatible vectors API rather than a Pinecone-compatible one. If your code uses the OpenAI client to upsert or query vectors, it works against ManyVector without changes. Code written against the Pinecone SDK needs to be updated to use the standard OpenAI client or ManyVector's own client library — typically a one-day migration.
How much can I save switching from Pinecone?
Savings depend on your query volume and vector count. Teams with high query-to-data ratios save the most because Pinecone's read-unit fees no longer apply. A workload with 10M vectors and 50M queries/month can reduce its vector-database bill by 60–80% by paying only S3 GET and list rates. Use our cost calculator at /resources/calculator to model your specific workload.
Does ManyVector support sparse vectors?
Yes. ManyVector stores sparse vectors natively in the inverted index alongside the BM25 term index. You can pass a sparse vector directly in the upsert payload and include it in hybrid queries via RRF fusion — the same fusion approach Pinecone uses for its sparse-dense hybrid mode.
How long does migration take?
For most teams: one to two days. The main steps are exporting vectors from Pinecone (via the fetch API or an export script), re-indexing into a ManyVector namespace backed by your bucket, and updating client code to point at the new endpoint. ManyVector provides a migration guide in the docs and pre-built export scripts for common Pinecone index sizes.