# Turbopuffer vs Pinecone

Canonical: https://inetgeek.com/compare/turbopuffer-vs-pinecone/

Every value below is read from Turbopuffer's and Pinecone's own documentation. See https://inetgeek.com/methodology/ for how.

## At a glance

| Criterion | Turbopuffer | Pinecone |
| --- | --- | --- |
| Entry paid plan | Launch plan: $16/month minimum usage, including all database features. Scale plan: $256/month minimum. Enterprise: $4,096+/month. | Builder plan: $20/month flat, for solo developers and small teams. Standard plan is usage-based with a $50/month minimum. |
| Pricing model | Subscription with a monthly minimum per tier, plus usage-based overage charges beyond that minimum. | Usage-based on the Standard plan, with a $50/month minimum before pay-as-you-go charges apply. |
| Hybrid search (vector + keyword) | Supported — combines vector search and BM25 full-text search to produce semantically relevant results. | Supported — combine a keyword signal (BM25 or sparse) with a dense signal at query time, often with reranking. |

## Where they differ

### Entry paid plan

- Turbopuffer: Launch plan: $16/month minimum usage, including all database features. Scale plan: $256/month minimum. Enterprise: $4,096+/month. ([source](https://turbopuffer.com/pricing))
- Pinecone: Builder plan: $20/month flat, for solo developers and small teams. Standard plan is usage-based with a $50/month minimum. ([source](https://www.pinecone.io/pricing/))

### Pricing model

- Turbopuffer: Subscription with a monthly minimum per tier, plus usage-based overage charges beyond that minimum. ([source](https://turbopuffer.com/pricing))
- Pinecone: Usage-based on the Standard plan, with a $50/month minimum before pay-as-you-go charges apply. ([source](https://www.pinecone.io/pricing/))

### Hybrid search (vector + keyword)

- Turbopuffer: Supported — combines vector search and BM25 full-text search to produce semantically relevant results. ([source](https://turbopuffer.com/docs/hybrid-search))
- Pinecone: Supported — combine a keyword signal (BM25 or sparse) with a dense signal at query time, often with reranking. ([source](https://docs.pinecone.io/guides/indexes/understanding-indexes))

## Which should you choose?

Pick Turbopuffer if Teams that want vector search billed like storage rather than compute — object-storage-backed pricing tends to undercut memory-resident vector databases at rest, especially for large, cold datasets.

Pick Pinecone if Teams that want a fully managed vector search product with hybrid search and metadata filtering built in, and don't want to operate the underlying index themselves.

Consider something else: Turbopuffer — The full list of supported similarity metrics matters before committing — that wasn't fully documented in what's sourced here, unlike competitors that publish the complete list.

Consider something else: Pinecone — Cost predictability at small scale matters more than managed convenience — the $50/month Standard minimum applies before any pay-as-you-go usage starts.

## Documented by only one

- Free tier: Pinecone: Yes
- Regions: Pinecone: Starter plan is limited to us-east-1. Standard and Enterprise plans support all available regions across AWS, Azure and GCP.
- Similarity metrics supported: Turbopuffer: Cosine distance is documented as a supported metric (used in the SDK's example code); the full list of supported metrics wasn't confirmed beyond this.
- Metadata filtering: Pinecone: Supported — Pinecone indexes metadata so a query can include a metadata filter to limit the search.

## Questions this comparison answers

**Entry paid plan: Turbopuffer or Pinecone?**

Turbopuffer: Launch plan: $16/month minimum usage, including all database features. Scale plan: $256/month minimum. Enterprise: $4,096+/month.
Pinecone: Builder plan: $20/month flat, for solo developers and small teams. Standard plan is usage-based with a $50/month minimum.

**Should I choose Turbopuffer or Pinecone?**

Pick Turbopuffer if Teams that want vector search billed like storage rather than compute — object-storage-backed pricing tends to undercut memory-resident vector databases at rest, especially for large, cold datasets.
Pick Pinecone if Teams that want a fully managed vector search product with hybrid search and metadata filtering built in, and don't want to operate the underlying index themselves.