# Pinecone vs Qdrant

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

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

## At a glance

| Criterion | Pinecone | Qdrant |
| --- | --- | --- |
| Free tier | Yes | Yes |
| Pricing model | Usage-based on the Standard plan, with a $50/month minimum before pay-as-you-go charges apply. | Usage-based on the Standard tier: billed for compute (vCPU), memory (GB) and storage (GB) consumed by clusters, backup storage, and inference tokens for paid models. |
| Regions | Starter plan is limited to us-east-1. Standard and Enterprise plans support all available regions across AWS, Azure and GCP. | Standard and Premium tiers run on AWS, Azure and GCP. |
| Metadata filtering | Supported — Pinecone indexes metadata so a query can include a metadata filter to limit the search. | Supported — collections store points (vectors with a payload) and support dedicated filtering on payload fields. |

## Where they differ

### Pricing model

- 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/))
- Qdrant: Usage-based on the Standard tier: billed for compute (vCPU), memory (GB) and storage (GB) consumed by clusters, backup storage, and inference tokens for paid models. ([source](https://qdrant.tech/pricing/))

### Regions

- Pinecone: Starter plan is limited to us-east-1. Standard and Enterprise plans support all available regions across AWS, Azure and GCP. ([source](https://www.pinecone.io/pricing/))
- Qdrant: Standard and Premium tiers run on AWS, Azure and GCP. ([source](https://qdrant.tech/pricing/))

### Metadata filtering

- Pinecone: Supported — Pinecone indexes metadata so a query can include a metadata filter to limit the search. ([source](https://docs.pinecone.io/guides/indexes/understanding-indexes))
- Qdrant: Supported — collections store points (vectors with a payload) and support dedicated filtering on payload fields. ([source](https://qdrant.tech/documentation/concepts/collections/))

## Which should you choose?

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.

Pick Qdrant if Teams that want a choice of similarity metric rather than one fixed default, or that want the option to self-host the same open-source engine the managed cloud runs.

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.

Consider something else: Qdrant — A published, calculator-free entry price matters — Qdrant Cloud's Standard tier pricing is usage-based with no static per-month figure quoted, unlike some competitors' flat entry plans.

## Documented by only one

- Entry paid plan: Pinecone: Builder plan: $20/month flat, for solo developers and small teams. Standard plan is usage-based with a $50/month minimum.
- Similarity metrics supported: Qdrant: Dot product, Cosine similarity (implemented as dot-product over automatically normalized vectors), Euclidean distance, and Manhattan distance.
- Hybrid search (vector + keyword): Pinecone: Supported — combine a keyword signal (BM25 or sparse) with a dense signal at query time, often with reranking.

## Questions this comparison answers

**Should I choose Pinecone or Qdrant?**

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.
Pick Qdrant if Teams that want a choice of similarity metric rather than one fixed default, or that want the option to self-host the same open-source engine the managed cloud runs.