Pinecone vs Qdrant
A comparison of Pinecone and Qdrant built from values read directly from each provider's own documentation, with the source recorded against every figure.
The short answer
- 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.
3 sourced criteria separate them:
- Pricing model
- Pinecone: Usage-based on the Standard plan, with a $50/month minimum before pay-as-you-go charges apply.
- 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.
- Regions
- Pinecone: Starter plan is limited to us-east-1. Standard and Enterprise plans support all available regions across AWS, Azure and GCP.
- Qdrant: Standard and Premium tiers run on AWS, Azure and GCP.
- Metadata filtering
- Pinecone: Supported — Pinecone indexes metadata so a query can include a metadata filter to limit the search.
- Qdrant: Supported — collections store points (vectors with a payload) and support dedicated filtering on payload fields.
Pick the criteria you care about. The chart counts how many of them lean toward each provider — the same read as scanning the bars below, just totalled for the ones you chose.
Pinecone 0
Qdrant 0
At a glance.
| Criterion | Pinecone | Qdrant |
|---|---|---|
| Pricing | ||
| 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. |
| Infrastructure | ||
| 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. |
| Vector | ||
| 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.
- 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.
Sources (2) →Sources ↓
- Pinecone Pricing ↗
“You'll be charged a minimum of $50/month. Once your usage exceeds this amount, you'll pay as you go.”
Read 2026-09-11 · official pricing
- Qdrant Cloud Pricing ↗
“Billing is calculated based on actual resource usage during the billing period. You're charged for compute (vCPU), memory (GB), storage (GB) consumed by your clusters, storage (GB) consumed by backups, and used inference tokens of paid models.”
Read 2026-09-11 · official pricing
Regions
- Pinecone
- Starter plan is limited to us-east-1. Standard and Enterprise plans support all available regions across AWS, Azure and GCP.
- Qdrant
- Standard and Premium tiers run on AWS, Azure and GCP.
Sources (2) →Sources ↓
- Pinecone Pricing ↗
“Starter: us-east-1. Standard/Enterprise: All available regions — AWS, Azure, GCP.”
Read 2026-09-11 · official pricing
- Qdrant Cloud Pricing ↗
“AWS, Azure, GCP.”
Read 2026-09-11 · official pricing
Metadata filtering
- Pinecone
- Supported — Pinecone indexes metadata so a query can include a metadata filter to limit the search.
- Qdrant
- Supported — collections store points (vectors with a payload) and support dedicated filtering on payload fields.
Sources (2) →Sources ↓
- Pinecone documentation ↗
“Pinecone indexes metadata for filtering, so a query can include a metadata filter to limit the search.”
Read 2026-09-11 · official docs
- Qdrant documentation ↗
“Points, and vectors with a payload. [...] filtering is available as a search feature.”
Read 2026-09-11 · official docs
When these numbers change, hear about it. Sources are re-checked monthly; a repricing goes out as a short note.
Which should you choose?
Choose 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.
Editorial · Palash Bagchi · approved
Choose 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.
Editorial · Palash Bagchi · approved
Consider something else if…
- 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.
- 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.
These criteria are published by one provider and not the other. An absence here means we have not found a source, not that the feature is missing.
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.