# Elastic Cloud vs Qdrant

Canonical: https://inetgeek.com/compare/elastic-cloud-vs-qdrant/

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

## At a glance

| Criterion | Elastic Cloud | Qdrant |
| --- | --- | --- |
| Free tier | No | Yes |
| Pricing model | Hosted: resource-based pricing, pay-as-you-go monthly or prepaid. Serverless: usage-based pricing, pay-as-you-go monthly or prepaid. | 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 | Hosted: 60 regions across AWS, Azure and Google Cloud (also Alibaba Cloud and FedRAMP). Serverless: generally available on AWS, Google Cloud and Azure. | Standard and Premium tiers run on AWS, Azure and GCP. |
| Metadata filtering | Supported — vector search results can be filtered by metadata while preserving approximate nearest neighbor (ANN) search speed. | Supported — collections store points (vectors with a payload) and support dedicated filtering on payload fields. |

## Where they differ

### Free tier

- Elastic Cloud: No ([source](https://www.elastic.co/pricing))
- Qdrant: Yes ([source](https://qdrant.tech/pricing/))

### Pricing model

- Elastic Cloud: Hosted: resource-based pricing, pay-as-you-go monthly or prepaid. Serverless: usage-based pricing, pay-as-you-go monthly or prepaid. ([source](https://www.elastic.co/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

- Elastic Cloud: Hosted: 60 regions across AWS, Azure and Google Cloud (also Alibaba Cloud and FedRAMP). Serverless: generally available on AWS, Google Cloud and Azure. ([source](https://www.elastic.co/pricing))
- Qdrant: Standard and Premium tiers run on AWS, Azure and GCP. ([source](https://qdrant.tech/pricing/))

### Metadata filtering

- Elastic Cloud: Supported — vector search results can be filtered by metadata while preserving approximate nearest neighbor (ANN) search speed. ([source](https://www.elastic.co/what-is/vector-search))
- 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 Elastic Cloud if Teams that need full-text search and observability on the same underlying engine, or that want the option to self-host later — Elastic's dual identity as a search platform and an observability platform is the actual differentiator against single-purpose competitors.

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: Elastic Cloud — The need is narrowly error tracking or APM — a purpose-built tool is usually simpler to operate than standing up an Elasticsearch cluster for one workload, and Elastic Cloud's per-resource pricing doesn't publish a flat entry price the way single-purpose competitors do.

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

- Self-hostable: Elastic Cloud: Supported
- 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): Elastic Cloud: Supported — combines vector similarity scores with BM25F text-relevance scores in a single hybrid query.

## Questions this comparison answers

**Free tier: Elastic Cloud or Qdrant?**

Elastic Cloud: No
Qdrant: Yes

**Should I choose Elastic Cloud or Qdrant?**

Pick Elastic Cloud if Teams that need full-text search and observability on the same underlying engine, or that want the option to self-host later — Elastic's dual identity as a search platform and an observability platform is the actual differentiator against single-purpose competitors.
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.