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MongoDB Atlas vs Qdrant

Two fact sheets from our own research, side by side — pricing, platforms, API access and status re-checked by us rather than quoted from the vendors.

The short answer

Free plan
Both have one
Public API
Both have one
Higher Trust score
MongoDB Atlas — 93 vs 88 for Qdrant
Best fit
MongoDB Atlas: Teams already on MongoDB adding RAG or semantic search · Qdrant: Teams building RAG or semantic search who want an open-source, self-hostable store
Status
Both up at our last check (Oct 2, 2026)

From our own records as of each tool's last check. Anything we have not confirmed is left out rather than guessed. How we verify

MongoDB AtlasManaged MongoDB cloud database with built-in vector search, hybrid search and Voyage AI embeddings for RAG and agent apps.QdrantOpen-source vector search engine written in Rust, available self-hosted or as a managed cloud for RAG and recommendation apps.

Side by side

FactMongoDB AtlasQdrant
CategoryVector Databases & RAGVector Databases & RAG
Pricing modelFreemiumFreemium
Public APIYesYes
Trust score93 / 10088 / 100
PlatformsWeb, APIWeb, API
CompanyMongoDB, Inc.Qdrant Solutions GmbH
Launched20162021
Last verifiedOct 2, 2026Oct 2, 2026

What our research says

MongoDB Atlas

A sensible choice if your application data already lives in MongoDB and you want vector and hybrid search next to it instead of syncing into a second system. It is a general-purpose database first, so it makes less sense as a standalone vector store for a team with no MongoDB footprint.

Pros

  • Vector, lexical and hybrid search run next to the operational data
  • Free cluster and an hourly Flex tier capped at $30/month
  • Runs on AWS, Azure and Google Cloud, with self-managed editions
  • Search nodes let vector workloads scale separately from the database

Cons

  • Hourly metered pricing makes monthly cost hard to forecast
  • Cost grows quickly on Dedicated clusters compared with single-purpose vector stores
  • Newer pieces such as Atlas Infinite and Atlas Agent Engine are still in preview

Qdrant

A mature, well-documented vector database that is easy to start with locally and scales to managed or private deployments. It is a strong pick when you want open-source control and fine-grained metadata filtering. Paid cost is usage-based and not quoted as a simple number, so estimate it with the vendor's calculator before committing.

Pros

  • Apache 2.0 engine with a free forever cloud tier
  • Native hybrid search with dense and sparse vectors
  • Deployable on your own servers, in Qdrant Cloud, or in your own cloud
  • SOC 2 and HIPAA compliance listed on the homepage

Cons

  • Standard and Premium cloud pricing is usage-based with no quoted starting price
  • The free tier is a single small node suited to prototypes only
  • Self-hosting means you handle scaling, backups and upgrades

Which one fits

Choose MongoDB Atlas if you need

  • Teams already on MongoDB adding RAG or semantic search
  • Developers who want embeddings, search and operational data under one query language
  • Startups that want a free cluster before paying for dedicated hardware

Choose Qdrant if you need

  • Teams building RAG or semantic search who want an open-source, self-hostable store
  • Applications that need heavy metadata filtering alongside vector similarity
  • Regulated companies that need to run managed clusters in their own cloud

MongoDB Atlas vs Qdrant: the questions people ask

Does MongoDB Atlas or Qdrant have a free tier?

Both: MongoDB Atlas has a free tier, and Qdrant has a free tier.

Which has a public API, MongoDB Atlas or Qdrant?

Both ship a public API, so either can be built on; the fact table above shows the platforms each one runs on.

More comparisons

All MongoDB Atlas alternatives →Browse all Vector Databases & RAG →