Meilisearch 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: Meilisearch — 96 vs 88 for Qdrant
Best fit: Meilisearch: Teams that want keyword and semantic retrieval from one index instead of two systems · Qdrant: Teams building RAG or semantic search who want an open-source, self-hostable store
Platforms: Meilisearch: Web, Linux · Qdrant: Web

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

MeilisearchOpen-source search engine with vector storage and hybrid keyword-plus-semantic retrieval, self-hosted or run as a cloud service

QdrantOpen-source vector search engine written in Rust, available self-hosted or as a managed cloud for RAG and recommendation apps.

Side by side

Fact	Meilisearch	Qdrant
Category	Vector Databases & RAG	Vector Databases & RAG
Pricing model	Freemium	Freemium
Starts at	$20/mo	—
Public API	Yes	Yes
Trust score	96 / 100	88 / 100
Platforms	Web, API, Linux	Web, API
Company	Meili	Qdrant Solutions GmbH
Launched	2018	2021
Last verified	Sep 6, 2026	Oct 2, 2026

What our research says

Meilisearch

Meilisearch is the pragmatic middle between bolting pgvector onto Postgres and running an Elasticsearch cluster: hybrid retrieval works out of the box and the open-source licence means the cloud bill is a convenience, not a lock-in. The limit is scope — it is a retrieval layer, not a full RAG framework, and there is no permanently free cloud tier.

Pros

Open source engine you can self-host, so the managed service is optional
Hybrid retrieval blends lexical and vector search in a single query
Documented integrations with LangChain and LlamaIndex
Cloud entry at $20/month with usage- or resource-based billing and a card-free 14-day trial

Cons

No permanently free cloud tier — the free option is self-hosting and running it yourself
Retrieval only; chunking, prompt assembly and generation stay your problem
SLAs, SSO SAML and SOC 2 sit behind quoted enterprise plans

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 Meilisearch if you need

Teams that want keyword and semantic retrieval from one index instead of two systems
Products needing typo-tolerant instant search with an escape hatch to self-hosting
Engineers wiring a retrieval layer under LangChain or LlamaIndex

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