memista vs Pinecone vs Qdrant vs Chroma: Do You Need a Vector Database?

The question

Should I use memista, Pinecone, Qdrant, Weaviate, Milvus, or Chroma for vector search?

Vector search has a well-known zoo of options. The right answer is usually: none of them, you need SQLite. This post is the comparison we wish we had when we started memista.

The 60-second version: Pinecone, Qdrant, Weaviate, Milvus, and Chroma are server-based vector databases. memista is an embedded vector search library in pure Rust. For sub-100K vectors, memista (or any embedded option) is dramatically simpler. For million+ vectors with high QPS, a server is the right answer.

What each option is

memista is a SQLite-backed approximate nearest-neighbour search library in pure Rust. It runs in the same process as your application; no separate service to deploy. It uses HNSW + IVF for the ANN and SQLite for persistence. Targets sub-100K-vector corpora and prototype workloads.

Pinecone is the managed vector database. The original “vector DB as a service.” Fully managed, no infra. Used at scale by many production RAG systems. Closed source.

Qdrant is the open-source vector database. Written in Rust, self-hostable, with a focus on filtering and metadata. The de-facto open-source choice for production.

Weaviate is the open-source vector database with a strong GraphQL API and built-in vectorisation modules. Written in Go. The right choice if you want a single component for vectors + metadata + search API.

Milvus is the open-source vector database built for scale. Written in Go and C++, with distributed deployment options. The right choice for very large corpora.

Chroma is the open-source embedding database, written in Python. The default choice for prototyping in Python. The right choice for notebooks and scripts; not for production.

The five dimensions

Dimension	memista	Pinecone	Qdrant	Weaviate	Milvus	Chroma
Architecture	Embedded (in-process)	Managed service	Self-hosted server	Self-hosted server	Self-hosted cluster	Embedded or server
Language	Rust	(closed)	Rust	Go	Go + C++	Python
Storage	SQLite	Managed	RocksDB / Memory	Memory + disk	Memory + disk	SQLite / DuckDB
Max corpus size	~100K	Billions	Millions	Millions	Billions	~1M
ANN algorithm	HNSW + IVF	Proprietary	HNSW	HNSW	HNSW + IVF	HNSW
Filtering	Yes (SQL)	Yes	Yes (rich)	Yes (GraphQL)	Yes	Yes
Hybrid search	Yes (SQL + vector)	Yes	Yes	Yes	Yes	No
License	GPL-3.0	Proprietary	Apache-2.0	BSD-3	Apache-2.0	Apache-2.0
Operational complexity	None (it’s a library)	None (managed)	Medium	Medium	High	Low
Cost	Free	Pay-per-vector	Free (self-host)	Free (self-host)	Free (self-host)	Free
Production users	(early)	Many	Many	Many	Many	(prototyping)

When to use which

Use memista when:

Your corpus is < 100K vectors.
You want zero operational complexity: no separate service, no separate database, just a Rust library that links into your application.
You already use SQLite (and most applications do).
You want to do hybrid search (SQL filtering + vector similarity) in a single query.
You are prototyping and the corpus is not yet at scale.

Use Pinecone when:

You have a billion+ vector corpus and need a managed service.
You are willing to pay for the operational simplicity.
You do not need to self-host.

Use Qdrant when:

You want the de-facto open-source production vector DB.
You need rich metadata filtering.
You have an SRE team that can run a Rust service.

Use Weaviate when:

You want GraphQL-first.
You want built-in vectorisation modules.

Use Milvus when:

You need a distributed vector database at very large scale.
You have a platform team that can manage a complex deployment.

Use Chroma when:

You are prototyping in Python.
You are running in a notebook.

The SQLite argument

The case for memista is the same case for SQLite: most applications don’t need a separate database server. They need a database that runs inside their application.

For 95% of the RAG and semantic-search workloads we see in practice, the corpus is < 100K vectors. For those workloads:

Operational complexity of a separate service is 10x the engineering cost of a library.
The performance penalty of in-process SQLite is negligible at 100K vectors (~10ms p99 query).
Hybrid search (SQL + vector) is dramatically easier when they live in the same query.

If your corpus is < 100K vectors and you don’t have a specific reason to need a server, you don’t need Pinecone, Qdrant, or anything else. You need memista (or any embedded vector search library).

A 5-minute memista eval

# Cargo.toml
[dependencies]
memista = "0.1"
rusqlite = "0.31"

use memista::{Memista, HnswConfig, Vector};
use rusqlite::Connection;

let conn = Connection::open("vectors.db")?;
let memista = Memista::new(&conn, HnswConfig::default())?;

// Insert vectors
for chunk in &chunks {
    memista.insert(
        &chunk.id,
        &chunk.embedding,
        &chunk.metadata,  // arbitrary JSON
    )?;
}

// Query
let query_vector: Vector = embed("user query");
let results = memista.search(&query_vector, 10)?;
// results: Vec<(id: String, distance: f32, metadata: Value)>

That’s it. No server. No port. No Dockerfile. The SQLite file is the database; memista is the search.

When memista is the WRONG answer

If you have > 1M vectors and the search latency budget is < 50ms p99, you need a server (Qdrant, Milvus).
If you need distributed serving across many nodes, you need a server.
If you need a fully managed SLA, you need Pinecone.

For all of these, memista is the wrong tool. For everything else, it is the right one.