Qdrant Cloud

~$25

/month for 1GB starter

+ bring your own embeddings
Configure vector schemas
Understand payload indexing
Per-node cloud pricing

FluxVector

$29

/month for 1M vectors

Embeddings included free
Hybrid BM25 + vector search
100+ language support
Self-host available

Same price, zero complexity — embeddings included, hybrid search by default

Feature Comparison

Feature	FluxVector	Qdrant
Built-in embeddings	Included free	No — bring your own
Hybrid search (BM25 + vector)	Yes, with RRF fusion	Yes, but requires sparse vector setup
Multilingual (100+ languages)	Native — multilingual-e5-large	Depends on your model
Self-hosted option	Free forever (Docker)	Yes (Docker/Helm)
Cold starts	None — always warm	None
Pricing model	Flat monthly fee	Per-node cloud pricing
Developer console	Built-in with playground	Yes (web UI)
Metadata filtering	MongoDB-style operators	Yes (payload filters)
Free tier	10K vectors, no expiry	1GB free cloud, 14-day trial
API style	REST, one endpoint per action	REST + gRPC
SDKs	Python, TypeScript	Python, JS, Rust, Go, Java
Local development	Same Docker image locally	Yes (Docker)

Code Comparison — Semantic Search

FluxVector

from fluxvector import FluxVector

fv = FluxVector(api_key="fv_live_...")

# Create collection
fv.collections.create("products", dimension=1024)

# Upsert — just send text
fv.vectors.upsert("products", [
  {"id": "1", "text": "Running shoes"},
  {"id": "2", "text": "Hiking boots"},
])

# Search
results = fv.search("products", "comfortable shoes")

12 lines. Zero ML setup.

Qdrant + OpenAI

from qdrant_client import QdrantClient
from qdrant_client.models import Distance, VectorParams, PointStruct
from openai import OpenAI

qd = QdrantClient(url="https://xxx.cloud.qdrant.io", api_key="...")
openai = OpenAI(api_key="sk-...")

# Create collection with schema
qd.create_collection("products", vectors_config=VectorParams(
    size=1536, distance=Distance.COSINE))

# Embed yourself, then upsert
emb = openai.embeddings.create(
    model="text-embedding-3-small",
    input=["Running shoes", "Hiking boots"])
qd.upsert("products", points=[
    PointStruct(id=1, vector=emb.data[0].embedding, payload={}),
    PointStruct(id=2, vector=emb.data[1].embedding, payload={}),
])

# Embed query, then search
q = openai.embeddings.create(
    model="text-embedding-3-small", input="comfortable shoes")
results = qd.query_points("products",
    query=q.data[0].embedding, limit=10)

24 lines. Schema config. Two API keys.

Common Questions

Is FluxVector as fast as Qdrant?

For most use cases, yes. Both use HNSW indexes. FluxVector runs on PostgreSQL/pgvector which handles millions of vectors. Qdrant may edge ahead at 100M+ vectors, but 99% of use cases won't hit that.

Can I migrate from Qdrant?

Yes. Export your vectors and metadata, upsert into FluxVector. If you were using OpenAI for embeddings, you can drop that dependency entirely.

What about Qdrant's Rust performance?

Qdrant is written in Rust for raw speed. FluxVector prioritizes DX — you send text, we handle everything. For the vast majority of apps, the bottleneck is the network, not the vector engine.

Does Qdrant have better filtering?

Qdrant's payload filtering is powerful. FluxVector supports MongoDB-style operators ($eq, $gt, $in, etc.) which covers most use cases. Qdrant supports more complex nested conditions.

Can I self-host both?

Yes. The difference: with FluxVector, embeddings are built in. With Qdrant, you still need an external embedding service.

FluxVector vs Qdrant

Ready to switch?