Weaviate

$25

/month serverless starting price

Vectorizer modules required
GraphQL + REST API
Class schemas & property definitions
14-day free sandbox only

FluxVector

$29

/month for 1M vectors

Embeddings included free
Pure REST API
No schemas, no modules
Free tier forever

Similar price, fraction of the complexity — REST not GraphQL, free tier forever

Feature Comparison

Feature	FluxVector	Weaviate
Built-in embeddings	Included free	Yes, via vectorizer modules (complex config)
Hybrid search (BM25 + vector)	Yes, with RRF fusion	Yes (BM25 + vector)
Multilingual (100+ languages)	Native — multilingual-e5-large	Via text2vec modules
Self-hosted option	Free forever (Docker)	Yes (Docker/Helm)
Cold starts	None — always warm	Seconds on serverless
Pricing model	Flat monthly fee	Serverless + Enterprise tiers
Developer console	Built-in with playground	Yes
Metadata filtering	MongoDB-style operators	Yes (where filters)
Free tier	10K vectors, no expiry	14-day sandbox only
API style	REST, one endpoint per action	GraphQL + REST
SDKs	Python, TypeScript	Python, JS, 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.

Weaviate

import weaviate
from weaviate.classes.config import Configure, Property, DataType
from weaviate.classes.query import MetadataQuery

client = weaviate.connect_to_weaviate_cloud(
    cluster_url="https://xxx.weaviate.network",
    auth_credentials=weaviate.auth.AuthApiKey("..."))

# Create class with vectorizer module
client.collections.create("Products",
    vectorizer_config=Configure.Vectorizer.text2vec_openai(),
    properties=[
        Property(name="text", data_type=DataType.TEXT),
    ])

# Insert objects (vectorizer embeds automatically)
col = client.collections.get("Products")
col.data.insert_many([
    {"text": "Running shoes"},
    {"text": "Hiking boots"},
])

# Search
results = col.query.near_text(
    query="comfortable shoes",
    limit=10,
    return_metadata=MetadataQuery(distance=True))
client.close()

25 lines. Class schemas. Module config. GraphQL under the hood.

Common Questions

Weaviate has built-in vectorizers too — how is FluxVector different?

Weaviate requires configuring vectorizer modules, setting up class schemas with property definitions, and uses GraphQL for queries. FluxVector is pure REST — send text to one endpoint, get results. No schemas, no modules, no GraphQL.

Can I migrate from Weaviate?

Yes. Export your objects, upsert the text into FluxVector. Since FluxVector embeds server-side, you don't need to replicate Weaviate's vectorizer module configuration.

What about Weaviate's generative modules?

Weaviate integrates with LLMs for RAG directly in the DB. FluxVector focuses on search — you bring your own LLM. This keeps your architecture flexible and avoids vendor lock-in on the generation side.

Weaviate has a free sandbox — why does FluxVector's free tier matter?

Weaviate's sandbox expires after 14 days. FluxVector's free tier (10K vectors) never expires. You can prototype, test, and even run small production workloads indefinitely.

Is GraphQL better than REST?

GraphQL gives you flexible query shapes. But for vector search, you're always doing the same operation: send text/vector, get results. REST is simpler, more cacheable, and every developer already knows it.

FluxVector vs Weaviate

Ready to switch?