Pinecone

$70

/month for 1M vectors

+ OpenAI embedding costs
~$0.10 per 1M tokens
No hybrid search included
No self-host option

FluxVector

$29

/month for 1M vectors

Embeddings included free
8-signal HyperSearch included
100+ language support
Self-host available

Save $41+/mo compared to Pinecone — that's $492+/year

Feature Comparison

Feature	FluxVector	Pinecone
Built-in embeddings	Included free	No — requires OpenAI
Hybrid search (BM25 + vector)	8-signal HyperSearch	Basic sparse vectors only
Multilingual (100+ languages)	Native — built-in, server-side	Depends on your embedding model
Self-hosted option	Free forever (Docker)	No
Cold starts	None — always warm	Seconds on serverless
Pricing model	Flat monthly fee	Per-query + per-vector storage
Developer console	Built-in with playground	Yes
Metadata filtering	MongoDB-style operators	Yes
Free tier	10K vectors, no expiry	Limited, single index
Reranker	Built-in (5 options)	No
Confidence scoring	Yes — anti-hallucination	No
fine-grained matching	Yes	No
HyDE deep search	Yes (via Ollama)	No
API style	REST, one endpoint per action	REST + gRPC
SDKs	Python, TypeScript	Python, TypeScript, Go, Java
Local development	Same Docker image locally	No local option

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.

Pinecone + OpenAI

from pinecone import Pinecone
from openai import OpenAI

pc = Pinecone(api_key="pc_...")
openai = OpenAI(api_key="sk-...")

# Create index
pc.create_index("products", dimension=1536,
  metric="cosine", spec={...})
idx = pc.Index("products")

# Embed yourself, then upsert
emb = openai.embeddings.create(
  model="text-embedding-3-small",
  input=["Running shoes", "Hiking boots"]
)
idx.upsert(vectors=[
  ("1", emb.data[0].embedding, {}),
  ("2", emb.data[1].embedding, {}),
])

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

25 lines. Two API keys. Two bills.

Common Questions

Is FluxVector production-ready?

Yes. FluxVector runs on database with optimized vector indexes. It powers production workloads today. The same architecture that powers billion-row Postgres deployments worldwide.

Can I migrate from Pinecone?

Yes. Export your vectors from Pinecone, upsert them into FluxVector with the same IDs and metadata. If you were using OpenAI embeddings, you can either keep sending pre-computed vectors or let FluxVector re-embed your text server-side for free.

What embedding model does FluxVector use?

9 embedding models including multilingual-primary model, cross-lingual model, fine-grained matching, multilingual model, and Harrier — all via optimized inference runtime. They handle 100+ languages natively. You can also send your own pre-computed vectors if you prefer a different model.

What about Pinecone's gRPC support?

FluxVector uses REST exclusively. For most use cases, the latency difference between gRPC and HTTP/2 is negligible. If you need sub-5ms latency at scale, self-host FluxVector next to your application.

What about Pinecone's Namespaces?

FluxVector uses Collections, which serve the same purpose. Each collection has its own dimension, index, and metadata. You can create up to 10 on Pro, unlimited on Scale.

Can I self-host?

Yes. Same Docker image, same API. Pull it, run it, your data stays on your servers. minimal dependencies. Pinecone has no self-hosted option at any price.

FluxVector vs Pinecone

Ready to switch?