Vector search with built-in embeddings. No ML pipeline, no vector wrangling. Hybrid BM25 + vector search across 100+ languages.
# Search in Spanish, find English results curl -X POST /v1/vectors/query \ -H "Authorization: Bearer $FV_KEY" \ -H "Content-Type: application/json" \ -d '{"collection": "products", "text": "zapatos comodos para correr"}' # Response {"results": [ {"id": "1", "text": "Trail running shoes", "score": 0.91}, {"id": "2", "text": "Waterproof hiking boots", "score": 0.84} ], "took_ms": 42}
Create a collection, upsert text, query. Embeddings happen server-side. No OpenAI key. No ML pipeline.
# Store English text curl -X POST .../v1/vectors/upsert \ -H "Authorization: Bearer $FV_KEY" \ -H "Content-Type: application/json" \ -d '{ "collection": "products", "vectors": [ {"id": "1", "text": "Trail running shoes"}, {"id": "2", "text": "Waterproof hiking boots"}, {"id": "3", "text": "Casual leather sandals"} ] }' # Search in Spanish curl -X POST .../v1/vectors/query \ -H "Authorization: Bearer $FV_KEY" \ -H "Content-Type: application/json" \ -d '{"collection": "products", "text": "zapatos comodos para correr"}'
{"upserted": 3}
// Embeddings generated server-side
// No OpenAI key needed
{"results": [
{"id": "1",
"text": "Trail running shoes",
"score": 0.91},
{"id": "2",
"text": "Waterproof hiking boots",
"score": 0.84}
], "took_ms": 42}
import requests BASE = "https://fluxvector.dev/v1" H = {"Authorization": f"Bearer {KEY}"} # Store English text requests.post(f"{BASE}/vectors/upsert", json={ "collection": "products", "vectors": [ {"id": "1", "text": "Trail running shoes"}, {"id": "2", "text": "Waterproof hiking boots"}, {"id": "3", "text": "Casual leather sandals"}, ] }, headers=H) # Search in Spanish r = requests.post(f"{BASE}/vectors/query", json={ "collection": "products", "text": "zapatos comodos para correr", }, headers=H) print(r.json())
// Embeddings generated server-side // No OpenAI key needed {"upserted": 3} {"results": [ {"id": "1", "text": "Trail running shoes", "score": 0.91}, {"id": "2", "text": "Waterproof hiking boots", "score": 0.84} ], "took_ms": 42}
const BASE = "https://fluxvector.dev/v1"; const h = { Authorization: `Bearer ${KEY}` }; // Store English text await fetch(`${BASE}/vectors/upsert`, { method: "POST", headers: h, body: JSON.stringify({ collection: "products", vectors: [ { id: "1", text: "Trail running shoes" }, { id: "2", text: "Waterproof hiking boots" }, { id: "3", text: "Casual leather sandals" }, ], }), }); // Search in Spanish const r = await fetch(`${BASE}/vectors/query`, { method: "POST", headers: h, body: JSON.stringify({ collection: "products", text: "zapatos comodos para correr", }), }); const data = await r.json();
// Embeddings generated server-side // No OpenAI key needed {"upserted": 3} {"results": [ {"id": "1", "text": "Trail running shoes", "score": 0.91}, {"id": "2", "text": "Waterproof hiking boots", "score": 0.84} ], "took_ms": 42}
Stored in English. Searched in Spanish. Found the right result. No configuration.
Semantic search without the complexity, the surprise bills, or the vendor lock-in.
Send text, not vectors. We embed with multilingual-e5-large server-side. No OpenAI calls. No ML pipeline.
BM25 full-text + vector similarity + Reciprocal Rank Fusion. Better results than vector-only, out of the box.
Store in Spanish, query in English. Multilingual model handles cross-lingual retrieval natively.
Monthly fee, that's it. Not per-query, per-embedding, per-dimension. Know what you pay before you start.
Same Docker image, same API. Your data stays in PostgreSQL, not a proprietary black box. Run it yourself forever.
Always warm. No serverless spin-up delays. Sub-second responses from the first query.
No asterisks.
| FluxVector | Pinecone | Qdrant | |
|---|---|---|---|
| Built-in embeddings | Included free | Add-on (extra cost) | No |
| Hybrid BM25 + vector | Yes + RRF | Basic sparse | Yes |
| Self-hosted | Free forever | No | Yes |
| Cold starts | None | Seconds (serverless) | None |
| 100+ languages | Native | Depends on your model | Depends on your model |
| Pricing model | Flat monthly | Per-query + storage | Free cloud + paid tiers |
| Developer console | Built-in | Yes | Yes |
| Free tier | 10K vectors, no expiry | Limited (single index) | 1GB free cloud |
Start free. Upgrade when you need to. Self-host if you prefer.
Self-hosted is free forever. Same API, same features, your infra.
curl -X POST https://fluxvector.dev/v1/vectors/query \ -H "Authorization: Bearer $FV_KEY" \ -H "Content-Type: application/json" \ -d '{"collection": "docs", "text": "your first query"}'
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