FluxVector vs DIY OpenAI + vectors
You can wire OpenAI + a vector DB + a chat UI yourself. Many teams do. FluxVector is for teams that need multi-tenant chatbot RAG with refuse-when-unsure without owning that stack forever.
| Capability | DIY (OpenAI + Pinecone/pgvector + glue) | FluxVector |
|---|---|---|
| Embeddings | You pick, bill, and version models | Built-in e5 / bge (send text) |
| Retrieval | Usually 1–2 signals | 8-signal HyperSearch + TopK fusion |
| Multi-tenant isolation | Easy to get wrong | Fail-closed namespaces (gated) |
| Chat + sessions | You build | /v1/chat + stream |
| Anti-hallucination | Optional prompt hope | S4 + confidence floors + golden gates |
| Generation cost | Always API $ | Free Ollama path · BYOK when quality needed |
| Published price | OpenAI + vector + eng time | Free · Pro $29 · Scale $99 |
| Best when | One product, full platform eng team | Chatbots / SaaS embedding a brain |
When DIY is the right call
- You already run a mature retrieval platform and only need an ANN index.
- You need GPU-class ANN for non-chat ML workloads (that is not FluxVector’s sell line).
- You want every line of glue code in-house forever.
When FluxVector is the right call
- Customer-facing chatbots that must refuse instead of invent.
- Multi-tenant products (hotels, clinics, vertical SaaS) sharing one cluster.
- You want Free/Pro/Scale pricing without assembling Pinecone + LangChain + auth + evals.
Honest limits
FluxVector runs CPU-first today. Free-path generation is local Ollama, not frontier models. Isolation bugs existed historically and are fixed + gated — we do not claim a perfect past.
Try Free (10k vectors) or open docs