Hardware sized for RAM
A vector DB is RAM-bound — the HNSW index lives in memory for fast search. Enterprise NVMe for persistence with no performance loss.
Vector DB · Qdrant · Weaviate · pgvector
Vector database hosting to power your AI agent.
Qdrant, Weaviate, Milvus or pgvector pre-installed on a Brazilian server with enterprise NVMe and generous RAM. Corporate RAG, semantic search, similarity recommendation — no per-vector cost, data in your environment.
- No per-vector cost
- Billing in BRL
- LGPD-ready
- No vendor lock-in
Why a Vector DB managed by Rollin Host
Your choice of stack
Qdrant (most popular for RAG), Weaviate (with GraphQL), Milvus (billions scale), pgvector (Postgres extension) or ChromaDB. You choose.
No per-vector charge
Pinecone charges per dimension × stored vector. Self-hosted: you pay for the server and index as much as you want. Predictable cost at volume.
Data in your environment
Embeddings, payloads and metadata stay in your storage. No routing through third parties. LGPD compliant from day 1.
Managed Vector DB plans
AMD EPYC hardware + NVMe + installation + maintenance. One-time setup of R$ 259 on every plan.
VDB Start
Up to ~1 million vectors
R$ 259/month
Sign up via WhatsApp + setup R$ 259 (one-time) · no lock-in
- 4 vCPU AMD EPYC
- 16 GB RAM
- 200 GB NVMe
- Stack of your choice (Qdrant, Weaviate, Milvus, pgvector)
- Daily snapshot · 7-day retention
- Monitoring dashboard
- 24/7 Brazilian support
VDB Pro
Up to ~50 million vectors
R$ 599/month
Sign up via WhatsApp + setup R$ 259 (one-time) · no lock-in
- 8 vCPU AMD EPYC
- 32 GB RAM
- 500 GB NVMe
- Stack of your choice (Qdrant, Weaviate, Milvus, pgvector)
- Daily snapshot · 7-day retention
- Monitoring dashboard
- 24/7 Brazilian support
VDB Scale
500 million+ vectors
R$ 1.199/month
Sign up via WhatsApp + setup R$ 259 (one-time) · no lock-in
- 16 vCPU AMD EPYC
- 64 GB RAM
- 1 TB NVMe
- Stack of your choice (Qdrant, Weaviate, Milvus, pgvector)
- Daily snapshot · 7-day retention
- Monitoring dashboard
- 24/7 Brazilian support
Payment via Pix, bank slip or card (up to 12x). Above 500M vectors or special requirements (multi-zone HA, encrypted dataset, etc.) — quote on request.
Use cases
Corporate RAG
Index your internal wiki (Notion, Confluence, GitBook), policies, technical docs. The agent answers with company context.
Semantic search
Replace keyword search (slow, frustrating) with semantic search over a product catalog, ticket base or FAQ.
Recommendation
"Similar products", "related articles", "similar candidates" — any recommendation by vector similarity.
Duplicate detection
Find duplicate leads in the CRM, plagiarized posts, similar images. Adjustable similarity threshold.
Long-term memory for agents
Open Claw and other always-on agents use a vector DB to "remember" past conversations and each user’s context.
Multi-label classification
Categorize tickets, emails, leads or content automatically by comparing with already-classified samples.
Frequently asked questions
What is a vector database?
A database specialized in storing and searching vectors (embeddings) — numerical representations of text, image or audio generated by AI models. It enables semantic similarity search at scale (RAG, recommendation, classification).
Which tools do you install?
You choose: Qdrant (most popular, great for RAG), Weaviate (with GraphQL and modules), Milvus (billions of vectors scale), pgvector (PostgreSQL with a vector extension) or ChromaDB. We install the latest stable version, optimally configured for your case.
Why self-hosted instead of Pinecone?
Predictable cost (you pay for the server, not per stored vector), full data control (important for the LGPD), zero vendor lock-in (you can export and migrate whenever you want) and low latency (Brazilian server vs. Pinecone US).
How do I size the plan?
Rule of thumb: 1M OpenAI vectors (1536 dim) consume ~6 GB of RAM. VDB Start (16 GB) covers up to 1-2M; Pro (32 GB) covers up to 50M with an HNSW index; Scale (64 GB) goes up to 500M+. When in doubt, talk to our team to size it case by case.
Does it work with OpenAI, Anthropic, Cohere?
Yes, any embedding model. You generate the embeddings with whichever API you prefer (OpenAI, Cohere, Anthropic, Voyage, local BERT/MiniLM models) and store them in your vector DB. Search queries also use the model of your choice.
Is there automatic backup?
Yes, an automatic daily snapshot with 7-day retention. Point-in-time restore on demand. Backup on a separate S3-compatible storage for geographic resilience.
How long does delivery take?
Provisioning within 3 business days after approval: server setup, installation of the chosen vector DB, authentication and TLS configuration, monitoring dashboard and a 1h onboarding with your team.
Can I upgrade the plan later?
Yes. Migration between tiers is done in an agreed window (usually 30-60 min of downtime). You pay proportional to usage and there is no penalty.
Pronto pra hospedar seu projeto de IA?
Comece em 5 minutos. Migração gratuita, suporte 24/7 em português e garantia de reembolso em 7 dias.
