Cloud Use Case

RAG Pipeline & Vector Database Hosting

Host retrieval-augmented generation pipelines with pgvector, Weaviate, Qdrant, or ChromaDB on dedicated cloud servers. LangChain and LlamaIndex ready.

$4/mo

Starting price

Global Data Centers

99.9%

Uptime SLA

24/7

Human Support

Why Host RAG on OMC Cloud

RAG (Retrieval-Augmented Generation) combines your proprietary data with LLM intelligence. It requires a vector database for embeddings, compute for the retrieval pipeline, and optionally a GPU for the LLM inference layer. Managed RAG services lock you in and charge per query.

Self-host your entire RAG stack on OMC Cloud: PostgreSQL + pgvector for embeddings, LangChain or LlamaIndex for orchestration, and optionally a GPU instance for local LLM inference. Full control over your data, your pipeline, and your costs.

Key Benefits

Full Stack Control

Own your entire RAG pipeline — embeddings, retrieval, generation.

pgvector & Weaviate

Run any vector DB: pgvector, Weaviate, Qdrant, ChromaDB, Milvus.

LangChain Ready

Install LangChain, LlamaIndex, Haystack, or custom orchestration.

Data Privacy

Your documents and embeddings stay on your server. No third-party access.

NVMe Performance

Fast vector similarity search on NVMe-backed storage.

Optional GPU

Add GPU for local LLM inference alongside your RAG pipeline.

Auto Backups

14 daily restore points for your embedding database.

Scale on Demand

Start small, add resources as your knowledge base grows.

How It Works

Choose

Select data center, GPU/CPU, RAM, storage, and OS.

Deploy

Server ready in under 60 seconds via console or API.

Go Live

Install your stack, configure, launch with 24/7 support.

Cloud vs On-Premise vs Shared

Feature	OMC Cloud	On-Premise	Shared
Upfront Cost	None — from $4/mo	$5,000-50,000+	$5-20/mo
Performance	Dedicated NVMe	Dedicated but fixed	Shared
Scaling	Instant	Weeks	Limited
Control	Full root access	Full	Very limited
Uptime	99.9% SLA	Depends on you	95-99%
Backups	Automated, 14 points	DIY	Basic
Global Reach	24 data centers	Single location	Shared

Recommended Configurations

RAG pipeline configurations — CPU for retrieval, optional GPU for inference.

RAG Starter

$8/mo

per month

• 2 vCPU, 4 GB RAM
• 40 GB NVMe
• pgvector or ChromaDB
• External LLM API

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RAG Production

$32/mo

per month

• 4 vCPU, 16 GB RAM
• 160 GB NVMe
• Weaviate or Qdrant
• LangChain orchestration

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RAG + Local LLM

$89/mo

per month

• NVIDIA L40S + CPU
• 8 vCPU, 32 GB RAM
• 200 GB NVMe
• Self-hosted LLM inference
• Full air-gapped RAG

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Technical Specifications

Vector DBs: pgvector, Weaviate, Qdrant, ChromaDB, Milvus

Orchestration: LangChain, LlamaIndex, Haystack

Embeddings: OpenAI, Cohere, or self-hosted (e5, BGE)

LLM: Optional GPU for local Llama/Mistral

Storage: NVMe for fast vector search

CPU: Up to 104 vCPU

RAM: Up to 512 GB (important for large indices)

Backup: 14 daily restore points

Frequently Asked Questions

What vector databases can I run?+

Any: pgvector (PostgreSQL extension), Weaviate, Qdrant, ChromaDB, Milvus, Pinecone alternative. Full root access.

Do I need a GPU for RAG?+

Not necessarily. The retrieval pipeline runs on CPU. You only need GPU if you want local LLM inference instead of an external API.

How much data can I index?+

Depends on RAM and storage. 4 GB RAM handles ~1M vectors. 32 GB handles ~10M+. NVMe storage scales to terabytes.

Can I use LangChain?+

Yes. Install LangChain, LlamaIndex, Haystack, or any Python framework. Full root access.

Is my data private?+

Completely. Self-hosted RAG means your documents and embeddings never leave your server.

How does this compare to managed RAG services?+

No per-query charges, no vendor lock-in, full data privacy. You control the entire stack.

Related Use Cases

LLM Inference

Local LLM for your RAG pipeline

Database Hosting

PostgreSQL + pgvector

AI Agents

RAG-powered AI agents

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