Running a production RAG pipeline on OpenAI APIs — embedding with text-embedding-3-large and generating with GPT-4o — costs approximately $4,200 per month at 50,000 queries per day. A fully self-hosted stack on a single RTX 6000 Pro 96 GB can deliver the same workload for under $650 per month. Here is every cost component, line by line.
The Five Cost Layers of a RAG Pipeline
A RAG system is not a single model — it is an assembly line. Each stage carries its own compute, storage, and operational cost. The five layers are: document ingestion and chunking, embedding generation, vector storage and search, context retrieval and reranking, and LLM generation. Skipping the cost analysis on any layer leads to budget surprises. Understanding the cost per million tokens at each stage is essential.
Self-Hosted RAG Cost Breakdown (50K Queries/Day)
| Component | Tool/Model | Resource | Monthly Cost |
|---|---|---|---|
| Embedding Model | BGE-large-en-v1.5 | Shared GPU (RTX 5090) | $45 |
| Vector Database | Qdrant (self-hosted) | 32GB RAM, 500GB SSD | $80 |
| Reranker | BGE-reranker-v2-m3 | Shared GPU (RTX 5090) | $35 |
| LLM Generation | Llama 3.1 70B (Q4) | RTX 6000 Pro 96 GB dedicated | $420 |
| Document Ingestion | LangChain + Unstructured | 4-core CPU server | $40 |
| Storage (documents) | MinIO / S3-compatible | 1TB block storage | $25 |
| Total | $645 |
Pricing reflects GigaGPU dedicated hosting rates. Embedding and reranker share a single GPU timeslice.
API-Based RAG Cost for Comparison
| Component | API Provider | Unit Cost | Monthly Cost (50K q/day) |
|---|---|---|---|
| Embedding | OpenAI text-embedding-3-large | $0.13/1M tokens | $290 |
| Vector DB | Pinecone (Standard) | $70/month base | $280 |
| LLM Generation | GPT-4o | $2.50/$10 per 1M tokens | $3,600 |
| Total | $4,170 |
The self-hosted stack saves approximately $3,525 per month — an 85% reduction. The break-even analysis shows self-hosting becomes cheaper at around 3,000 queries per day for this configuration.
Scaling Costs: From 10K to 500K Queries per Day
Self-hosted RAG scales linearly in hardware, not in token cost. At 10K queries per day, a single budget GPU running a quantised 7B model handles everything for under $200 per month. At 500K queries per day, you need multi-GPU clusters — typically 4x RTX 6000 Pros for the LLM layer plus a separate embedding server — totalling approximately $2,100 per month. The API equivalent at that volume exceeds $40,000 per month.
Use the LLM cost calculator to model your specific query volume against both approaches.
Optimising Each Cost Layer
The LLM generation layer consumes 65-75% of total self-hosted RAG cost. Optimise it first. Running vLLM with continuous batching improves throughput by 3-4x. Smaller context windows (sending only the top 3 retrieved chunks instead of 10) reduce token consumption by 50% with minimal quality loss. On the embedding side, quantised embedding models like BGE-small run on CPU for low-volume ingestion, eliminating GPU cost entirely for that layer.
Deploy Your RAG Stack on GigaGPU
A self-hosted RAG pipeline on GigaGPU dedicated servers gives you full control over every component, zero per-token charges, and data that never leaves your infrastructure. Start with open-source LLM hosting for the generation layer and scale as your query volume grows.
Compare your current API spend against self-hosted costs with the GPU vs API cost comparison tool. For regulated industries requiring data isolation, explore private AI hosting. Find more pipeline cost analyses on the cost blog.
