RAG Pipeline: Cost at 100K Queries/Day
What does it cost to run rag pipeline at 100K queries/day? Self-hosted dedicated GPU vs API provider pricing.
Monthly Cost Comparison at 100K queries/day
| Provider | Monthly Cost | Pricing Model | vs GigaGPU |
|---|---|---|---|
| GigaGPU (2x RTX 5090) | £358/mo | Fixed | — |
| OpenAI Embeddings + GPT-4o-mini | £2800/mo | Per-queries | 87% cheaper with GigaGPU |
| Cohere Embed + Command | £2400/mo | Per-queries | 85% cheaper with GigaGPU |
| Azure OpenAI | £3100/mo | Per-queries | 88% cheaper with GigaGPU |
100K Queries/Day: Where API Costs Become Untenable
Three million RAG queries per month. That is the volume of a customer-facing knowledge base serving thousands of concurrent users, an enterprise search tool spanning millions of documents, or an AI assistant handling support tickets at scale.
At this volume, Azure OpenAI charges £3,100/month for the combined embedding and generation costs. A 2x RTX 5090 on GigaGPU runs the entire pipeline for £358/month — an 88% reduction. The £2,742 monthly gap compounds to nearly £33,000 in annual savings, and your 100,001st query costs nothing extra.
Annual savings potential: Up to £32,904 per year compared to the most expensive API option, assuming consistent 100K queries/day usage.
Why High-Volume RAG Demands Dedicated Infrastructure
- Rate limit elimination: At 100K queries/day, API rate limits become a production bottleneck. Dedicated hardware processes queries at GPU speed without artificial throttling.
- Full-stack control: Optimise chunking strategies, embedding dimensions, reranking models, and generation parameters as a unified system rather than stitching together separate API calls.
- Data governance: Three million queries per month over proprietary documents is an enormous surface area for data exposure. Self-hosting keeps every document, embedding, and response on your infrastructure.
- Cost-free experimentation: A/B test retrieval strategies, swap LLMs, adjust context windows — all without per-query charges inflating your experimentation budget.
Remaining API Use Cases
- Multi-region global deployment: If your users span six continents with strict latency requirements, managed APIs handle geographic routing natively.
- Overflow scaling: Use APIs for demand spikes beyond your dedicated capacity while GPU hardware handles the steady baseline.
- Rapid model evaluation: Quickly test whether a new embedding or LLM model improves retrieval quality before deploying it on your own hardware.
Recommended Configuration
A 2x RTX 5090 configuration at £358/month delivers the parallel processing power for 100K daily RAG queries with 20-30% burst headroom. Pre-configured with CUDA, Docker, and inference frameworks for production deployment.
Serve 100K RAG Queries/Day for £358/Month
Replace £3,100/month in API costs with a fixed-price GPU cluster that runs your entire retrieval-augmented generation pipeline.
