RAG Pipeline: Cost at 10K Queries/Day
What does it cost to run rag pipeline at 10K queries/day? Self-hosted dedicated GPU vs API provider pricing.
Monthly Cost Comparison at 10K queries/day
| Provider | Monthly Cost | Pricing Model | vs GigaGPU |
|---|---|---|---|
| GigaGPU (RTX 5080) | £109/mo | Fixed | — |
| OpenAI Embeddings + GPT-4o-mini | £280/mo | Per-queries | 61% cheaper with GigaGPU |
| Cohere Embed + Command | £240/mo | Per-queries | 55% cheaper with GigaGPU |
| Azure OpenAI | £310/mo | Per-queries | 65% cheaper with GigaGPU |
RAG Pipelines Hit Two API Bills Simultaneously
A RAG pipeline is not a single API call — it is at minimum an embedding lookup plus a generation call for every query. At 10,000 queries per day, that compounds quickly. OpenAI charges you for both the embedding and the GPT-4o-mini response, totalling £280/month. Azure adds its own markup, reaching £310/month.
A dedicated RTX 5080 at £109/month runs both the embedding model and the LLM on the same hardware. One fixed cost replaces two variable API bills. You also gain the ability to tune retrieval parameters, swap models, and iterate on your pipeline without worrying about per-query charges.
Annual savings potential: Up to £2,412 per year compared to the most expensive API option, assuming consistent 10K queries/day usage.
Self-Hosting Advantages for RAG
- End-to-end data privacy: Your documents, embeddings, and queries never leave your infrastructure. Essential for internal knowledge bases containing proprietary information.
- Iteration speed: Swap embedding models, adjust chunk sizes, tune retrieval parameters without any cost per experiment. API-based RAG penalises experimentation.
- Latency optimisation: Co-locating embeddings, vector search, and generation on the same server eliminates network round-trips between services.
- Model choice freedom: Use BGE, E5, or custom-trained embeddings paired with any LLM. No vendor lock-in to a specific embedding-generation combination.
When API-Based RAG Works Better
- Volume under 2K queries/day: Below this threshold, per-query API costs may undercut the fixed cost of dedicated hardware.
- Rapid prototyping: Testing RAG pipeline architectures before committing to production infrastructure.
- Managed vector databases: Services like Pinecone handle vector storage and indexing, which you would manage yourself on dedicated hardware.
Recommended Hardware
The RTX 5080 at £109/month has the VRAM to run both an embedding model and a 7B-parameter LLM concurrently, handling 10K queries/day with 20-30% burst headroom. Pre-configured with CUDA, Docker, and inference frameworks.
Run Your Entire RAG Pipeline for £109/Month
Embeddings, retrieval, and generation on one server — no per-query fees, no dual API bills.
