Self-Hosted Embeddings vs OpenAI Embeddings API: Cost

Embeddings are the hidden cost multiplier in every RAG pipeline. Every document you index, every query you run, and every re-ranking step generates embedding API calls. Running embedding models on GigaGPU dedicated GPU servers eliminates per-token billing for embeddings entirely. This guide shows exactly when self-hosting saves money.

OpenAI’s text-embedding-3 models are popular but far from the only option. Open-source embedding models like BGE, E5, and GTE match or exceed OpenAI’s quality on retrieval benchmarks — and they run on modest hardware through open-source hosting.

OpenAI Embeddings API vs Self-Hosted Embedding Models

OpenAI text-embedding-3-small charges $0.02 per 1M tokens. Text-embedding-3-large charges $0.13 per 1M tokens. Self-hosted models like BGE-large-en or E5-large-v2 run on a single RTX 5090, processing thousands of documents per second with batch inference. The monthly cost is fixed regardless of how many tokens you embed.

For a comprehensive comparison of GPU vs API pricing across model types, see our cost per 1M tokens: GPU vs OpenAI breakdown.

Cost at 10M to 10B Tokens

OpenAI’s embedding prices are low per-token, but embeddings are a volume game. RAG pipelines re-embed on every index update, every query, and every re-ranking pass. Volumes compound quickly.

Break-Even Analysis

Against text-embedding-3-small ($0.02/1M), break-even occurs at approximately 10B tokens per month. Against text-embedding-3-large ($0.13/1M), break-even drops to approximately 1.5B tokens per month. For RAG systems that re-index frequently or handle large document corpora, these thresholds are crossed quickly.

The real cost, however, is rarely embeddings alone. Most RAG pipelines combine embeddings with vector search and LLM generation — all of which benefit from self-hosting. See our self-hosted RAG vs OpenAI Assistants cost comparison for the full pipeline economics. Our break-even guide covers the general methodology.

Savings at Scale

For teams running embeddings alongside vector databases, pair self-hosted embeddings with ChromaDB or Qdrant on the same server for a fully self-contained RAG stack.

Best Self-Hosted Embedding Models

The top open-source embedding models for self-hosting include BGE-large-en-v1.5 (1024 dimensions, MTEB leader), E5-large-v2 (strong multilingual support), GTE-large (fast inference, good quality), and nomic-embed-text (long context, 8192 tokens). All run efficiently on a single RTX 5090 with room to spare for concurrent workloads.

For teams evaluating the full self-hosted search stack, our replace Pinecone with self-hosted vector DB guide covers the database layer. See also the cheapest GPU for inference to optimise your hardware spend.

When to Self-Host Your Embeddings

If you use text-embedding-3-large and process more than 1.5B tokens per month, self-hosting is cheaper. If you use the small model, the threshold is higher at 10B tokens — but most serious RAG deployments exceed that. Self-hosting also eliminates API latency for embeddings, which directly improves search and retrieval response times.

Deploy your embedding stack on GigaGPU dedicated servers alongside your vector database and LLM for maximum efficiency. Use our LLM Cost Calculator to model the full pipeline cost, or explore our best Pinecone alternatives for vector database options.