Deploy Embedding Models with Hugging Face Inference Endpoints
Hugging Face
Hugging Face now lets you deploy embedding models straight to production via Inference Endpoints. Bonus: it's reportedly 64x cheaper than OpenAI's embedding API.
Based on reporting by Hugging Face — read the original for the full story.
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Embeddings are the quiet workhorses behind most retrieval-augmented generation setups — the systems that let a chatbot search your PDFs or a semantic search bar actually understand what you meant instead of just matching keywords. Hugging Face just made it considerably easier to put open-source embedding models into production, pairing its Inference Endpoints service with a new backend called Text Embeddings Inference, or TEI.
TEI is purpose-built for serving embedding models fast. It skips model graph compilation, boots quickly thanks to small Docker images, batches tokens dynamically, and leans on Flash Attention and cuBLASLt for optimized inference. Hugging Face says it supports all ten of the top models on the MTEB leaderboard, including BGE, GTE, E5, and Ember — basically the models people actually reach for when building serious retrieval systems.
The numbers Hugging Face is citing are the real hook here. Running BAAI/bge-base-en-v1.5 on a single Nvidia A10G, with 512-token sequences and a batch size of 32, they measured throughput above 450 requests per second. That works out to roughly $0.00156 per million tokens — or $0.00000156 per thousand — which the company claims is 64 times cheaper than OpenAI's embedding API at $0.0001 per thousand tokens. Even accounting for benchmark cherry-picking, that's a big enough gap to matter for anyone running embeddings at scale, where costs compound fast across millions of documents.
Getting a model running is mostly point-and-click. You pick a repo — BAAI/bge-base-en-v1.5 in Hugging Face's example — choose a cloud and region, and the platform suggests hardware sized to the model. Swapping to an A10G GPU instance gets you the benchmarked performance, though heavier GPU tiers may require requesting quota first. Deployment takes one to three minutes, after which the endpoint is live and can be tested through a built-in widget or hit directly with cURL, Python, or JavaScript snippets that Hugging Face auto-generates for you.
Batching is where the real efficiency gains show up. TEI accepts multiple inputs in a single request, and Hugging Face's example demonstrates sending three sentences at once with truncation enabled, since TEI doesn't auto-truncate long inputs by default. For anyone re-embedding a document corpus after switching models or fine-tuning — a task that has to happen repeatedly as models improve — that batch throughput is arguably more important than raw latency on a single query.
My take — AI-written commentary, not fact-checked reporting
The 64x cost claim is the kind of number vendors love to put in bold, and I'd want to see it reproduced outside Hugging Face's own benchmark before treating it as gospel — but even discounted, running your own open embedding model instead of paying per-token to a closed API is obviously the smarter long-term move for anyone building RAG at scale. This is exactly the kind of infrastructure work, unglamorous as it is, that actually determines whether open models win the practical argument against closed ones, not benchmark leaderboards.
Read more about this at: Hugging Face