---
title: "Deploy Text Embeddings Inference"
description: "Hugging Face Text Embeddings Inference 1.9 on CPU with an API key."
category: "AI/ML"
url: https://railway.com/deploy/text-embeddings-in-1
---

# Deploy Text Embeddings Inference

Hugging Face Text Embeddings Inference 1.9 on CPU with an API key.

**[Deploy Text Embeddings Inference on Railway](https://railway.com/template/text-embeddings-in-1)**

Machine-readable deploy manifest (JSON, validated by TemplateCI): https://railway.com/deploy/text-embeddings-in-1/manifest.json

- **Creator:** Agaz Self-Host
- **Category:** AI/ML

## Template content

### tei https://github.com/huggingface.png

- **Image:** ghcr.io/huggingface/text-embeddings-inference:cpu-1.9.4
- **Health check:** /health
- **Public domain:** Yes

## Documentation

# Deploy and Host Text Embeddings Inference on Railway

Text Embeddings Inference (TEI) is Hugging Face's high-performance server for text embedding and reranker models. It serves models such as BGE, GTE, E5, Nomic and sentence-transformers through a simple REST API and an OpenAI-compatible embeddings endpoint, with dynamic batching and fast tokenization, which makes it a drop-in backend for RAG pipelines.

## About Hosting Text Embeddings Inference

This template deploys TEI v1.9.4 using the CPU image, with `BAAI/bge-small-en-v1.5` as the default model. The model is downloaded from Hugging Face on first start into a Railway volume, so restarts reuse the cache. Every request needs the generated API key as a bearer token. The service listens on IPv4 and IPv6, so other Railway services can use the private URL. The default model produces 384-dimension vectors and uses roughly 500 MB of memory. Change `MODEL_ID` for another embedding or reranker model, and size memory accordingly. Add replicas to scale throughput horizontally.

## Common Use Cases

- Embeddings for RAG, semantic search and clustering without an external API
- An OpenAI-compatible `/v1/embeddings` endpoint for LangChain, LlamaIndex and others
- Reranking search results with a cross-encoder model

## Dependencies for Text Embeddings Inference Hosting

- `ghcr.io/huggingface/text-embeddings-inference:cpu-1.9.4` (official image)
- A Railway volume at `/data` for the model cache

### Deployment Dependencies

- [TEI documentation](https://huggingface.co/docs/text-embeddings-inference/index)
- [TEI v1.9.4 release](https://github.com/huggingface/text-embeddings-inference/releases/tag/v1.9.4)
- [Supported models](https://huggingface.co/docs/text-embeddings-inference/supported_models)
- [Railway volumes](https://docs.railway.com/reference/volumes)

### Implementation Details

| Service | Image | Networking | Storage |
| --- | --- | --- | --- |
| tei | `text-embeddings-inference:cpu-1.9.4` | public domain on 80; private IPv4/IPv6 | volume at `/data` |

```bash
curl "$TEI_URL/v1/embeddings" -H "Authorization: Bearer $API_KEY" -H "Content-Type: application/json" \
  -d '{"input": ["hello world"], "model": "bge"}'
```

OpenAI SDKs work with `base_url = "$TEI_URL/v1"` and the API key.

| Variable | Default | Purpose |
| --- | --- | --- |
| `MODEL_ID` | `BAAI/bge-small-en-v1.5` | Hugging Face model to serve |
| `API_KEY` | generated | Required bearer token |
| `HOSTNAME` | `::` | Bind address; do not remove, Docker otherwise sets it to the container hostname |
| `HF_TOKEN` | unset | Add for gated or private models |

Notes:

- The first start downloads the model; the health check allows up to 10 minutes.
- Larger models (for example `BAAI/bge-m3`) need several GB of RAM.

This is a community-maintained deployment package and does not imply affiliation with or endorsement by the Text Embeddings Inference project or its maintainers.

## Why Deploy Text Embeddings Inference on Railway?

Railway is a singular platform to deploy your infrastructure stack. Railway will host your infrastructure so you don't have to deal with configuration, while allowing you to vertically and horizontally scale it.

By deploying Text Embeddings Inference on Railway, you are one step closer to supporting a complete full-stack application with minimal burden. Host your servers, databases, AI agents, and more on Railway.


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