Serve embedding models for your RAG stack

Host embedding and reranking models on your own autoscaling endpoints instead of per-token third-party APIs. Beam runs the GPUs; your RAG pipeline just calls HTTPS.

Your retrieval stack, your terms

Embeddings, rerankers, and the LLM itself — each piece deployed and scaled independently, all from Python.

Your models, not per-token APIs
Serve BGE, GTE, or your own fine-tuned embedding model with flat per-second compute pricing instead of metered tokens.
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Warm models, low latency
on_start keeps the model loaded per container, and keep_warm_seconds holds containers hot for query-time traffic.
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Parallel corpus backfills
Embed millions of chunks by fanning a function out across containers with .map() — then scale back to zero.
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Secrets for your data stores
Keep vector database credentials in Beam's secrets manager and connect from inside the container.
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Scale each piece independently
Embedding endpoint, reranker, and LLM run as separate deployments, each with its own autoscaling policy.
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Drops into any framework
Your endpoint is plain HTTPS — point LangChain, LlamaIndex, or hand-rolled pipelines at it with a custom endpoint class.
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Stand up retrieval in three steps

01

Wrap your embedding model

A few lines turn any sentence-transformers model into an endpoint definition with a GPU attached.

02

Deploy it

beam deploy gives you an authenticated HTTPS URL that autoscales with query volume.

03

Point your pipeline at it

Call it for query-time embeddings, and use .map() to backfill your corpus in parallel.

$ beam deploy app.py:embed
=> Deployed 🎉
=> https://embeddings-abc123.app.beam.cloud

# Backfill your corpus in parallel
from beam import function

@function(gpu="T4")
def embed_chunk(chunk: list):
    return model.encode(chunk).tolist()

for vectors in embed_chunk.map(chunks):
    index.upsert(vectors)

Frequently asked questions

Which embedding models work?+

Any model sentence-transformers or transformers can load — BGE, GTE, E5, and your own fine-tuned variants included.

Do I need a specific vector database?+

No. Beam serves the compute; your endpoint returns vectors and you store them anywhere — Postgres with pgvector, Pinecone, Qdrant, or your own index.

How do I keep query latency low?+

Models load once per container with on_start, and keep_warm_seconds keeps containers hot so query-time embeddings skip the cold path entirely.

Can I host the LLM side of RAG too?+

Yes. Run an OpenAI-compatible vLLM server on Beam next to your embedding endpoint, so the whole stack lives on one platform.

Can I serve a fine-tuned embedding model?+

Yes — load weights from a persistent volume or the Hugging Face Hub exactly like any other model. Fine-tune it on Beam too, if you like.