Sold by: Waltsoft Inc.
Deployed on AWS
BAAI BGE Small EN v1.5 on SageMaker. 384-dimensional embeddings, MTEB score 62.17, at $0.07/hr -- the budget tier for high-volume RAG ingestion and semantic search without per-token billing.
Overview
BGE Small EN v1.5 from BAAI is a lightweight English embedding model that scores 62.17 on the MTEB benchmark -- within 3% of the Large variant at one-tenth the model size. It produces 384-dimensional L2-normalized vectors from inputs up to 512 tokens.
Deploy it as a SageMaker endpoint in your own AWS account for bulk document ingestion, nightly re-embedding pipelines, or any workload where throughput matters more than the last point of retrieval precision. At $0.07/hr on ml.m5.xlarge you can embed over 10,000 short passages per minute.
Drop-in compatible with the same vector stores as the Large variant: pgvector, Amazon OpenSearch, Pinecone, Weaviate, Qdrant, and Chroma. No code changes required when scaling up to BGE Base or Large -- same 384-dim input, same L2-normalized output contract.
Primary use cases: bulk document ingestion before a one-time migration, high-frequency log or telemetry clustering, cost-sensitive semantic deduplication at scale, and RAG pipelines where you trade a small precision delta for 3x throughput.
Highlights
- MTEB score 62.17 at $0.07/hr -- embed 10,000+ passages per minute on ml.m5.xlarge, no per-token charges
- 384-dimensional L2-normalized vectors, 512-token input -- drop-in for pgvector, OpenSearch, Pinecone, and Weaviate
- Zero code changes to upgrade to BGE Base or Large -- same API, same vector dimensions in the S/M/L ladder
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Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m5.xlarge instance type, real-time mode | $0.07 |
ml.m5.xlarge Inference (Batch) Recommended | Model inference on the ml.m5.xlarge instance type, batch mode | $0.07 |
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Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
Initial release
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Inputs
- Summary
BAAI BGE Small EN v1.5 on SageMaker. 384-dimensional embeddings, MTEB score 62.17, at $0.07/hr -- the budget tier for high-volume RAG ingestion and semantic search without per-token billing.
- Input MIME type
- application/json
https://waltsoft-marketplace-assets.s3.amazonaws.com/ml-validation/text-sample/sample.jsonl
https://waltsoft-marketplace-assets.s3.amazonaws.com/ml-validation/text-sample/sample.jsonl
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Contact support@waltsoft.net for deployment assistance.
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