Sold by: Waltsoft Inc.
Deployed on AWS
intfloat/multilingual-e5-base on SageMaker. 768-dimensional mean-pooled embeddings covering 100 languages. One endpoint for global-enterprise RAG -- no per-language model sprawl, no token billing.
Overview
multilingual-e5-base from intfloat supports 100 languages (initialized from xlm-roberta-base) in a single model, enabling cross-lingual semantic search where queries and documents can be in different languages. It produces 768-dimensional mean-pooled vectors from inputs up to 512 tokens.
Deploy as a SageMaker endpoint in your own AWS account. A single endpoint replaces separate per-language embedding services -- simplified architecture, lower operational overhead, and all documents staying in your VPC.
IMPORTANT: The model requires query and passage prefix instructions. Prepend "query: " to search queries and "passage: " to documents before embedding -- even for non-English texts. Per the model card FAQ: skipping the prefix degrades performance, this is how the model was trained.
Works with pgvector, Amazon OpenSearch, Pinecone, Weaviate, and any vector store accepting 768-dimensional float32 vectors.
Primary use cases: multilingual customer support search, global enterprise document retrieval, cross-lingual product catalog similarity, compliance document search across regions, and any RAG pipeline serving users in multiple languages.
Highlights
- 100-language support in one endpoint -- eliminates per-language model sprawl for global enterprise deployments
- 768-dimensional mean-pooled vectors, 512-token input -- cross-lingual retrieval across query and document language pairs
- Flat $0.08/hr on ml.m5.xlarge -- no token charges, all documents in your VPC, scalable to batch transform for offline ingestion
Details
Sold by
Delivery method
Latest version
Deployed on AWS
New
Introducing multi-product solutions
You can now purchase comprehensive solutions tailored to use cases and industries.
Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m5.xlarge instance type, real-time mode | $0.08 |
ml.m5.xlarge Inference (Batch) Recommended | Model inference on the ml.m5.xlarge instance type, batch mode | $0.08 |
Vendor refund policy
No refunds.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
Delivery details
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
Additional details
Inputs
- Summary
intfloat/multilingual-e5-base on SageMaker. 768-dimensional mean-pooled embeddings covering 100 languages. One endpoint for global-enterprise RAG -- no per-language model sprawl, no 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
Support
Vendor support
Contact support@waltsoft.net for deployment assistance.
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Similar products
BAAI BGE Base EN v1.5 on SageMaker. 768-dimensional embeddings scoring 63.55 on MTEB -- 95% of Large quality at $0.08/hr. The mid-tier for production RAG when cost and quality both matter.
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.
Ask Sage is a secure, model and cloud-agnostic generative AI platform trusted by 15,000 government teams and 2,500 companies. Users have access to any commercial and government approved Large Language Model and various open-source models with ability to ingest CUI data, and integrate with existing systems via APIs. IL5/IL6 authorized with Top Secret deployments. Ask Sage is built with zero-trust architecture and label-based access control. Deploy on any cloud, on-premise, or air-gapped.
Ask Sage is a secure, model and cloud-agnostic generative AI platform trusted by 15,000 government teams and 2,500 companies. Users have access to any commercial and government approved Large Language Model and various open-source models with ability to ingest CUI data, and integrate with existing systems via APIs. IL5/IL6 authorized with Top Secret deployments. Ask Sage is built with zero-trust architecture and label-based access control. Deploy on any cloud, on-premise, or air-gapped.
Customer reviews
No customer reviews yet
Be the first to review this product . We've partnered with PeerSpot to gather customer feedback. You can share your experience by writing or recording a review, or scheduling a call with a PeerSpot analyst.