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Amazon Sagemaker

Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.

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voyage-multimodal-3 Embedding Model Free trial

Latest Version:
v1.0.1
Rich multimodal embedding model that can vectorize interleaved text and content-rich images. 32K context length.

    Product Overview

    Multimodal embedding models are neural networks that transform multiple modalities, such as text and images, into numerical vectors. They are a crucial building block for semantic search/retrieval systems and retrieval-augmented generation (RAG) and are responsible for the retrieval quality. voyage-multimodal-3 is a state-of-the-art multimodal embedding model that uniquely vectorizes interleaved texts + images while capturing visual features from PDFs, slides, tables, figures, and more, eliminating complex document parsing. It improves retrieval accuracy by an average of 19.63% over the next best-performing multimodal embedding model when evaluated across 3 multimodal retrieval tasks (20 total datasets). Latency is 75 ms for a single query with at most 200 tokens, and throughput is 57M tokens per hour at $0.06 per 1M tokens on an ml.g6.xlarge. Learn more about voyage-multimodal-3 here: https://blog.voyageai.com/2024/11/12/voyage-multimodal-3/

    Key Data

    Type
    Model Package
    Fulfillment Methods
    Amazon SageMaker

    Highlights

    • Unlike existing multimodal embedding models, voyage-multimodal-3 is capable of vectorizing interleaved texts + images and capturing key visual features from screenshots of PDFs, slides, tables, figures, and more, thereby eliminating the need for complex document parsing.

    • Improves retrieval accuracy by an average of 19.63% over the next best-performing multimodal embedding model when evaluated across 3 multimodal retrieval tasks (20 total datasets).

    • 32K token context length. Latency is 75 ms for a single query with at most 200 tokens, and throughput is 57M tokens per hour at $0.06 per 1M tokens on an ml.g6.xlarge.

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    Pricing Information

    Use this tool to estimate the software and infrastructure costs based your configuration choices. Your usage and costs might be different from this estimate. They will be reflected on your monthly AWS billing reports.

    Contact us to request contract pricing for this product.


    Estimating your costs

    Choose your region and launch option to see the pricing details. Then, modify the estimated price by choosing different instance types.

    Version
    Region

    Software Pricing

    Model Realtime Inference$2.2534/hr

    running on ml.g6.xlarge

    Model Batch Transform$3.03/hr

    running on ml.g5.2xlarge

    Infrastructure Pricing

    With Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
    Learn more about SageMaker pricing

    SageMaker Realtime Inference$1.1267/host/hr

    running on ml.g6.xlarge

    SageMaker Batch Transform$1.515/host/hr

    running on ml.g5.2xlarge

    About Free trial

    Try this product for 7 days. There will be no software charges, but AWS infrastructure charges still apply. Free Trials will automatically convert to a paid subscription upon expiration.

    Model Realtime Inference

    For model deployment as Real-time endpoint in Amazon SageMaker, the software is priced based on hourly pricing that can vary by instance type. Additional infrastructure cost, taxes or fees may apply.
    InstanceType
    Realtime Inference/hr
    ml.g6.2xlarge
    $2.444
    ml.g5.xlarge
    $2.816
    ml.g5.8xlarge
    $6.12
    ml.g6.4xlarge
    $3.308
    ml.g5.2xlarge
    $3.03
    ml.g5.4xlarge
    $4.06
    ml.g6.8xlarge
    $5.036
    ml.g6.xlarge
    Vendor Recommended
    $2.2534

    Usage Information

    Model input and output details

    Input

    Summary
    1. inputs: List[dict] - A list of multimodal inputs to be vectorized. (see input description for details).
    2. input_type: str, optional (default=null) - "query" or "document".
    3. truncation: bool, optional (default=True) - Truncate input.
    4. output_encoding: str, optional (default=null) - Encoding (e.g., Base64)
    5. id: str, optional (default=null) - Batch request ID.
    Limitations for input type
    The maximum tokens for each text is 32K, the maximum length of the list is 128, and the total number of tokens in the list is at most 128K. Every 560 pixels of an image counts as a token.
    Input MIME type
    text/csv, application/json, application/jsonlines
    Sample input data

    Output

    Summary

    The API will respond with a JSON object containing a list of embedding objects, along with other information such as model name and batch transform ID if applicable.

    Output MIME type
    application/json, text/csv, application/jsonlines
    Sample output data

    End User License Agreement

    By subscribing to this product you agree to terms and conditions outlined in the product End user License Agreement (EULA)

    Support Information

    voyage-multimodal-3 Embedding Model

    Please email us at contact@voyageai.com for inquiries and customer support. Join our Discord channel: https://discord.gg/zAU7GQEmvT

    AWS Infrastructure

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    Refund Policy

    Refunds to be processed under the conditions specified in EULA. Please contact contact@voyageai.com for further assistance.

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