Sold by: MongoDB, Inc
Deployed on AWS
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State-of-the-art text embedding model with the best general-purpose and multilingual retrieval quality. 32K context length.
Overview
Text embedding models are neural networks that transform texts 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-4-large is a state-of-the-art general-purpose and multilingual embedding optimized for retrieval quality. Enabled by Matryoshka learning and quantization-aware training, voyage-4-large supports embeddings in 2048, 1024, 512, and 256 dimensions, with multiple quantization options.
Learn more about voyage-4-large here: https://blog.voyageai.com/2026/01/15/voyage-4
Highlights
- State-of-the-art general-purpose and multilingual embedding optimized for retrieval quality.
- Supports embeddings of 2048, 1024, 512, and 256 dimensions and offers multiple embedding quantization, including float (32-bit floating point), int8 (8-bit signed integer), uint8 (8-bit unsigned integer), binary (bit-packed int8), and ubinary (bit-packed uint8).
- 32K token context length.
Details
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Pricing
Free trial
Try this product free for 7 days according to the free trial terms set by the vendor.
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.g7e.2xlarge Inference (Real-Time) Recommended | Model inference on the ml.g7e.2xlarge instance type, real-time mode | $4.49 |
ml.g5.2xlarge Inference (Batch) Recommended | Model inference on the ml.g5.2xlarge instance type, batch mode | $35.92 |
ml.p4de.24xlarge Inference (Real-Time) | Model inference on the ml.p4de.24xlarge instance type, real-time mode | $35.92 |
ml.p5.48xlarge Inference (Real-Time) | Model inference on the ml.p5.48xlarge instance type, real-time mode | $35.92 |
ml.g7e.4xlarge Inference (Real-Time) | Model inference on the ml.g7e.4xlarge instance type, real-time mode | $4.49 |
ml.g7e.8xlarge Inference (Real-Time) | Model inference on the ml.g7e.8xlarge instance type, real-time mode | $4.49 |
ml.g7e.12xlarge Inference (Real-Time) | Model inference on the ml.g7e.12xlarge instance type, real-time mode | $8.98 |
ml.g7e.24xlarge Inference (Real-Time) | Model inference on the ml.g7e.24xlarge instance type, real-time mode | $17.96 |
ml.g7e.48xlarge Inference (Real-Time) | Model inference on the ml.g7e.48xlarge instance type, real-time mode | $35.92 |
Dimensions summary
You pay an hourly rate for running this embedding model on your chosen AWS instance. All dimensions bill by host hours (HostHrs). Eight options cover real-time inference, where you keep a persistent API endpoint running for request-by-request use. These span several GPU instance families and sizes, so your hourly cost scales with the compute power you select. One option covers batch mode on the ml.g5.2xlarge instance, which runs a finite job for bulk processing rather than a persistent endpoint. Your total cost combines this software rate with the separate AWS infrastructure charge for the instance.
Top-of-mind questions for buyers
The hourly rate covers software usage of the embedding model itself. Your invoice also carries a separate AWS charge for the underlying instance. Both meter by the hour. Your total hourly cost is the sum of the software rate plus the AWS infrastructure rate for that instance type and region.
Yes. Real-time endpoints are persistent, so charges accrue for every hour the endpoint stays deployed, whether or not it handles requests. To stop charges, delete the endpoint through SageMaker. Canceling your subscription alone does not terminate running endpoints or batch jobs.
Real-time options run a persistent API endpoint that bills every hour it stays up, suited to request-by-request use. The batch option on the ml.g5.2xlarge instance runs a finite job for bulk processing. It bills only for the hours the job runs, then stops. Batch suits large one-time datasets.
docs.voyageai.com
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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
This version adds support for NVIDIA RTX PRO 6000 Blackwell (ml.g7e) instances for real-time inference, alongside the existing ml.p4de and ml.p5 instances.
Additional details
Inputs
- Summary
- input (string or List[string]) – A single string or a list of strings (max 1,000 items).
- input_type (string, optional, default = null) – The role of the input: query, document, or null.
- truncation (bool, optional, default = true) – Whether to truncate inputs to fit context limits.
- output_dimension (int, optional, default = null) – Supported dimensions: 2048, 1024, 512, 256.
- output_dtype (string, optional, default = "float") – Data type for embeddings: float, int8, uint8, binary, or ubinary.
- encoding_format (string, optional, default = null) – Format in which the embeddings are encoded, other options: base64.
Note: voyage-4-large embedding model does NOT support batch transform.
- Limitations for input type
- Max List Length: 1,000 strings per request. Max Tokens: 120,000 total tokens per request. Note: voyage-4-large embedding model does NOT support batch transform.
- Input MIME type
- application/json
https://github.com/voyage-ai/voyageai-aws/blob/main/sample_embedding_input.json
voyage-4-large embedding model does NOT support batch transform.
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
input | A single string or a list of strings | Type: string or List[string]
Max List Length: 1,000 strings per request.
Max Tokens: 120,000 total tokens per request. | Yes |
input_type | The role of the input: query, document, or null.
| Default value: null
Type: string | No |
truncation | Whether to truncate inputs to fit context limits. | Default value: true
Type: boolean | No |
output_dimension | Supported dimensions: 2048, 1024, 512, 256. | Default value: 1024
Type: int | No |
output_dtype | Data type for embeddings: float, int8, uint8, binary, or ubinary. | Default value: "float"
Type: string | No |
encoding_format | Format in which the embeddings are encoded, other options: base64.
| Default value: null
Type: string | No |
Resources
Vendor resources
Support
Vendor support
Please email us at aws-marketplace@mongodb.com for inquiries and customer support.
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.
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