Sold by: MongoDB, Inc
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Text embedding model optimized for general-purpose (including multilingual) retrieval/search and AI applications. 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 is a general-purpose (including multilingual) embedding model optimized for retrieval/search and AI applications. voyage-4 supports embeddings in 2048, 1024, 512, and 256 dimensions, with multiple quantization options.
Learn more about voyage-4 here: https://blog.voyageai.com/2026/01/15/voyage-4
Highlights
- General-purpose (including multilingual) embedding model optimized for retrieval/search and AI applications.
- 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.g5.2xlarge Inference (Batch) Recommended | Model inference on the ml.g5.2xlarge instance type, batch mode | $3.03 |
ml.g6.2xlarge Inference (Real-Time) Recommended | Model inference on the ml.g6.2xlarge instance type, real-time mode | $2.44 |
ml.g5.xlarge Inference (Real-Time) | Model inference on the ml.g5.xlarge instance type, real-time mode | $2.82 |
ml.g5.2xlarge Inference (Real-Time) | Model inference on the ml.g5.2xlarge instance type, real-time mode | $3.03 |
ml.g5.4xlarge Inference (Real-Time) | Model inference on the ml.g5.4xlarge instance type, real-time mode | $4.06 |
ml.g5.8xlarge Inference (Real-Time) | Model inference on the ml.g5.8xlarge instance type, real-time mode | $6.12 |
ml.g6.xlarge Inference (Real-Time) | Model inference on the ml.g6.xlarge instance type, real-time mode | $2.25 |
ml.g6.4xlarge Inference (Real-Time) | Model inference on the ml.g6.4xlarge instance type, real-time mode | $3.31 |
ml.g6.8xlarge Inference (Real-Time) | Model inference on the ml.g6.8xlarge instance type, real-time mode | $5.04 |
ml.p4d.24xlarge Inference (Real-Time) | Model inference on the ml.p4d.24xlarge instance type, real-time mode | $35.92 |
Dimensions summary
You pay an hourly rate for each hour the model runs on your chosen AWS GPU instance. Pricing is organized by instance type and deployment mode. One option covers batch mode on the ml.g5.2xlarge instance, running finite jobs for bulk processing. The remaining options cover real-time mode across ml.g5, ml.g6, ml.p4d, ml.p4de, and ml.p5 instance families, which keep a persistent endpoint running for request-by-request use. Cost scales with the size and family of the instance you select. Your total hourly charge combines this software rate with separate AWS infrastructure charges.
Top-of-mind questions for buyers
The hourly rate covers model usage on your chosen instance. Your total hourly cost adds separate AWS infrastructure charges for the underlying instance. Both are metered per hour and appear together. Rates vary by deployment type, instance type, and region.
Charges accrue for every hour a real-time endpoint runs, whether or not it processes requests. It keeps a persistent endpoint active, so idle time still bills. Delete endpoints you no longer need to stop charges. Canceling your subscription does not automatically terminate running endpoints.
The batch option on ml.g5.2xlarge runs finite jobs for bulk processing, then stops, so you pay only for the job's run time. Real-time options keep a persistent endpoint running for request-by-request use and bill continuously while active. Batch suits one-time dataset processing; real-time suits ongoing serving.
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
MongoDB is excited to announce the initial release of voyage-4
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.
- id (string, optional, default=null) - Batch request ID.
- Limitations for input type
- Max List Length: 1,000 strings per request. Max Tokens: 320,000 total tokens per request.
- Input MIME type
- application/json
https://github.com/voyage-ai/voyageai-aws/blob/main/sample_embedding_input.json
https://github.com/voyage-ai/voyageai-aws/blob/main/sample_batch_input_embedding.jsonl
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: 320,000 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 |
id | Batch request ID.
| 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
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