Sold by: MongoDB, Inc
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Text embedding model optimized for general-purpose retrieval quality, latency, and cost for 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-lite is a lightweight, general-purpose embedding model optimized for low latency and cost. Enabled by Matryoshka learning and quantization-aware training, voyage-4-lite supports embeddings in 2048, 1024, 512, and 256 dimensions, with multiple quantization options.
Learn more about voyage-4-lite here: https://blog.voyageai.com/2026/01/15/voyage-4
Highlights
- Lightweight, general-purpose embedding model optimized for low latency and cost.
- 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.g5.2xlarge Inference (Real-Time) Recommended | Model inference on the ml.g5.2xlarge instance type, real-time mode | $3.03 |
ml.g5.xlarge Inference (Real-Time) | Model inference on the ml.g5.xlarge instance type, real-time mode | $2.82 |
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.g6.2xlarge Inference (Real-Time) | Model inference on the ml.g6.2xlarge instance type, real-time mode | $2.44 |
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 tied to the AWS instance you run the model on. Most dimensions cover real-time inference, where a persistent endpoint handles request-by-request inference. One dimension covers batch mode on the ml.g5.2xlarge instance, which runs finite jobs for bulk processing. Hourly cost rises with instance size, moving from ml.g5 and ml.g6 GPU types up through the larger ml.p4d, ml.p4de, and ml.p5 types. You choose the instance and deployment mode that fit your workload. Cost scales with how many hours each instance runs.
Top-of-mind questions for buyers
Your total hourly cost is the sum of software pricing and infrastructure pricing. Software pricing covers model usage. Infrastructure pricing covers the AWS instance. Both are billed at an hourly rate and vary by deployment type, instance type, and region.
Real-time inference runs a persistent, managed endpoint for request-by-request processing, so charges accrue while the endpoint stays active. Batch mode on ml.g5.2xlarge runs a finite transform job for bulk processing, writing results to a file. Charges stop when the job finishes.
Canceling your subscription does not terminate existing real-time endpoints or batch jobs. Active real-time endpoints keep accruing hourly charges until you delete them through SageMaker Studio or the SageMaker console. Delete endpoints you no longer need to avoid unexpected charges.
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-lite
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: 1,000,000 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: 1,000,000 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.
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