Sold by: MongoDB, Inc
Deployed on AWS
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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 GPU instance you deploy this embedding model on. Twelve options span two instance families and several sizes, so cost scales with the compute you choose. Larger instances carry higher hourly rates. Eleven options run in real-time mode for request-by-request inference through a persistent endpoint. One ml.g5.2xlarge option runs in batch mode for bulk processing of datasets. Your total hourly cost combines this software rate with separate AWS infrastructure charges. A free trial is available.
Top-of-mind questions for buyers
One HostHrs unit is one hour that a chosen GPU instance runs the model. Each instance size and family has its own hourly software rate. Counting is based on time the endpoint or batch job stays active, not on requests or tokens processed.
Yes. Real-time endpoints are persistent, so charges accrue for every hour the endpoint stays running, whether or not requests arrive. Delete endpoints you no longer need to stop charges. Batch transform jobs run for a finite period and stop when the job finishes.
Your total hourly cost is the sum of two parts. The software rate covers model usage. The AWS infrastructure rate covers the GPU instance itself. Both meter by the hour and appear together. Rates vary by deployment type, instance type, and region.
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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