Sold by: MongoDB, IncÂ
Deployed on AWS
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-3.5 is a state-of-the-art general-purpose and multilingual embedding model that outperforms OpenAI-v3-large by 8.26% on average across evaluated domains. Enabled by Matryoshka learning and quantization-aware training, voyage-3.5 supports smaller dimensions and int8 and binary quantization that dramatically reduce vectorDB costs with minimal impact on retrieval quality. Latency is 62.5 ms for a single query with at most 200 tokens, and throughput is 40M tokens per hour at $0.08 per 1M tokens on an ml.g6.xlarge. Learn more about voyage-3.5 here: https://blog.voyageai.com/2025/05/20/voyage-3-5/Â
Highlights
- Optimized for general-purpose and multilingual retrieval quality, outperforings OpenAI v3 large by 8.26% on average across evaluated domains. Compared with OpenAI-v3-large (float, 3072), voyage-3.5 (int8, 2048) reduces vector database costs by 83%, while achieving higher 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, well-suited for applications on long documents. Latency is 62.5 ms for a single query with at most 200 tokens. 40M tokens per hour at $0.08 per 1M tokens on an ml.g6.xlarge.
Details
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Pricing
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.xlarge Inference (Real-Time) Recommended | Model inference on the ml.g6.xlarge instance type, real-time mode | $2.25 |
ml.g5.16xlarge Inference (Real-Time) | Model inference on the ml.g5.16xlarge instance type, real-time mode | $10.24 |
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.g5.xlarge Inference (Real-Time) | Model inference on the ml.g5.xlarge instance type, real-time mode | $2.82 |
ml.g6.16xlarge Inference (Real-Time) | Model inference on the ml.g6.16xlarge instance type, real-time mode | $8.49 |
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 |
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Refunds to be processed under the conditions specified in EULA. Please contact aws-marketplace@mongodb.com for further assistance.
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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-3.5
Additional details
Inputs
- Summary
- input: str or List[str] - Single text or list of texts.
- input_type: str, optional (default=None) - May also be "query" or "document".
- truncation: bool, optional (default=True) - True: Truncates. False: raises error if any 4. given text exceeds the context length.
- encoding_format: str, optional (default=None) - Embedding format. None: float list; "base64": compressed encoding.
- id: str, optional (default=None) - Batch transform 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 32k.
- Input MIME type
- text/csv, application/json, application/jsonlines
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 text string, or a list of texts as a list of strings. | Type: FreeText Limitations: 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 160K. | Yes |
input_type | Type of the input text. Default to None. Other options: "query", "document". | Default value: None Type: FreeText | No |
truncation | Whether to truncate the input texts to fit within the context length. - If True, over-length input texts will be truncated to fit within the context length. - If False, an error will be raised if any given text exceeds the context length. | Default value: True Type: Categorical Allowed values: True, False | No |
encoding_format | Format in which the embeddings are encoded. We currently support two options: - None (default): the embeddings are represented as lists of floating-point numbers; - "base64": the embeddings are compressed to Base64 encodings. | Default value: None Type: Categorical Allowed values: base64, None | No |
id | Batch transform request ID. If specified, this will be returned in the output. | Default value: None Type: FreeText | No |
Support
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Please email us at aws-marketplace@mongodb.com for inquiries and customer support. Join our Discord channel: https://discord.gg/zAU7GQEmvTÂ
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