Amazon Sagemaker
Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.
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voyage-3-large Embedding Model Free trial
By:
Latest Version:
v1.0.0
State-of-the-art text embedding model with the best general-purpose and multilingual retrieval quality. 32K context length.
Product 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-large is a state-of-the-art general-purpose and multilingual embedding model that ranks first across eight evaluated domains spanning 100 datasets, including law, finance, and code. It outperforms OpenAI-v3-large and Cohere-v3-English by an average of 9.74% and 20.71%, respectively. Enabled by Matryoshka learning and quantization-aware training, voyage-3-large supports smaller dimensions and int8 and binary quantization that dramatically reduce vectorDB costs with minimal impact on retrieval quality. Latency is 90 ms for a single query with at most 100 tokens, and throughput is 12.6M tokens per hour at $0.22 per 1M tokens on an ml.g6.xlarge. Learn more about voyage-3-large here: https://blog.voyageai.com/2025/01/07/voyage-3-large/
Key Data
Version
Type
Model Package
Highlights
Outperforms OpenAI-v3-large and Cohere-v3-English by an average of 9.74% and 20.71%, respectively, across 100 datasets, spanning eight diverse domains, including law, finance, and code.
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. Latency is 90 ms for a single query with at most 100 tokens, and throughput is 12.6M tokens per hour at $0.22 per 1M tokens on an ml.g6.xlarge.
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Pricing Information
Use this tool to estimate the software and infrastructure costs based your configuration choices. Your usage and costs might be different from this estimate. They will be reflected on your monthly AWS billing reports.
Contact us to request contract pricing for this product.
Estimating your costs
Choose your region and launch option to see the pricing details. Then, modify the estimated price by choosing different instance types.
Version
Region
Software Pricing
Model Realtime Inference$1.6901/hr
running on ml.g6.xlarge
Model Batch Transform$2.2725/hr
running on ml.g5.2xlarge
Infrastructure PricingWith Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
With Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
SageMaker Realtime Inference$1.1267/host/hr
running on ml.g6.xlarge
SageMaker Batch Transform$1.515/host/hr
running on ml.g5.2xlarge
About Free trial
Try this product for 7 days. There will be no software charges, but AWS infrastructure charges still apply. Free Trials will automatically convert to a paid subscription upon expiration.
Model Realtime Inference
For model deployment as Real-time endpoint in Amazon SageMaker, the software is priced based on hourly pricing that can vary by instance type. Additional infrastructure cost, taxes or fees may apply.InstanceType | Realtime Inference/hr | |
---|---|---|
ml.g6.2xlarge | $1.833 | |
ml.g5.xlarge | $2.112 | |
ml.g5.8xlarge | $4.59 | |
ml.g6.4xlarge | $2.481 | |
ml.g5.2xlarge | $2.2725 | |
ml.g5.4xlarge | $3.045 | |
ml.g6.8xlarge | $3.777 | |
ml.g6.xlarge Vendor Recommended | $1.6901 |
Usage Information
Model input and output details
Input
Summary
input
:str
orList[str]
- Text(s)input_type
:str
, optional (default=null
) -"query"
or"document"
.truncation
:bool
, optional (default=True
) - Truncate input.output_dimension
:int
, optional (default=null
) - Dimensions for embeddings.output_dtype
:str
, optional (default="float"
) - Embedding data typeencoding_format
:str
, optional (default=null
) - Encoding (e.g., Base64)id
:str
, optional (default=null
) - Batch 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/jsonlinesSample input data
Output
Summary
The API will respond with a JSON object containing a list of embedding objects, along with other information such as model name, token usage, and batch transform ID if applicable.
Output MIME type
application/json, text/csv, application/jsonlinesSample output data
Sample notebook
Additional Resources
End User License Agreement
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Support Information
voyage-3-large Embedding Model
Please email us at contact@voyageai.com for inquiries and customer support. Join our Discord channel: https://discord.gg/zAU7GQEmvT
AWS Infrastructure
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Learn MoreRefund Policy
Refunds to be processed under the conditions specified in EULA. Please contact contact@voyageai.com for further assistance.
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