Sold by: NomicÂ
Deployed on AWS
Open source text embedder with 8192-length context, flexible dimensionality, and binary quantization
Overview
Nomic Embed is the only truly open (open source, weights, and data) text embedder to beat OpenAI on both short and long context tasks.
Highlights
- Compared to similar models such as OpenAI's text-embedding-3-small, Nomic Embed outperforms on short context (MTEB 62.39 vs 62.26) and long context (LoCo 85.53 vs 82.40) benchmarks.
- Nomic embed supports binary embeddings, which can reduce the memory footprint of vector collections by several orders of magnitude.
- Nomic embed supports Matryoshka embeddings, allowing the user to have fine tuned control over the performance/disk space tradeoff.
Details
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Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
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.p3.2xlarge Inference (Batch) Recommended | Model inference on the ml.p3.2xlarge instance type, batch mode | $1.50 |
ml.g5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.g5.xlarge instance type, real-time mode | $2.50 |
ml.p2.xlarge Inference (Batch) | Model inference on the ml.p2.xlarge instance type, batch mode | $1.50 |
ml.g4dn.4xlarge Inference (Batch) | Model inference on the ml.g4dn.4xlarge instance type, batch mode | $2.60 |
ml.g4dn.16xlarge Inference (Batch) | Model inference on the ml.g4dn.16xlarge instance type, batch mode | $9.50 |
ml.p2.16xlarge Inference (Batch) | Model inference on the ml.p2.16xlarge instance type, batch mode | $1.50 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $1.50 |
ml.g5.xlarge Inference (Batch) | Model inference on the ml.g5.xlarge instance type, batch mode | $1.60 |
ml.g5.8xlarge Inference (Batch) | Model inference on the ml.g5.8xlarge instance type, batch mode | $11.75 |
ml.g5.12xlarge Inference (Batch) | Model inference on the ml.g5.12xlarge instance type, batch mode | $17.87 |
Vendor refund policy
Please contact support@nomic.ai for inquires about refunds.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA)Â .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
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
task_type now accepted in request body
Additional details
Inputs
- Summary
Using our python client, you can simply pass this model a list of strings to embed.
If you are using Boto directly, you can hit the endpoint like
import boto3 data = { "texts": ["The quick brown fox jumps over the lazy dog"], "task_type": "search_document", } client = boto3.client("sagemaker-runtime", region_name='us-east-2') response = client.invoke_endpoint( EndpointName=ENDPOINT_NAME, Body=json.dumps(data), ContentType='application/json', )
- Input MIME type
- text/csv, application/json
https://nomic-embed-sagemaker-cmp-validation.s3.us-east-2.amazonaws.com/validation/example.csv
https://nomic-embed-sagemaker-cmp-validation.s3.us-east-2.amazonaws.com/validation/example.csv
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
csv | Input data should be a csv file without any column headers with each line containing a single text. | Type: FreeText | Yes |
Resources
Support
Vendor support
Please contact support@nomic.ai to inquire about support packages
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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Customer reviews
0 ratings
0 AWS reviews
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12 external reviews
Star ratings include only reviews from verified AWS customers. External reviews can also include a star rating, but star ratings from external reviews are not averaged in with the AWS customer star ratings.
Pratima B.
Provide base layout and styling is also nice
Reviewed on Jul 31, 2025
Review provided by G2
What do you like best about the product?
Visualization tool
Innovative and friendly technology
Innovative and friendly technology
What do you dislike about the product?
There is nothing to dislike about the application
What problems is the product solving and how is that benefiting you?
Managing and understanding enterprise data, natural language understanding
Ruchita S.
used for unstructured data
Reviewed on Jun 24, 2024
Review provided by G2
What do you like best about the product?
if u have unstructured data u can use it for structure and for the grouping and its very much help full to group the unstrutured data and easy to use and understandable. we also can use for some of the coding and can mention locations as well.
What do you dislike about the product?
if some body wants to use there is only few days to useas free. so its not that much easy to understand in few days.
What problems is the product solving and how is that benefiting you?
it help to group the data and help to visualise the data aswell. mainly for unstructured data ihave used for marketing, candidate datas, and sales.
Rohith S.
Atlas: A Easy Tool for Analyzing Scan Data with Voyager
Reviewed on Jun 17, 2024
Review provided by G2
What do you like best about the product?
What stands out to me the most about Atlas is how it simplifies complex scan data analysis using Voyager, making it incredibly straightforward and effective.Atlas is really good at measuring differences from CAD models and golden samples. This makes it super useful for checking quality and verifying designs.
What do you dislike about the product?
One thing I don't like about Atlas is that it can be a bit overwhelming for new users because of its many features and options.
What problems is the product solving and how is that benefiting you?
Atlas solves the problem of making sense of detailed scan data using Voyager. This benefits me by simplifying quality checks and ensuring designs are accurate.
Pranendu Bikash P.
Database management made easier
Reviewed on Jun 11, 2024
Review provided by G2
What do you like best about the product?
The ease of implementing a database and managing it.
What do you dislike about the product?
Nothing as of now. Will update if I find any
What problems is the product solving and how is that benefiting you?
It is helping us to manage and deploy multi-instance cloud servers in an efficient and easy way.
Vidya S.
Best tool for generating effective report
Reviewed on Apr 23, 2024
Review provided by G2
What do you like best about the product?
This platform is ingrediable for generating the report seamless, we can import and wxport the data that make tool user friendly.
What do you dislike about the product?
sometime due to server tool speed lagging than expected.
What problems is the product solving and how is that benefiting you?
with this tool task become easy, reporting with this tool become easy.