Sold by: IsaacusÂ
Deployed on AWS
State-of-the-art legal embedding and reranking models optimized for legal research, legal RAG, and legal document classification.
Overview
Kanon 2 Embedder and Kanon Universal Classifier are state-of-the-art legal embedding, reranking, and zero-shot classification models optimized for semantic search, RAG, and document classification. Kanon 2 Embedder can be used to sort through millions of legal documents to find the most similar passages to a user query, with Kanon Universal Classifier then reranking those passages by their relevance with extreme precision, making this model bundle especially valuable for legal research and legal RAG applications.
As of 28 October 2025, Kanon 2 Embedder ranks first on the Massive Legal Embedding Benchmark (MLEB) ahead of 20 other models, including OpenAI Text Embedding 3 Large, Gemini Embedding, Voyage 3 Large, Qwen 3 Embedding 8B, and Jina Embeddings v4 (https://arxiv.org/abs/2510.19365Â ). It also ranks first on case, legislation, and regulation retrieval and third on contract retrieval.
Kanon Universal Classifier likewise manages to outperform its largest open-source equivalent, DeBERTa v3 large, at legal classification and NLI while remaining 71% faster.
Kanon 2 Embedder has a context window of up to 16,384 tokens, and Kanon Universal Classifier has a local context window of 512 tokens. Kanon Universal Classifier can process documents of any length thanks to Isaacus' semchunk semantic chunking algorithm (https://github.com/isaacus-dev/semchunk ).
On a single g6.2xlarge instance, Kanon 2 Embedder can embed up to ~15k legal documents (62 million tokens) per hour while Kanon Universal Classifier can process up to ~32k documents (131 million tokens) an hour.
Like all other Isaacus SageMaker models, your Kanon 2 Embedder and Kanon Universal Classifier bundle will be fully air-gapped--no data will enter or leave your AWS account.
Conveniently, Isaacus SageMaker models are also compatible with the standard Isaacus Python SDK via the Isaacus SageMaker Python integration (https://docs.isaacus.com/integrations/amazon-sagemaker ).
Both Kanon 2 Embedder (https://aws.amazon.com/marketplace/pp/prodview-qoz2rxyqhtewu ) and Kanon Universal Classifier (https://aws.amazon.com/marketplace/pp/prodview-6dotzeq7aq4sy ) can be purchased separately on the AWS Marketplace.
You can negotiate a discount to this model bundle by contacting us at https://isaacus.com/support .
Highlights
- Kanon 2 Embedder is ranked first on the [Massive Legal Embedding Benchmark (MLEB)](https://huggingface.co/papers/2510.19365) at legal document retrieval out of 20 other models, including OpenAI Text Embedding 3 Large, Gemini Embedding, Voyage 3 Large, Qwen 3 Embedding 8B, and Jina Embeddings v4, while Kanon Universal Classifier ranks ahead of its largest open-source competitors at legal natural language inference by both accuracy and inference time.
- Capable of embedding up to ~15k legal documents (62 million tokens) per hour and reranking up to ~32k documents (131 million tokens) per hour.
- Fully compatible with the Isaacus Python SDK via the [Isaacus SageMaker Python integration](https://docs.isaacus.com/integrations/amazon-sagemaker)--no substantive code changes necessary.
Details
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Delivery method
Latest version
Deployed on AWS
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Financing for AWS Marketplace purchases
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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.g6.2xlarge Inference (Batch) Recommended | Model inference on the ml.g6.2xlarge instance type, batch mode | $7.99 |
ml.g6.2xlarge Inference (Real-Time) Recommended | Model inference on the ml.g6.2xlarge instance type, real-time mode | $7.99 |
ml.g5.2xlarge Inference (Batch) | Model inference on the ml.g5.2xlarge instance type, batch mode | $7.99 |
ml.g5.4xlarge Inference (Batch) | Model inference on the ml.g5.4xlarge instance type, batch mode | $7.99 |
ml.g5.8xlarge Inference (Batch) | Model inference on the ml.g5.8xlarge instance type, batch mode | $7.99 |
ml.g5.16xlarge Inference (Batch) | Model inference on the ml.g5.16xlarge instance type, batch mode | $7.99 |
ml.g6.4xlarge Inference (Batch) | Model inference on the ml.g6.4xlarge instance type, batch mode | $7.99 |
ml.g6.8xlarge Inference (Batch) | Model inference on the ml.g6.8xlarge instance type, batch mode | $7.99 |
ml.g6.16xlarge Inference (Batch) | Model inference on the ml.g6.16xlarge instance type, batch mode | $7.99 |
ml.g5.2xlarge Inference (Real-Time) | Model inference on the ml.g5.2xlarge instance type, real-time mode | $7.99 |
Vendor refund policy
To the maximum extent permitted by law, there are no refunds for consumption of this product.
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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
Patched release of the Kanon 2 Embedder and Kanon Universal Classifier bundle with version 0.1.3 of the Isaacus SageMaker Model Server, offering feature parity with version 0.7.0 (https://github.com/isaacus-dev/openapi/blob/8591b10de78a2b028df3f74fb5d6574d23bb62b2/openapi.yaml ) of the Isaacus API.
This patch disables support for older AWS instance types with outdated CUDA versions barring g5.2xlarge for batch transformation which, although not working, must be included due to AWS' own limitations preventing validation with newever instances.
Additional details
Inputs
- Summary
For a user-friendly walkthrough of how to get started deploying Isaacus models on SageMaker, check out the Isaacus SageMaker quickstart guide on our docs.
This model runs on the fully air-gapped Isaacus SageMaker Model Server, which supports all the same functionality as the standard Isaacus API except that requests to the server must be proxied through the /invocations endpoint.
For example, if you wanted to send a POST request to /v1/embeddings with the data {"model": "kanon-2-embedder", "texts": ["This is a confidentiality clause."], "task": "retrieval/query"}, you could so by sending /invocations the payload {"path": "/v1/embeddings","data": {"model": "kanon-2-embedder", "texts": ["This is a confidentiality clause."], "task": "retrieval/query"}}.
Likewise, if you wanted to send the request {"model":"kanon-universal-classifier","query":"Who is the Governor-General?","texts":["The Governor-General is Sam Mostyn.","The King is Charles III."]} to /v1/rerankings, you could do so by sending /invocations the payload {"path":"/v1/rerankings","data":{"model":"kanon-universal-classifier","query":"Who is the Governor-General?","texts":["The Governor-General is Sam Mostyn.","The King is Charles III."]}}.
This means that minimal code changes are necessary to switch between the online Isaacus API and your own private Isaacus model deployments.
In fact, Python users can use the Isaacus SageMaker Python integration to automatically forward requests to the Isaacus API to SageMaker deployments using the standard Isaacus SDK.
Given that this is a private deployment and that authentication is managed by AWS, Isaacus API keys are not needed and are ignored.
As an embedding model, Kanon 2 Embedder currently only supports the /v1/embeddings endpoint. As a reranking and classification model, Kanon Universal Classifier supports the /v1/rerankings and /v1/classifications/universal endpoints.
- Limitations for input type
- All the same limitations applicable to the Isaacus API except for the need for an API key.
- Input MIME type
- application/json
{
"path": "/v1/embeddings",
"data": {
"model": "kanon-2-embedder",
"texts": [
"Who was the plaintiff in Mabo?"
],
"task": "retrieval/query"
}
}
{"path": "/v1/embeddings","data": {"model": "kanon-2-embedder", "texts": ["This is a confidentiality clause."], "task": "retrieval/query"}}
{"path":"/v1/rerankings","data":{"model":"kanon-universal-classifier","query":"Who is the Governor-General?","texts":["The Governor-General is Sam Mostyn.","The King is Charles III."]}}
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
path | The path of the API endpoint being invoked (e.g., `/v1/embeddings`). | One of `v1/embeddings`, `/v1/rerankings`, `/v1/extractions/qa`, and `/v1/classifications/universal`. | Yes |
method | The HTTP method used for the invocation (e.g., `POST`). Defaults to `POST`. | One of `POST`. | No |
headers | The HTTP headers to include in the invocation request. Defaults to `null`/`None`, in which case no additional headers are sent. | Must be a mapping of strings to strings. | No |
data | The data to be sent as the body of the invocation request. This can be any serializable object. Defaults to `null`/`None`, in which case no body is sent. | - | No |
Resources
Vendor resources
Support
Vendor support
To get in touch with our support team, you can reach out via the support form on our website: https://isaacus.com/support . We endeavor to respond within 24 hours.
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