Sold by: CUBIG AIÂ
Deployed on AWS
LLM Capsule is a privacy-preserving containerized engine that enables enterprises to safely use Large Language Models without exposing sensitive data. It provides real-time de-identification, token-level masking, and synthetic data generation to ensure regulatory compliance while maintaining high model performance.
Overview
LLM Capsule is designed to help organizations securely adopt and scale Generative AI by eliminating the risks of sensitive data exposure. Unlike traditional de-identification tools that degrade accuracy, LLM Capsule applies advanced Named Entity Recognition (NER) and privacy-preserving algorithms to anonymize data streams in real time, ensuring both compliance and reliability.
Key features include:
Privacy-first AI adoption: Sensitive information such as PII and financial data is automatically masked or pseudonymized before reaching the LLM.
Synthetic data generation: Built-in DTS engine produces high-fidelity synthetic datasets, preserving up to 99% of original data utility while ensuring zero traceability.
Flexible deployment: Delivered as a lightweight container, LLM Capsule integrates with existing infrastructures on AWS EKS, ECS, or SageMaker environments.
By combining security, compliance, and high-performance AI processing, LLM Capsule empowers enterprises in regulated industries such as finance, healthcare, and public sector to safely leverage the full potential of LLMs.
External Dependencies & Requirements:
- No external database required.
- No external paid APIs required. All AI processing is performed locally within the container.
- Container runs as non-root user (UID: 1000) for enhanced security.
- All included packages are sourced from PyPI with commercial-friendly licenses (MIT, BSD, Apache-2.0).
Highlights
- Real-time privacy protection with advanced NER and token masking for confidential data.
- High-fidelity synthetic data generation (up to 99% utility preserved) for safe AI training and testing.
- Seamless AWS integration via containerized deployment on EKS, ECS, and SageMaker environments.
Details
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Operating system
Linux
Deployed on AWS
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Pricing
Pricing is based on the duration and terms of your contract with the vendor. This entitles you to a specified quantity of use for the contract duration. If you choose not to renew or replace your contract before it ends, access to these entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/month |
|---|---|---|
License | LLM Capsule License | $4,000.00 |
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test refund policy
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Delivery details
unit delivery option
Supported services: Learn moreÂ
- Amazon ECS
Container image
Containers are lightweight, portable execution environments that wrap server application software in a filesystem that includes everything it needs to run. Container applications run on supported container runtimes and orchestration services, such as Amazon Elastic Container Service (Amazon ECS) or Amazon Elastic Kubernetes Service (Amazon EKS). Both eliminate the need for you to install and operate your own container orchestration software by managing and scheduling containers on a scalable cluster of virtual machines.
Version release notes
Version 1.0.3 - Security & Compliance Update
- Container runs as non-root user (UID: 1000) for enhanced security
- Added health check endpoint (/health)
- Removed unnecessary dependencies
- Optimized container image structure
- Updated documentation for external dependencies
Additional details
Usage instructions
Usage Instructions
- Prerequisites
- Docker runtime environment (Amazon ECS, EKS, etc.)
- No external database required
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Environment Variables Refer to product documentation for required environment variables.
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Run Container docker run -d -p 8080:8080 <image-uri>
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Verify Health Check curl http://localhost:8080/health Expected response: {"status":"healthy"} or similar
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External Dependencies
- No external database required
- No external paid APIs required
- All AI processing is performed locally within the container
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Included Packages (from PyPI) fastapi, uvicorn, torch, transformers, huggingface-hub, pandas, numpy, cryptography, pydantic All packages use permissive licenses (MIT, BSD, Apache-2.0) for commercial use.
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Security
- Container runs as non-root user (UID: 1000)
- Health check endpoint: /health
- Exposed port: 8080
Support
Vendor support
Please reach us at contact@cubig.ai for any assistance or questions.
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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