LISA: Enterprise-Grade LLM Inference Solution on AWS Dedicated Cloud

Enterprise LLM deployment faces the core conflict between AI productivity improvement and data security compliance. While public cloud services are convenient, they pose high risks for sensitive industry data. The open-source LISA project from AWS Labs, as a dedicated cloud LLM inference solution, provides private deployment, security compliance, and elastic scaling capabilities to meet the security and performance needs of enterprise AI applications.

With the popularization of generative AI, enterprises have strict requirements for data sovereignty and privacy protection (e.g., EU GDPR, China Data Security Law). The core advantages of the dedicated cloud model are physical isolation and full control, where data does not leave the controlled environment. It is suitable for processing PII/PHI, compliance scenarios, edge computing, IP-sensitive activities, etc. LISA aims to enable enterprises to obtain public cloud-level inference capabilities on dedicated clouds.

LISA (LLM Inference Solution for Amazon Dedicated Cloud) follows cloud-native principles, with core components including:

• Model Service Layer: Based on vLLM/TGI frameworks, supports multiple models, containerized deployment;
• Orchestration and Scheduling Layer: Kubernetes manages resources, auto-scaling;
• API Gateway Layer: Unified RESTful API, compatible with OpenAI format;
• Security Monitoring Layer: Integrates AWS security practices (IAM, VPC, CloudWatch), supports enterprise security integration.

LISA supports deployment modes from single-node testing to multi-region production clusters, avoiding over-investment. It offers open model support: not tied to specific models, allowing deployment of open-source models like Llama/Mistral/Falcon or licensed commercial models to avoid vendor lock-in; supports multiple model instances in the same cluster, distributing requests via routing, suitable for internal MaaS platforms.

LISA optimizes performance and cost:

• Inference Performance: Integrates vLLM, uses PagedAttention and Continuous Batching to improve GPU utilization and throughput;
• Auto-scaling: Dynamically adjusts the number of instances based on load, balancing service quality and cost;
• Heterogeneous Computing: Supports dedicated accelerators like AWS Inferentia to improve cost-effectiveness;
• Cost Monitoring: Provides resource usage reports and analysis to help optimize deployment strategies.

LISA meets enterprise security and compliance requirements:

• Data Encryption: Transport layer (TLS) and storage layer encryption;
• Access Control: RBAC mechanism for fine-grained permission management;
• Audit Logs: Complete request records to meet compliance audits;
• Network Isolation: VPC deployment and private subnets to avoid public exposure;
• Identity Integration: Supports Active Directory/Okta, etc., to implement SSO.

LISA is open-sourced under the Apache 2.0 license, with advantages including:

• Transparency: Enterprises can review the code to eliminate security concerns;
• Customizability: Freely modify and extend functions without vendor restrictions;
• Community Support: Share deployment experiences and best practices;
• Sustainability: Even if AWS policies change, enterprises can still maintain the code. AWS Labs commits to continuous maintenance and welcomes community contributions.

Enterprises are recommended to implement LISA in phases: first pilot non-critical businesses to accumulate experience and verify performance and cost, then expand to core systems. At the same time, it is necessary to establish supporting AI governance frameworks such as model evaluation, prompt specification, output review, etc. In the future, the importance of dedicated cloud inference solutions will increase. LISA provides a technical foundation for enterprises to balance AI dividends and data control, which is key to the maturity of enterprise AI.