LLMOps Framework for AWS

Deploying LLM-powered applications in production introduces unique challenges across infrastructure, cost management, evaluation, and governance. Traditional MLOps approaches are not designed to handle open-ended outputs, real-time inference costs, or the operational complexity of modern LLM workloads.

LLMOps in AWS is a comprehensive lifecycle solution that helps organizations design, deploy, and operate production-grade LLM applications on AWS using services such as Amazon Bedrock and Amazon SageMaker. Our framework accelerates time-to-production while ensuring security, observability, and cost efficiency. This offering is delivered as a structured 5+ week engagement, designed to take teams from initial design through production-ready LLMOps foundations.

Key Challenges Addressed

Complexity LLM applications require scalable infrastructure, orchestration across inference, prompt management, vector search, and downstream workflows. Our LLMOps framework simplifies deployment, supports controlled rollouts, and integrates seamlessly with AWS-native services and existing data pipelines to enable patterns such as Retrieval-Augmented Generation (RAG), agents, and automation.

Cost & Performance Real-time LLM inference can be expensive without proper controls. We implement autoscaling, workload isolation, inference optimization, and cost attribution to ensure high performance while keeping costs predictable and optimized.

Evaluation & Quality LLM outputs are difficult to evaluate with traditional metrics. Our approach enables automated evaluations using custom metrics, domain-specific benchmarks, and human-in-the-loop feedback. We track quality, drift, and regressions to support safer releases and continuous improvement.

Our LLMOps Framework

• Our AWS-native LLMOps framework provides a secure, end-to-end foundation for building and operating LLM-based applications:
• AWS-native architecture using Amazon Bedrock and Amazon SageMaker, with Infrastructure as Code (AWS CDK or Terraform)
• LLM enablement including RAG, vector search, prompt versioning, and agent orchestration with Amazon Bedrock Agents
• CI/CD & automation through GitOps pipelines, automated testing, and safe deployment strategies such as blue/green and canary releases
• Monitoring & evaluation with Amazon CloudWatch metrics and logs, model quality tracking, drift detection, and token usage and cost visibility
• Security & governance using AWS IAM, encryption in transit and at rest, audit logging, and Amazon Bedrock Guardrails
• Cost optimization through autoscaling, inference optimization, AWS Budgets, cost attribution, and rightsizing recommendations

Value Delivered

• Faster transition from PoC to production
• Lower inference and operational costs
• Observable, governable, production-ready LLM applications on AWS