Generative AI on AWS — Production-Ready LLM Apps in Weeks

Generative AI on AWS refers to building LLM-powered applications using AWS managed services — primarily Amazon Bedrock (for accessing foundation models from Anthropic, Meta, Mistral, and Amazon without managing infrastructure) and Amazon SageMaker (for training, fine-tuning, and hosting custom models). AWS provides a complete stack for enterprise generative AI: managed model access, vector databases (OpenSearch, RDS pgvector), retrieval pipelines, orchestration (Step Functions, Bedrock Agents), guardrails, and security controls that keep your data within your AWS environment.

Direct third-party LLM APIs (OpenAI, Anthropic API) send your data to the model provider. For enterprise applications — especially in healthcare, fintech, or legal — this creates data residency, privacy, and compliance issues. Amazon Bedrock provides access to the same underlying models (including Claude from Anthropic) through an API that stays within your AWS environment. Your data never leaves your AWS account. You also get consolidated billing, IAM-based access control, CloudTrail logging of all model invocations, and integration with your existing VPCs and security controls.

RAG (Retrieval-Augmented Generation) is an architecture where a foundation model is given relevant context retrieved from your private knowledge base before generating a response. Without RAG, the model only knows what was in its training data — it cannot answer questions about your products, policies, customer history, or any other proprietary information. With RAG, you retrieve relevant documents from your knowledge base and include them in the model prompt, enabling accurate, grounded responses based on your data. Most enterprise GenAI applications — internal assistants, customer support bots, document Q&A — require RAG.

A focused prototype (demonstrating core functionality with real data) typically takes 2–3 weeks. Production-hardening — adding error handling, observability, guardrails, cost controls, and user authentication — adds another 4–6 weeks. More complex systems with fine-tuning, multiple agents, or integration with many data sources take longer. We recommend starting with a focused prototype on the highest-value use case, validating it with real users, then expanding.

The right model depends on your use case, latency requirements, and cost tolerance. Claude (Anthropic) excels at complex reasoning, long documents, and nuanced instruction-following — and is our default recommendation for most enterprise use cases. Llama (Meta) is a strong choice for workloads that need fine-tuning flexibility or cost optimization at high volume. Titan (Amazon) integrates natively with other AWS services. Mistral offers strong price-performance for classification and summarization tasks. We evaluate models against your specific use case and data before recommending one.

Security is built into every GenAI engagement from day one. We deploy all components within your VPC — no data traverses the public internet. Amazon Bedrock model providers never see your data. We implement Bedrock Guardrails to prevent PII leakage, prompt injection, and harmful content. All model invocations are logged to CloudTrail. IAM policies restrict model access to authorized roles. For regulated industries (healthcare, fintech), we ensure the architecture meets applicable compliance requirements (HIPAA, SOC 2, PCI DSS) before deployment.

Bedrock provides access to pre-trained foundation models via API — no infrastructure to manage, no training required. You invoke the model, pass a prompt, and get a response. SageMaker is a platform for the full ML lifecycle: data preparation, model training, fine-tuning, hosting, and monitoring. Use Bedrock when you want to build applications on top of existing foundation models. Use SageMaker when you need to train custom models on your own data, fine-tune models at scale, or host proprietary models. Many enterprise GenAI platforms use both: Bedrock for the primary LLM and SageMaker for custom models or embeddings.

Yes — our GenAI Discovery Call is specifically designed for this. We will assess your use case against four criteria: data availability (do you have the private data to power the application), business value (is the expected ROI clear), technical feasibility (can the use case be reliably solved with current LLM capabilities), and organizational readiness (does your team have the context to validate and iterate on the system). Not every use case is a good fit for GenAI, and we will tell you honestly if yours is not.

We implement hard budget limits at the AWS Bedrock account level, per-feature token budgets, and cost monitoring dashboards from sprint one. Most clients know their per-query cost within two weeks of going live — before they have committed to scale. Automated alerts trigger before spend thresholds are reached, not after.

We establish a golden dataset during development — a curated set of test cases drawn from your real user queries — and run automated evaluations against it on every deployment. This gives you a quality number, not just a demo. We also set up hallucination scoring and output validation so your team can monitor quality in production, not just at launch.