Generative AI & LLM Deployment on AWS

Deploy large language models in production with structured architecture, secure integration, and controlled inference scaling.
We design and implement Amazon Bedrock and custom LLM environments on AWS, engineered for latency control, cost visibility, and operational stability.

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Access to foundation models is straightforward. Deploying them reliably in production is not.

Inference latency, token consumption, secure data access, and integration with existing applications determine whether generative AI becomes a stable product capability.

We engineer structured LLM deployments on AWS — ensuring generative AI operates within controlled, scalable, and observable cloud environments.

Built for Teams Deploying Generative AI in Production

This service supports organisations that are:

Launching AI-native products powered by LLMs
Embedding generative AI features into SaaS platforms
Evaluating Amazon Bedrock versus self-hosted model deployment
Designing Retrieval-Augmented Generation (RAG) systems
Scaling LLM inference under real user demand

Generative AI must be engineered deliberately for production from day one.

While accessing foundation models is straightforward, production deployment introduces complexity:

Organisations commonly struggle with:

Inference latency under concurrent load
Unpredictable token consumption and cost growth
Insecure exposure of LLM endpoints
Data governance risks in RAG architectures
Model integration outside structured delivery pipelines
Limited visibility into prompt-to-response lifecycle behaviour

Without deliberate architecture, LLM systems become unstable, expensive, and difficult to scale.

Amazon Bedrock Integration

Amazon Bedrock enables access to managed foundation models without infrastructure overhead.

We implement secure Bedrock deployments with structured IAM access, private networking, cost visibility, and performance tracking — ensuring managed LLM services operate as governed components of your architecture.

Custom & Containerised Model Deployment

Where flexibility or model control is required, we design containerised inference environments on AWS, including GPU-enabled clusters and autoscaling policies.

This approach enables custom model control while maintaining operational stability and scalability.

Retrieval-Augmented Generation (RAG) Architecture

RAG systems introduce additional infrastructure beyond the model layer.

We architect secure vector database integration, controlled ingestion pipelines, embedding workflows, and governance boundaries between data and model interaction.

RAG deployments must balance relevance, performance, and security.