Amazon Bedrock

Bedrock's model catalogue has expanded significantly:

Amazon Nova 2 models offer reasoning capabilities across different workload profiles, from everyday tasks to more complex multi-step work.

Anthropic Claude models including Claude Opus 4.5, Sonnet 4.5, and Haiku 4.5 provide frontier reasoning and coding capabilities with strong safety characteristics.

Meta Llama 4 Scout and Maverick models bring open-weight multimodal intelligence with exceptional context windows up to 10 million tokens.

Mistral AI models including the new Mistral Large 3 and Ministral 3 family offer efficient options for enterprise deployments.

Open-weight models from Google (Gemma 3), NVIDIA (Nemotron Nano 2), and others expand options for customisation and private deployment.

Bedrock's approach centres on choice and enterprise integration:

Model diversity: Access frontier and specialised models without separate vendor relationships. Compare and switch based on your specific requirements.

Serverless deployment: No infrastructure management. AWS handles scaling, availability, and capacity. You pay for what you use.

AWS integration: Native connection to Lambda, Step Functions, S3, DynamoDB, and the full AWS ecosystem.

AgentCore: New capabilities for building, deploying, and operating sophisticated AI agents at enterprise scale with quality evaluations and policy controls.

Customisation: Fine-tune models on your data, including reinforcement fine-tuning for improved accuracy.

Guardrails: Built-in content filtering and safety controls with configurable policies.

Bedrock makes sense for organisations that:

Already have significant AWS infrastructure. You want AI to live in the same environment as the rest of your stack.

Want flexibility across models. Different tasks benefit from different models, and you want to keep that option open.

Need enterprise security and compliance controls. You want governance that matches regulated or high-trust environments.

Prefer usage-based consumption. You want to avoid managing GPU fleets for many workloads.

Value the ability to switch providers. You want resilience as the model landscape changes.

If your technology strategy centres on AWS, Bedrock keeps AI consistent with everything else.

Our AWS AI work includes:

Intelligent applications using Bedrock models for natural language understanding, content generation, and decision support.

RAG systems combining Bedrock with Amazon OpenSearch or Kendra for retrieval-augmented generation applications.

Agents using Bedrock AgentCore to create AI that can take action, access data, and complete multi-step tasks with appropriate controls.

Processing pipelines integrating Bedrock with Lambda and Step Functions for automated document processing and data transformation.

Nova Act deployments for browser-based automation with AI agents that can take actions in web applications.

Bedrock addresses enterprise requirements:

Data privacy: Your data is not used to train base models. Customised models and data stay in your account.

Security: IAM integration, VPC endpoints, encryption at rest and in transit. Standard AWS security model applies.

Compliance: Bedrock inherits AWS compliance certifications. Appropriate for regulated industries with proper configuration.

Governance: CloudWatch logging, cost allocation tags, and usage monitoring. Visibility into how AI is being used.

Cross-region inference: Distribute workloads across regions for higher throughput and availability.

We help AWS-focused organisations use Bedrock effectively:

Evaluation: Understanding which models work best for your requirements.

Architecture: Designing applications that use Bedrock appropriately within your AWS environment.

Implementation: Building production applications with proper error handling, monitoring, and cost management.

Optimisation: Improving performance and economics of existing Bedrock deployments.

Use these prompts to explore architecture and governance decisions.

“Which model(s) should we use for each step of our workflow, and what evaluation will prove the choice?”

“What guardrails, logging, and approval points do we need for safe operation?”

“Where should we use retrieval (RAG), structured outputs, and deterministic checks to reduce errors?”