AWS Bedrock Development Services
Engineering teams hire InterCode when generic API wrappers stop cutting it. Need tight cloud security, low token latency, and zero lock-in to a single foundation model provider? That is where our developers step in. We build, test, and ship production software directly on Amazon's managed artificial intelligence infrastructure.
What is AWS Bedrock?
Amazon Bedrock is a fully managed cloud platform providing access to high-performing foundation models through one uniform API. Your private company data never leaves your AWS environment. You get automated infrastructure, strict governance controls, and serverless execution without managing expensive GPU clusters yourself. We build enterprise systems with Amazon Generative AI, turning raw model APIs into reliable software products. Our team helps you move past basic prototypes into production environments running Bedrock AI and custom automated workflows.
How does AWS Bedrock pricing work?
Bedrock pricing depends on model selection and workload structure:
- On-Demand Inference. You pay strictly per 1,000 or 1,000,000 input and output tokens. Lightweight models like Nova Micro or Claude Haiku cost pennies per million tokens, while large reasoning models carry higher per-token rates.
- Batch Inference. You submit non-urgent, asynchronous jobs and receive a 50% discount on standard on-demand token prices.
- Cross-Region Routing. Global and geographic inference profiles route requests across AWS regions, often lowering token rates while preventing throttling spikes.
- Provisioned Throughput. You purchase dedicated model units on one-month or six-month commitments. This guarantees consistent low-latency throughput for heavy traffic.
- Model Customization. Training jobs are billed by tokens processed during fine-tuning. Serving a custom model requires purchasing provisioned model units.
Which foundation models are available on AWS Bedrock?
Bedrock offers direct access to frontier text, multimodal, code, and image generation models:
- Amazon: Nova Pro, Nova Lite, Nova Micro, Nova Premier, Nova Canvas (image), Nova Reel (video), and the Titan family.
- Anthropic: Claude 3.7 Sonnet, Claude 3.5 Sonnet, Claude 3.5 Haiku, and Claude 3 Opus.
- Meta: Llama 3.3, Llama 3.2 (lightweight and vision models), and Llama 3.1 (including the 405B parameter variant).
- Mistral AI: Mistral Large, Mistral Small, Pixtral, and Mixtral 8x7B.
- DeepSeek: DeepSeek-R1 and DeepSeek-V3 reasoning models.
- Cohere: Command R, Command R+, and Cohere Embed.
- AI21 Labs: Jamba 1.5 Large and Jamba 1.5 Mini.
- Stability AI: Stable Image Ultra, Stable Diffusion 3, and Stable Image editing tools.
AWS adds new model checkpoints and open weights regularly.
Why We Use AWS Bedrock
This technology is part of our AI and machine learning toolkit for building intelligent systems. We select AI tools based on model quality, inference performance, integration complexity, and whether they can be deployed in the environments our clients require — cloud, on-premise, or edge. Through a single managed API inside your existing AWS account, Amazon Bedrock gives access to a curated catalogue of foundation models. There is no infrastructure to provision, no GPU clusters to manage, and no model weights to store. Your data stays within your VPC and is never used to train the underlying models. Moreover, most corporate AI projects fail during security and data compliance reviews. Bedrock removes those hurdles by running inside your existing cloud architecture.
| Feature | What AWS Bedrock Delivers | How Our Team Implements It |
|---|---|---|
| Data Privacy | Zero data sharing with third-party model vendors | Encrypted S3 storage, private VPC endpoints, and KMS keys |
| Model Choice | Access to Claude, Nova, Llama, and Mistral via one API | Dynamic routing rules that reduce token costs by up to 60% |
| Tool Execution | Native agentic action groups and managed runtime | Autonomous agents hooked directly to internal APIs and databases |
| Safety Controls | Bedrock Guardrails with content filtering | Automated PII masking and prompt injection prevention |
- 0
- data shared with model vendors
- 60%
- token cost cut by dynamic routing
- 4
- model providers through one API
- 4
- stages in our delivery process
Bedrock itself is not an LLM. It operates as a serverless control plane, routing your prompt to the selected model provider inside an isolated container.
What We Build With AWS Bedrock (AWS Bedrock Use Cases)
InterCode builds systems for heavy production workloads, strict regulatory compliance, and tight cost control. We develop:
- Autonomous Task Agents. We build custom Bedrock agents that parse complex user inputs, call internal REST APIs, and execute multi-step database transactions.
- Managed Agentic Runtime. By using AWS Agent Core, we give your agents persistent memory across sessions, automated context retrieval, and sandbox environments for multi-tool execution.
- Enterprise Knowledge Retrieval (RAG). We connect Bedrock Knowledge Bases to OpenSearch Serverless, Aurora PostgreSQL, or external vector databases. Your teams query internal documentation with strict source citations and zero external data leaks.
- Smart Multi-Model Pipeline. We configure and route between varied AWS Bedrock models (including Anthropic Claude, Meta Llama, Amazon Nova, and Mistral) based on prompt difficulty. Fast, cheap models handle classification tasks, while heavy reasoning models handle complex analysis.
- Automated Guardrail and Safety Systems. We set up Bedrock Guardrails to automatically redact sensitive PII, block hallucinations, and stop prompt injection attacks across all Amazon Bedrock AI pipelines.
How Do We Work with AWS Bedrock
Our process is fast, technical, and grounded in real performance benchmarks.
- 1
Model Evaluation and System Architecture
No single foundation model fits every application. We benchmark multiple Bedrock models against your domain datasets to measure accuracy, inference speed, and token cost per request.
- 2
Building Agents and Tool Execution
We configure AWS Bedrock agents using Lambda action groups, OpenAPI specifications, and vector search hooks. We deploy them using AWS Agent Core so your agents handle session state, user authentication, and tool execution cleanly.
- 3
Security Hardening and VPC Configuration
We run all Bedrock Amazon workloads inside your private VPCs with strict IAM permission boundaries and AWS KMS key encryption. Base model providers never receive your data for training.
- 4
Lifecycle Maintenance and Cost Control
Model lifecycles change fast. We monitor AWS Bedrock news and review new Amazon Bedrock updates so your team avoids unexpected price bumps from Extended Access phases or sudden model deprecations. We also watch broader AWS AI news to adopt useful framework capabilities the week they drop.
Running foundation models on your own data?
We architect Bedrock deployments that keep prompts inside your VPC.
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Frequently Asked Questions
Amazon Bedrock is a fully managed cloud service that gives software teams access to top foundation models through a single API endpoint. Bedrock itself is not an LLM. Instead, it operates as a serverless control plane. You send an inference request through an SDK, AWS routes the prompt to the selected model provider inside an isolated container, and the system streams the token output back without exposing raw server hardware.
Companies run Bedrock to power production software that requires private data processing. It is used for the development of Retrieval-Augmented Generation (RAG), autonomous task agents, content and code generation, and document extraction.
AWS Bedrock fits teams running on AWS that require strict VPC isolation, IAM credential management, and multi-model flexibility. Azure OpenAI works best if your entire enterprise stack lives on Microsoft services and you exclusively build with OpenAI GPT models. The direct OpenAI developer API delivers raw model updates immediately, but you must build custom security boundaries, rate limit handlers, and network proxies yourself. Bedrock gives you model diversity without vendor lock-in. You switch between Claude, Nova, Mistral, and Llama through a single Converse API without rewriting your application logic.
No. AWS never uses your input prompts, uploaded documents, or generated completions to train base foundation models. Third-party model providers like Anthropic and Meta receive zero access to your traffic. All data stays inside your designated AWS account and region. Requests travel over TLS encryption in transit and rest behind AWS KMS keys. You can block public internet access entirely by routing traffic through AWS PrivateLink endpoints inside your VPC. Prompt logging remains disabled by default unless you route logs to your own encrypted S3 buckets or CloudWatch log groups for auditing.
Bedrock Agents is a fully managed, serverless orchestration system. It connects foundation models to AWS Lambda functions and Amazon OpenSearch vector indexes using clean OpenAPI specifications. AWS manages session memory, execution sandboxes, tool calls, and IAM permissions automatically. It fits teams that want fast deployment without running container clusters. LangGraph is an open-source Python framework designed for complex, cyclic multi-agent graphs and precise state machines. You control every step of execution logic, but your team must host, monitor, and scale the runtime infrastructure on services like Amazon ECS or EKS. Many engineering teams combine both: using LangGraph as the application logic layer while invoking Bedrock models and Guardrails as the backend inference engine.
SageMaker is built for machine learning teams that want to train, fine-tune, and deploy custom model checkpoints on dedicated EC2 instances. You configure compute instances, load balancers, and container endpoints yourself. Bedrock is completely serverless. You call pre-hosted foundation models instantly without managing GPU capacity. Compared to consumer APIs, Bedrock executes all requests inside your Amazon Virtual Private Cloud (VPC), keeping your network traffic isolated behind AWS Identity and Access Management (IAM) controls.
No. OpenAI models run exclusively on OpenAI and Microsoft Azure infrastructure. Bedrock provides access to foundation models from other developers, including Anthropic (Claude), Meta (Llama), Mistral AI, Cohere, AI21 Labs, and Amazon's proprietary Nova and Titan series.
No. When you invoke Claude models on Bedrock, your prompts and generated outputs stay inside your selected AWS Region. Data is not sent to Anthropic servers, and neither AWS nor Anthropic uses your application traffic to train foundation models. AWS also disables prompt logging by default, though you can route encrypted audit logs to your private Amazon S3 buckets or CloudWatch groups if your compliance team requires it.
Yes. AWS Bedrock is HIPAA eligible. You can process protected health information (PHI) once your organization signs an AWS Business Associate Addendum (BAA). 10.Is AWS Bedrock FedRAMP certified? Bedrock holds FedRAMP authorization. It has High baseline authorization across AWS GovCloud regions for foundational model deployments including Claude and Titan. 11.Does AWS Bedrock support MCP (Model Context Protocol)? Yes. AWS supports the open Model Context Protocol (MCP) standard across its developer toolset and Bedrock AgentCore workflows. MCP servers let foundation models query structured external data, invoke Lambda action tools, and connect directly to local development clients like Claude Code and Cursor. 12.What is AWS Bedrock AgentCore? Bedrock AgentCore is the runtime infrastructure designed for running production AI agents. It manages execution sandboxes, persistent multi-turn memory, MCP protocol connectivity, and tool calling. It handles agent session state automatically so developers avoid writing fragile custom session handlers. 13.What is an AWS Bedrock Knowledge Base? Bedrock Knowledge Bases is a managed RAG service. It connects directly to raw document repositories in Amazon S3, automatically handles parsing and chunking, generates embeddings, and indexes vectors into engines like Amazon OpenSearch Serverless, Pinecone, or Amazon Aurora. When a user asks a question, the Knowledge Base fetches relevant passages and passes them to the model for source-attributed answers. 14.Is AWS Bedrock free? AWS Bedrock does not have a perpetual free tier, but you only pay for what you consume. 15.How can you tell if AWS Bedrock is down? Check the AWS Health Dashboard for real-time operational status and regional service outages. For production applications, we configure Amazon CloudWatch alarms to track spikes in 5xx server error responses and client-side ThrottlingException rates.
Start Your AWS Bedrock Project
Talk to our AI engineers about building production generative AI on AWS Bedrock. We will design the right architecture — RAG, agents, fine-tuning, or Guardrails — for your use case.
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