Amazon Bedrock consulting and implementation

What the engagement should achieve

We agree one useful task, its permitted inputs, expected output, failure behavior and acceptance criteria before expanding the build. The outcome can be a source-linked internal assistant, a document-processing step in your product, or an approval-bound agent workflow. It includes the application integration and operating evidence, not only a working model request.

Scope, timing, acceptance checks, assumptions, client dependencies and commercial terms are agreed for each engagement. Account access, model availability, data approvals and third-party charges remain explicit dependencies. We do not promise a universal deployment timeline, savings percentage or error-free AI.

Typical starting situations include a Bedrock prototype without release criteria, an existing AWS product adding AI, several API integrations with unclear ownership, or an internal assistant that cannot enforce the same access rules as its source systems.

Should you use Bedrock instead of paying Claude or OpenAI separately?

First separate employee subscriptions from application APIs. ChatGPT and OpenAI API billing are separate systems. Paid Claude products and Console API access are also distinct, but current Max and Team plans include monthly API credits. Check the actual plan, credit terms and remaining obligations. Moving application requests to Bedrock does not automatically replace the employee tools, interfaces or entitlements your team uses.

OpenAI billing guidance · Claude billing guidance

For an application, Bedrock gives you a route to supported foundation models within AWS. That can fit a team already operating its identity, infrastructure and cost controls there. Some third-party model charges appear on the AWS bill through AWS Marketplace; AWS's Claude Haiku 4.5 card describes this arrangement. Fewer billing destinations may simplify an operating process, but it does not establish lower total cost.

Amazon Bedrock overview · Haiku 4.5 model card

The supported model catalogue and API features change. Availability on Bedrock does not mean every direct-provider feature or future version is interchangeable. We record the exact model, API, endpoint, profile, Region and account prerequisites for the proposed workload and check the current official catalogue.

Amazon Bedrock model catalogue

Choose the smallest implementation that solves the task

Answers grounded in permitted business information

For an internal knowledge assistant, we start with the authoritative sources, user access rules, freshness requirements and questions the corpus can answer. Bedrock Knowledge Bases can provide managed retrieval-augmented generation. A custom retrieval layer may be preferable when your application needs different access enforcement, indexing or retrieval behavior.

How Knowledge Bases works

The deliverable includes ingestion and deletion behavior, retrieval evidence, source references and out-of-scope responses. A citation does not by itself establish that the user was permitted to retrieve its document. We test missing evidence, revoked access, stale documents and cross-tenant requests independently of answer fluency. Provider-neutral LLM and RAG engineering remains available when Bedrock is not the chosen platform.

LLM and RAG engineering

Document and product workflows

Summaries, extraction and classification need an output contract. We define required fields, preserved exceptions, review requirements and downstream use before selecting a model. A document assistant that produces a draft for review has a different consequence boundary from a system that changes a customer record. Batch, synchronous and streaming paths are evaluated against the actual workflow deadline rather than selected because they look more interactive.

For example, a proposed support assistant may retrieve a user's permitted policy revision, draft an answer with its source and stop when the exception cannot be supported. The first release need not update the ticket or send the reply. Later write access would be a separately scoped change with approval, duplicate-effect prevention and recovery checks. This is an illustrative design, not an Ampity customer result or executed AWS test.

Agents that use tools

Use an agent when the task needs a sequence of tool-supported decisions and a simpler controlled workflow is insufficient. The application must enforce allowed operations, the user's authority, approval requirements, action budgets and recovery. Prompt instructions alone cannot grant or restrict business-system permissions.

AgentCore is a candidate for selected runtime, identity, tool access and observation needs. It can work with models inside or outside Bedrock, so using AgentCore does not automatically move every model charge or processing dependency to Bedrock. We select only the services the workflow needs and review their support, data handling and operating requirements.

AgentCore overview

We agree the tool boundary before implementing it. High-consequence actions may require human approval tied to the exact proposed action. Unknown outcomes need reconciliation before replay. The broader implementation discipline is covered by agentic workflows.

agentic workflows

Protect the data and select the exact model route

We record who may submit each input class, which source data may be retrieved, the permitted processing locations, and which logs or stored results may be retained. The request's source Region is not a blanket single-Region processing guarantee. The Bedrock inference-placement paper provides the deeper route-admission framework.

Bedrock inference-placement paper

API and feature support differs between Bedrock Runtime and Mantle and between individual models. The design must also identify actual workload identity and authorization, rather than treating a valid credential as sufficient permission. We check those choices against the current endpoint documentation and selected model card.

Bedrock endpoints

Guardrails can add configured content and grounding checks where supported. They supplement access controls and task evaluation; they do not certify the output or authorize a tool action. We test blocked answers, missed restrictions and useful requests rejected by the selected configuration.

Bedrock Guardrails

Observation needs its own data decision. Optional model invocation logging can retain request and response content. We agree the fields, destinations, access, retention and deletion checks instead of enabling full payload collection by default. AgentCore and application traces are separate retained representations to review.

Invocation logging

Establish release criteria before measuring a promising demo

The evaluation uses representative permitted inputs, expected outputs and adverse cases. We test retrieval and generation separately where appropriate, then check the complete user task. The acceptance record identifies which model, prompt, source revision, route and application build produced each result.

Thresholds depend on the task and its consequences. We do not copy one accuracy percentage across unrelated use cases. The production AI evaluation workbook helps you prepare the dataset and acceptance discussion without contacting us.

production AI evaluation workbook

Compare cost per accepted task, not only the token price

We review model usage, retries, selected cache behavior, embeddings, retrieval and vector storage, document processing, logs, data movement and network costs. Add application hosting, support, review effort, engineering and migration costs within the agreed comparison boundary. Existing seats, retained provider APIs and parallel operation stay visible until evidence supports removing them.

Use dated rates for the exact model, route, mode and contract. Confirmed credits belong in a separate line with their eligibility, expiry and applicable charges; unconfirmed credits remain an assumption. Never make a recurring operating case depend on an unspecified funding promise.

The same workload must meet the required quality and processing boundary on each route before a price comparison can guide selection. A route with lower cost but unacceptable output is not a cheaper accepted service. Estimate cost per accepted result can support the discussion within its declared boundary; it is not an AWS quote.

Estimate cost per accepted result

Capacity is also route-specific. Runtime's model quota is shared across its inference APIs, while Mantle allocation is separate. We inspect the account's applied quotas, other consumers and the workload's input/output and retry envelope. A requested increase is not available capacity, and a short response in a demo does not establish production headroom.

Runtime quotas

What your team receives

The agreed delivery can include a task and data-boundary record, architecture decisions, implemented application integration, versioned infrastructure and configuration, a scoped retrieval or tool layer, and evaluation evidence. Release work includes fallback or held-work behavior, cost and latency observation, deployment and rollback controls, and a controlled exposure plan.

Handover includes runbooks, alert ownership, the model and prompt change process, retained-data decisions, recovery checks and the outstanding decision register. Managed operation, coverage hours and continuing optimization are separate scope choices. Your team should be able to explain what was accepted, what remains excluded and who acts when a control fails.

Relevant AWS credentials, with their limits

Ampity is an AWS Advanced Tier Services Partner. Our AWS practice also holds separate Amazon ECS Delivery, Amazon RDS Delivery and AWS Transfer Family Delivery designations. These are company and named-service credentials, not a claim of a Bedrock-specific specialization or proof that your AI workload is ready. Review our AWS delivery practice and agree workload-specific acceptance evidence.

our AWS delivery practice

Is this the right engagement?

This work fits a team with an identifiable AI workflow, owners for data and application decisions, and access to permitted evaluation and operating evidence. It can begin with an implementation review when the platform choice is unsettled.

Bedrock may be the wrong route when the required feature or processing location is unsupported, the existing provider contract is essential, or a deterministic change solves the problem more directly. We should pause when no one can approve the data use or define a useful accepted output. A badge, a model catalogue entry or a lower quoted token price cannot resolve those gaps.

Start with one workflow

Tell us what users need to complete, what your prototype or existing integration already does, and what prevents release. We will help define the implementation boundary and the evidence needed to accept it.

Discuss your Bedrock implementation. The supporting guides and calculator above are available without submitting an enquiry.

Discuss your Bedrock implementation

AWS Advanced Tier Services Partner

Company-level AWS credentials for our Bedrock implementation practice, not a Bedrock-specific designation

Ampity is an AWS Advanced Tier Services Partner. This company-level credential supports our AWS practice. Scope, success measures and implementation decisions still depend on your workload.

Questions buyers ask