OpenAI on Bedrock Workload Benchmark

Most organizations already have OpenAI or Azure OpenAI workloads in pilot or early production. The challenge is not whether AI works—it is whether those workloads can scale with the right cost control, governance, security, quality, and architecture.

The OpenAI on Bedrock Workload Benchmark is a fixed-scope engagement that helps customers make an evidence-based decision before committing to broader migration, modernization, or production investment.

Instead of evaluating multiple use cases or forcing a platform decision, Compass UOL focuses on one existing or near-production workload tied to a clear business outcome. We establish the current-state baseline, confirm available data and validation criteria, and run a controlled comparison against a Bedrock-aligned approach using agreed inputs.

The result is a decision-ready view of whether the workload should be:

Maintained as-is

Optimized

Selectively modernized

Migrated

Validated further

Deferred

This is not a migration project, implementation, or model bake-off. It is a focused benchmark designed to reduce uncertainty and help the customer decide what to do next—before committing time, budget, or engineering effort.

Customers gain visibility into:

Cost drivers and usage patterns

Governance, security, and auditability readiness

Workload-specific quality expectations

Architecture implications and flexibility

Production readiness risks and gaps

The engagement aligns to AWS Bedrock adoption and AI Assessment motions and may be eligible for AWS funding, subject to approval.

Buyer Problem / Business Trigger

Rising AI costs without clear cost drivers or predictability

Workload works in pilot but lacks production readiness (governance, security, scale)

Need evidence before committing to migration, modernization, or Bedrock adoption

Delivery Model

Discovery and baseline confirmation (workload, metrics, inputs)

Bedrock-aligned benchmark and side-by-side comparison

Business case, target architecture, and final decision playback

Assessment / Engagement Scope

One existing or near-production OpenAI workload

Workload-specific evaluation across cost, quality, governance, security, and architecture

Customer-provided inputs: usage data, scenarios, validation criteria

No implementation, migration, or multi-workload scope

Expected Output / Deliverables Benchmark report (current vs. Bedrock-aligned approach) High-level target architecture and business case Decision-ready recommendation and next steps

Customer Decision Questions

This offer helps the customer answer:

Should this workload stay, be optimized, modernized, or moved to Bedrock?

Are cost, governance, and quality strong enough to scale?

Is there a justified business case for further AI investment?