Answer: Use Amazon Bedrock with On-Demand Throughput.

## Detailed Analysis of the Question Requirements The question presents a scenario with several key constraints: 1. **Low pilot usage with no performance concerns** - The company is in an early phase where minimal resources are sufficient. 2. **Unpredictable future usage after full deployment** - The company cannot forecast how much the application will be used once fully launched. 3. **Cost minimization as primary objective** - Keeping expenses low is the critical requirement. ## Evaluation of Each Option ### **Option A: Use GPU-powered Amazon EC2 instances** - **Why this is unsuitable**: GPU-powered EC2 instances involve provisioning dedicated compute resources with fixed costs, regardless of actual usage. This requires capacity planning and upfront commitment, which contradicts the requirement for unpredictable usage patterns. The company would pay for idle resources during low-usage periods, increasing costs unnecessarily. ### **Option B: Use Amazon Bedrock with Provisioned Throughput** - **Why this is unsuitable**: Provisioned Throughput in Amazon Bedrock involves committing to a minimum level of usage with guaranteed throughput. While it offers predictable pricing, it requires capacity planning and commitment to minimum usage levels. This is inappropriate for unpredictable usage patterns as it could lead to paying for unused capacity. ### **Option C: Use Amazon Bedrock with On-Demand Throughput** - **Why this is optimal**: Amazon Bedrock's On-Demand Throughput is a pay-as-you-go model where you only pay for what you use, without any minimum commitments. This perfectly aligns with: - **Low pilot usage**: No upfront costs or minimum commitments during the pilot phase - **Unpredictable future usage**: Scales automatically with usage without requiring capacity planning - **Cost minimization**: Eliminates the risk of paying for unused capacity; costs directly correlate with actual usage - **Managed service benefits**: No infrastructure management overhead, allowing focus on application development ### **Option D: Use Amazon SageMaker JumpStart** - **Why this is unsuitable**: While SageMaker JumpStart provides pre-built models and solutions, it typically involves deploying models on SageMaker endpoints with provisioned instances. This requires capacity planning and fixed costs for running endpoints, which doesn't align with unpredictable usage patterns. The compute costs would be incurred regardless of actual inference requests. ## Key AWS Service Considerations Amazon Bedrock's On-Demand Throughput offers: 1. **No minimum commitments** - Pay only for tokens processed 2. **Automatic scaling** - Handles variable workloads without manual intervention 3. **Managed infrastructure** - No need to provision or manage underlying resources 4. **Cost optimization** - Perfect for scenarios with unpredictable or variable usage patterns This approach allows the company to start with minimal costs during the pilot phase and scale seamlessly as usage grows, without the risk of over-provisioning or under-provisioning resources.