Build a Cloud Accelerator Procurement and Reservation Simulator

Simulate capacity procurement strategies across on-demand and reserved instances for open-model serving. Synthesize multi-cloud reservation proposals using hosted Claude.

Infrastructure Capacity Planner

Ingest historical inference request traffic patterns and multi-cloud accelerator instance pricing sheets.

Model baseline and peak capacity demand curves across simulated 1-year and 3-year commitment tiers.

Prompt hosted Claude to critique procurement risk, spot preemption vulnerability, and cost optimization levers.

Assemble a comprehensive procurement proposal dossier with projected cash flow impact.

Spreadsheet-style simulation UI with downloadable procurement commitment briefs.

Tabular CSV files containing normalized spot, on-demand, and reserved pricing for cloud GPU instances. Starter examples below are synthetic. They are not downloaded records.

AWS Neuron documentation Incorporates AWS accelerator instance deployment principles (such as Inf2 and Trn1 instances) into procurement models.

optuna, anthropic. Optuna optimizes the mathematical allocation between on-demand, reserved, and spot capacity to minimize financial cost. Hosted a currently available hosted Claude model accessed via API; models and costs are evaluated analytically.

Submit runnable source, pinned dependencies, setup commands, a working interface, example outputs, and a recorded end-to-end demo. Include tests and an action log.

Computes optimal instance blend minimizing overall financial spend for a given SLA threshold.

Integrates hosted Claude analysis to explain contract commitment risks.

Outputs clear tabular cost comparisons between procurement scenarios.

```json [ { "input": { "workload_type": "ProductionServing", "peak_gpus_needed": 32, "baseline_gpus_needed": 16, "on_demand_rate_hourly": 3, "reserved_1yr_rate_hourly": 1.8 }, "expected_output": { "recommended_reserved_count": 16, "recommended_on_demand_count": 16, "annual_projected_cost": 294336, "procurement_rationale": "Covering the steady 16 GPU baseline with 1-year reserved instances yields a 40% cost reduction on constant load." } }, { "input": { "workload_type": "AdHocResearch", "peak_gpus_needed": 8, "baseline_gpus_needed": 0, "on_demand_rate_hourly": 3, "reserved_1yr_rate_hourly": 1.8 }, "expected_output": { "recommended_reserved_count": 0, "recommended_on_demand_count": 8, "annual_projected_cost": 52560, "procurement_rationale": "Zero baseline usage indicates high variance. Pure on-demand or spot procurement prevents unutilized reserved capital waste." } } ] ```

Procurement simulations are purely financial modeling exercises without issuing cloud reservation API purchases. Use mock external writes. Require human review before any real deployment.