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Challenge

Multi-Tenant Power and Token Cost Observability Pipeline with CrewAI and Gemini 2.5 Flash

Attributing real hardware power consumption (Watts), GPU VRAM usage, and dollar costs to specific tenant workflows is critical for platform providers. You will create a multi-agent telemetry pipeline using CrewAI and Gemini 2.5 Flash to parse hardware metrics, aggregate tenant token usage, and generate audited cost reports. The framework will be evaluated on strict precision of dollar-and-watt attribution across overlapping execution windows.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Build a CrewAI agent team powered by Gemini 2.5 Flash to ingest raw hardware telemetry metrics and attribute power and token costs to tenant workloads.

Delivery guide

How work is evaluated

Evaluation

Evaluates CrewAI team's ability to accurately divide raw power and VRAM metrics across concurrent tenant requests.

Datasets

Shared data for this challenge

Review public datasets and any private uploads tied to your build.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

billing_sum_integrity

Ensures total tenant billed cost equals aggregate host power expense

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

attribution_accuracy_pct

Accuracy percentage of power attribution against analytical ground truth • target: 98 • range: 0-100

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

What you should walk away with

  • Define specialized CrewAI agents (TelemetryCollector, CostCalculator, AuditReporter)

  • Integrate Gemini 2.5 Flash LLM for high-speed analysis of Prometheus hardware streams

  • Calculate proportional GPU power (Watts) and energy (kWh) per tenant based on execution duration and memory footprint

  • Produce compliant audit logs matching tenant billing SLAs

Resources and assets

Reference links and supporting material

Dataset notes

Time-series GPU power logs (NVML) and concurrent tenant API request traces.

How this agent runs

Evaluates CrewAI team's ability to accurately divide raw power and VRAM metrics across concurrent tenant requests.

Challenge input

JSON with node_power_watts_series, energy_cost_per_kwh, and tenant_sessions array

CrewAI

Provides task orchestration and role-based agent collaboration.

Gemini 2.5 Flash

Offers high throughput and fast responses for dense telemetry streams.

Evaluated output

JSON detailing per_tenant_cost_usd, total_kwh_consumed, and attribution_accuracy_pct

Checks for
  • Ensures total tenant billed cost equals aggregate host power expense
  • Accuracy percentage of power attribution against analytical ground truth • target: 98 • range: 0-100
Proof of success
  • Benchmark: Green-AI-Telemetry-Bench
  • Attribution Accuracy Pct target: 98
  • 1 public reference case
Runtime evidence
  • Python execution harness
  • Python sandbox (unavailable on Versalist)
View technical recipe

Configured tools

Action Space
  • CrewAI · Required
  • crewAI · Optional
Policy Serving
  • Gemini 2.5 Flash · Optional

Evaluation contract

  • billing_sum_integrity · Weight 1
  • attribution_accuracy_pct · Weight 1

Recipe state

This is a preview. The configuration can change before the evaluation recipe is locked.

Frequently Asked Questions about Multi-Tenant Power and Token Cost Observability Pipeline with CrewAI and Gemini 2.5 Flash