What TPU Economics Look Like in Practice

Solar panels look cheap on paper. But installation, inverters, permits, and ongoing maintenance change the total cost of ownership. The panel price is just the beginning.

TPU economics work similarly. The hourly rate is attractive. The full picture requires accounting for conversion, ecosystem, and operational complexity.

The Hourly Rate Comparison

class HourlyRates:
    # Approximate GCP pricing (varies by region, commitment)
    gpu_h100 = {
        "on_demand": "$4.00-5.00/hour",
        "committed": "$2.50-3.50/hour",
        "spot": "$1.50-2.50/hour",
    }

    tpu_v5e = {
        "on_demand": "$1.20/hour (per chip)",
        "committed": "$0.80/hour (per chip)",
        "note": "Need 4-8 chips for comparable throughput",
    }

    comparison = """
    Single H100: $4.00/hour
    4x TPU v5e chips: $4.80/hour

    Comparable throughput, similar cost.

    But TPU pods at scale:
    256 TPU chips: $307/hour
    Equivalent GPU cluster: $500+/hour

    Scale is where TPU economics shine.
    """

The Conversion Cost Nobody Counts

def conversion_cost_analysis():
    """
    Converting PyTorch to JAX/TPU
    """
    engineer_time = {
        "simple_model": "2-4 weeks",
        "complex_model": "1-3 months",
        "custom_attention": "Add 2 weeks",
        "ongoing_maintenance": "10-20% of original effort per year",
    }

    # At $150K/year fully loaded engineer cost
    engineer_cost = {
        "simple_model": 15000,  # 4 weeks
        "complex_model": 50000,  # 3 months
        "year_one_maintenance": 7500,
    }

    breakeven = """
    If conversion costs $50K and saves $1K/month:
    Break-even: 50 months (4+ years)

    If conversion costs $15K and saves $2K/month:
    Break-even: 7.5 months

    Most teams underestimate conversion cost.
    """
    return engineer_time, engineer_cost

Where TPU Economics Actually Work

def tpu_economics_scenarios() -> dict:
    return {
        "works_well": [
            {
                "scenario": "High volume batch inference",
                "why": "Scale amortizes fixed costs",
                "example": "Processing 1B tokens/day",
                "savings": "30-50% vs GPU at scale",
            },
            {
                "scenario": "Already using JAX",
                "why": "No conversion cost",
                "example": "Research team with JAX expertise",
                "savings": "Immediate cost reduction",
            },
            {
                "scenario": "GCP-only deployment",
                "why": "Integration benefits",
                "example": "All infra on GCP, no multi-cloud needs",
                "savings": "15-25% plus operational simplicity",
            },
        ],
        "doesnt_work": [
            {
                "scenario": "Low volume",
                "why": "Conversion cost not amortized",
                "threshold": "Under $10K/month in compute",
            },
            {
                "scenario": "PyTorch-only team",
                "why": "Learning curve + conversion + maintenance",
                "cost": "Often exceeds savings",
            },
            {
                "scenario": "Latency-critical real-time",
                "why": "GPU single-node often faster",
                "note": "TPU optimized for throughput, not latency",
            },
        ],
    }

The Total Cost Model

def total_cost_of_ownership(
    monthly_tokens: int,
    deployment_duration_months: int,
) -> dict:
    # GPU path (H100)
    gpu_cost_per_token = 0.0001  # $0.10 per 1M tokens
    gpu_monthly = monthly_tokens * gpu_cost_per_token / 1_000_000
    gpu_setup = 5000  # Minimal setup, use existing infra
    gpu_total = gpu_setup + gpu_monthly * deployment_duration_months

    # TPU path
    tpu_cost_per_token = 0.00007  # $0.07 per 1M tokens (30% cheaper)
    tpu_monthly = monthly_tokens * tpu_cost_per_token / 1_000_000
    tpu_setup = 40000  # Conversion, testing, learning
    tpu_maintenance = 2000  # Per month ongoing
    tpu_total = tpu_setup + (tpu_monthly + tpu_maintenance) * deployment_duration_months

    return {
        "gpu_total": gpu_total,
        "tpu_total": tpu_total,
        "breakeven_months": tpu_setup / (gpu_monthly - tpu_monthly - tpu_maintenance),
    }

# Example: 100M tokens/month for 12 months
# GPU total: $125K
# TPU total: $148K (setup cost dominates)
# Breakeven: 20 months

# Example: 1B tokens/month for 12 months
# GPU total: $1.2M
# TPU total: $924K
# Breakeven: 5 months

The Operational Reality

class OperationalComparison:
    gpu_operations = {
        "debugging": "Extensive tooling, PyTorch debugger",
        "profiling": "NVIDIA NSight, torch.profiler",
        "community": "Abundant Stack Overflow, tutorials",
        "hiring": "Large talent pool",
        "on_call": "Team likely knows GPUs",
    }

    tpu_operations = {
        "debugging": "Improving but harder",
        "profiling": "TPU profiler, fewer examples",
        "community": "Growing but smaller",
        "hiring": "Harder to find JAX/TPU expertise",
        "on_call": "Steeper learning curve for new engineers",
    }

    hidden_costs = """
    When 3am incident happens:
    - GPU: Team likely knows how to debug
    - TPU: May need specialized knowledge

    This translates to:
    - Longer incident resolution
    - Higher risk of extended outages
    - More expensive on-call staffing
    """

The Decision Framework

def should_use_tpu(context: dict) -> str:
    # Strong yes
    if context.get("volume_tokens_per_month") > 500_000_000:
        if context.get("already_on_gcp"):
            if context.get("have_jax_team"):
                return "TPU likely wins economically"

    # Strong no
    if context.get("volume_tokens_per_month") < 50_000_000:
        return "GPU - volume too low to justify conversion"

    if context.get("team_pytorch_only"):
        return "GPU - conversion cost likely exceeds savings"

    # Maybe
    return "Do detailed TCO analysis before deciding"

The $4/hour vs $10/hour comparison is misleading. True economics require modeling conversion cost, maintenance overhead, and opportunity cost of engineering time. At scale, TPU can win. At typical volumes, GPU's ecosystem advantages often outweigh TPU's cost advantages.