See how much Inference Launchpad can save you

All figures are illustrative. Your actual costs will vary with electricity rates, facility efficiency, hardware utilization, model mix, and other operational factors.

1. Baseline scenario: "Without Launchpad" assumes 100% of AI workloads run against frontier model APIs (Anthropic, OpenAI, or equivalent). Your input is that current monthly bill. Year 1 = monthly spend × 12; each later year multiplies the prior year by (1 + annual growth). "With Launchpad" for each year factors in the annual subscription cost of Spectro Cloud Inference Launchpad, the operating costs, and the annual baseline spend on frontier models.
2. Minimum monthly spend and default: This calculator is built for teams running AI at enterprise scale, where dedicated local inference starts to earn its keep. $35K is a realistic mid-market threshold; it's a modeling floor, not an industry benchmark. The $100K default reflects an enterprise AI team at scale: above Ramp's 90th percentile of business AI spend at $73,030 per month, below the $140,842 monthly average. For context, Gartner reports that organizations spent an average of $1.9 million on GenAI initiatives in 2024. See Gartner: Hype Cycle for Artificial Intelligence and Ramp: How Much Do AI Tokens Cost Businesses? (avg $140,842/mo; 90th percentile $73,030; 95th $211,409; 99th $831,338).
3. Growth default (30% YoY): conservative against Gartner's forecast of 47% worldwide AI spending growth for 2026. See Gartner: Worldwide AI Spending to Grow 47% in 2026.
4. Hardware: this calculator assumes you already own the GPU hardware to run the Inference Launchpad on. Hardware is treated as a sunk cost, so no purchase price or depreciation appears in the model. If you need a solution that includes hardware, check out AMD Instinct™ Coder.
5. Operating costs ($50,000/yr per node): applied to the hardware you already own, because the box has to be operated either way; spread evenly across the 12 months. Split in the breakdown table: approximately $11,000/yr for power and cooling, plus $39,000/yr for rack, IT ops, network, and other datacenter overhead. Power and cooling ($11,000/yr): at 85% realistic utilization, the node uses about 79,854 kWh of electricity per year. After accounting for typical data center cooling and facility overhead using a 1.54 PUE, total annual energy use rises to about 122,976 kWh. At the U.S. industrial average electricity rate of $0.0862/kWh, that works out to approximately $10,600 per year, rounded to $11,000. Formula: 79,854 kWh × 1.54 PUE × $0.0862/kWh = ~$10,600/year, rounded to $11,000. Actual costs will vary by region, electricity rates, utilization, facility efficiency, and cooling model. Sources: EIA Electric Power Annual, Table 2.7; Uptime Institute Global Data Center Survey 2025.
6. Launchpad subscription: $50,000 per server per year, billed annually up front. Treated as a Year 1 upfront cost with renewals at the start of Years 2 and 3.
7. Cost-effectiveness of on-prem inference: a Dell / ESG independent analysis found on-premises inference 2.9× to 4.1× more cost-effective than API-based service for sustained workloads. See ESG: Understanding the Total Cost of Inferencing LLMs on-premises with Dell (PDF).
8. Developer-usage tiers (Light $100, Typical $550, Heavy/agentic $1,000 per developer per month): illustrative modeling points inside the $100 to $1,000+ range Gartner reports for AI coding and agentic token costs. Gartner sets the range; the three tiers here are our anchors for translating your monthly spend into approximate developer counts. Estimated developers per tier = monthly spend ÷ that tier's per-developer cost, rounded to the nearest whole developer. See Gartner: How to Plan for Escalating AI Token Costs. Ramp's 2026 transaction data (Ramp: How Much Do AI Tokens Cost Businesses?) adds supporting evidence at the organization level.