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, hardware depreciation (across a 3-yr timeframe), 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 $50K default reflects a typical enterprise AI team, sitting just below Ramp's 90th percentile of business AI spend at $73,030 per month. 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 pricing: AMD: $450,000, based on the Supermicro AS-8126GS-TNMR 8U GPU SuperServer, configured with 8× AMD Instinct MI325X GPUs. NVIDIA: $600,000, based on a similar Supermicro GPU SuperServer, such as the SYS-821GE-TNHR, configured with 8× NVIDIA H200 GPUs. The difference reflects the higher estimated cost of an H200-based server configuration. Both are baseline estimates before discounts and depreciation; the Hardware discount slider models negotiated pricing directly.
5. Hardware depreciation: standard straight-line depreciation with no salvage value. Annual hardware cost = (list price × (1 − discount)) ÷ depreciation years. At the defaults that's $450,000 × (1 − 15%) ÷ 3 years = $127,500 per year. Only years inside the depreciation window carry a hardware charge, so a 1- or 2-year depreciation period leaves later years with no hardware line.
6. Operating costs ($50,000/yr per node): applied to both newly-purchased and already-owned hardware, because the box has to be operated either way. 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.
7. 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.
8. 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).
9. 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.
10. Sunk-cost hardware path: when GPUs are already available in house, hardware CapEx = $0. Operating costs still apply as an ongoing charge.