See how much Inference Launchpad can save you
PaletteAI Inference Launchpad is a turnkey local inference solution that can reduce your frontier model token spend while unlocking valuable insights into model usage.
Hardware needs
Existing capacity is treated as a sunk cost.
Server assumptions:
AMD: Supermicro 8U GPU SuperServer. NVIDIA: large Dell server.
AMD: Supermicro 8U GPU SuperServer. NVIDIA: large Dell server.
Typical 10–20%; large-volume deals reach 25–40%.
Straight-line amortization of one-time CapEx.
Great — hardware CapEx is treated as sunk. Savings shown reflect token spend reduction only.
3-year impact
Total savings of 0% with Launchpad
3-yr frontier spend without Launchpad
$0
3-yr spend with Launchpad
$0
Total 3-yr savings
$0
— plan
| Year 1 | Year 2 | Year 3 | Total | |
|---|---|---|---|---|
| Without Launchpad (100% frontier) | $0 | $0 | $0 | $0 |
| With Launchpad (all-in) | $0 | $0 | $0 | $0 |
| — of which: annual hardware (amortized) | $0 | $0 | $0 | $0 |
| Annual savings | $0 | $0 | $0 | $0 |
Annual spend: with vs. without Launchpad
Projected token spend by year
Without Launchpad With Launchpad
Savings per year
Annual savings, compounding with usage growth
Savings per year
Cumulative net savings & payback
Total dollars in the black over 36 months
Cumulative net savings (all developers) Payback crossover
The annual dips reflect your Launchpad subscription and, when applicable, the annual hardware amortization.
Upfront hardware costs are included, which may push payback out slightly.
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Assumptions & sources
Defaults are grounded in publicly available research. All inputs can be adjusted. Figures should be treated as illustrative; actual savings depend on workload mix, negotiated pricing, and deployment specifics.
- Baseline scenario: "Without Launchpad" assumes 100% of AI workloads are served by frontier model APIs (Anthropic, OpenAI, or equivalent). Your input reflects that current monthly bill.
- Growth default (30% YoY): conservative vs. Gartner's forecast of +47% worldwide AI spending growth for 2026. See Gartner: Worldwide AI Spending to Grow 47% in 2026, Stanford HAI 2026 AI Index — Economy, and Ramp AI Index (enterprise AI bills tripled in 12 months).
- Frontier % default (25%): production routing patterns show 65–80% of enterprise queries run fine on open/local models; only 20–35% require frontier reasoning. See Enterprise AI doesn't need a frontier model.
- Hardware pricing: Single baseline per GPU platform, before discounts and amortization. AMD — $262,000 baseline, based on a Supermicro 8U GPU SuperServer configuration. NVIDIA — $530,000 baseline, based on a large Dell server configuration. Both figures reflect list-price enterprise builds; realistic negotiated pricing typically comes in 10–20% below list, which the "Hardware discount" slider models directly.
- Launchpad for AI subscription pricing: Small plan (<25 developers) — $25,000 per server per year. Large plan (25+ developers) — $50,000 per server per year. Billed annually up-front; the calculator treats this as a Year-1 upfront cost with additional charges at the start of Years 2 and 3, which is why savings are never truly "immediate."
- Plan sizing thresholds: The Large plan is auto-recommended at 25+ developers, matching typical concurrent-usage headroom for a 4-GPU Small deployment. Larger fleets may benefit from multiple Launchpads — the alerts panel flags this above 40 developers.
- Cost-effectiveness of on-prem inference: Dell / ESG independent analysis found on-premises inference 2.9×–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).
- Per-developer benchmarks (context only): enterprise typical Claude Code / Copilot spend is ~$150–$250 / developer / month; power users hit $500–$2,000. See AI Coding Costs 2026 and Ramp: How Much AI Tokens Cost Businesses.
- Sunk-cost hardware path: when GPUs are already available in-house for this workload, hardware CapEx = $0. The calculator then measures token spend savings only.