Your AI platform works fine. The invoice is the problem.
If your company spends $5,000+ a month on AI seats and usage, this page will show you, in dollars, what the same work costs on open models (Kimi K3, GLM-5.2 class) hosted on your own dedicated GPUs. Same capabilities. Same workflows. A much smaller number.
Paste in three numbers. Get your verdict.
Defaults reflect a typical 25-seat team on a frontier AI platform. Adjust to match your bill. Everything updates live.
Your current setup
Get the full audit as a one-page PDF: model-by-model breakdown, migration plan, and the GPU sizing we'd recommend. We send it, you keep it.
Three spending profiles. One of them is you.
Inspired by Ramp's token-spend benchmark: same work, wildly different bills. The difference is almost never usage. It's model choice and where the model runs.
Pays frontier prices for everything
Every task, from a support macro to a board deck, runs on the most expensive model, on a meter that never sleeps.
- Model mix: frontier for ~85%+ of tokens
- Infra: shared, metered API seats
- Bill shape: grows every month, no one knows why
- Typical savings on Duet: 55–75%
Some discipline, real leakage
Cheaper models for easy tasks, but seat licenses + overages + premium defaults quietly eat the difference.
- Model mix: ~50/50 frontier vs mid-tier
- Infra: metered seats + usage overages
- Bill shape: predictable-ish, with spikes
- Typical savings on Duet: 40–60%
Already cost-aware
Routes work to the cheapest capable model. The remaining lever is structural: own the infrastructure instead of renting it.
- Model mix: frontier only where it earns its keep
- Infra: mixed, partially self-hosted
- Bill shape: flat, scrutinized
- Typical savings on Duet: 20–40%
How we model your number
No black box. The audit uses public list prices and a conservative utilization assumption. If anything, it understates the gap.
| Workload class | Frontier list price | Open model on Duet |
|---|---|---|
| Drafting, research, reporting, support | $3–15 / 1M tok (frontier models) | ≈$0.75 / 1M tok effective* |
| Hardest 10–15% (keep on frontier) | $10–50 / 1M tok (highest-cost reasoning models) | unchanged, routed via Duet |
| Seat license, per person | $20–100 / mo | $0, included |
| Dedicated GPU node (sized to team) | Not applicable | flat, from ~$1.5k/mo |
*Blended effective rate for Kimi K3 / GLM-5.2-class models amortized over a dedicated node at ~45% utilization, including the Duet platform. We keep 15% of your workload at frontier prices on purpose. Some tasks genuinely need the biggest model, and Duet routes those through too.
Why this isn't apples-to-oranges. Open models crossed the "good enough for business work" line for drafting, research, enrichment, support triage, and reporting, the exact workloads that dominate non-coding AI bills. Frontier models still win on the hardest reasoning, and the audit keeps that 10–15% of your spend in place.
What changes is who owns the meter. Today you rent tokens by the sip and seats by the head. On Duet, open models run on dedicated GPUs sized to your team. The meter is yours, the data stays on your infrastructure, and the AI product (agents, memory, channels, apps) comes fully wired in.
The result: same capabilities your team already relies on, at a flat infrastructure cost instead of a growing per-seat, per-token bill.
Before you run the numbers
Is the output quality actually the same?
What does "dedicated GPUs" actually get me?
How is this different from just switching to a cheaper plan?
What does migration actually look like?
Who is this not for?
Run the audit. Then make us prove it.
Bring last month's invoice to a 20-minute working session. We'll rebuild your bill line by line on dedicated open-model infrastructure. If the gap isn't real, you'll know in one call.