On-Prem vs Cloud AI Cost Comparison Calculator
This calculator compares the cost of buying LLM capacity as cloud API tokens versus owning GPU infrastructure, built for CFOs and IT leaders weighing the build-vs-buy decision. Cloud APIs scale linearly with usage forever; on-prem hardware is a step cost that gets cheaper per token as utilization rises. The tool computes your monthly cost under both models, the monthly savings (or premium) of going on-prem, and the month your hardware investment breaks even against avoided API spend - the single number most boards ask for first.
Your numbers
Total input plus output tokens per month across all AI workloads.
Blended input/output price of the API tier your workload would use.
GPUs needed to serve the same workload on your own hardware.
Per-card price; H100 ~$30K, A100 ~$17K, L40S ~$11K.
Period over which capex is spread; 3 years is typical for GPUs.
Power, cooling, support contracts, and staffing share for the cluster.
Your results
Estimates only. API prices change frequently and on-prem throughput varies by model and stack. Treat the breakeven point as directional, not contractual.
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How the comparison is modeled
Cloud cost is simply monthly token volume times a blended per-million-token price for your API tier - frontier models blend to roughly $10 per million tokens at typical input-output ratios, mid-tier models around $3, and small-model endpoints under $1. On-prem cost takes your GPU capex, adds 30% for chassis, networking, storage, and installation, spreads it across the amortization period, and adds monthly operating costs for power, support, and staffing. Breakeven divides total capex by the monthly API spend you avoid net of operating costs. At the defaults - 1.5 billion tokens per month on frontier-class models versus a 4x H100 server - on-prem saves roughly $4,700 per month and pays back in about 17 months.
Where the crossover typically lands
In Netray's engagements, the economics follow a consistent pattern by volume tier. The crossover moves earlier when data sovereignty forces you into premium cloud isolation tiers, and later when workloads are spiky enough that owned hardware sits idle.
- Under ~300M tokens/month: cloud APIs almost always cheaper; stay on APIs
- 300M-1B tokens/month: gray zone; decision driven by security and latency, not cost
- 1B-5B tokens/month: on-prem typically 30-60% cheaper over 3 years
- Over 5B tokens/month: on-prem wins decisively; API bills fund hardware in months
What the numbers do not capture
Cost parity is not decision parity. On-prem adds capabilities APIs cannot offer: air-gapped operation for ITAR and classified programs, guaranteed data residency, no per-token throttling, deterministic latency, and freedom to fine-tune on proprietary data without it leaving your network. It also adds obligations - hardware refresh cycles, an operations capability, and capacity planning discipline. If your monthly savings are modestly negative but you operate under CMMC, ITAR, or export-control constraints, on-prem often still wins on total business value.
How Netray helps you decide and execute
Netray runs build-vs-buy analyses for manufacturers weekly, and we are deliberately agnostic: roughly a third of our assessments conclude the client should stay on cloud APIs for now. When on-prem wins, we handle architecture, procurement support, deployment, and managed operations - including hybrid patterns where sensitive workloads run on-prem while overflow bursts to cloud. Our fixed-fee assessment turns this calculator's estimate into a validated business case with real quotes and a measured workload profile.
Frequently Asked Questions
What does 'blended' API price mean?
LLM APIs price input and output tokens differently - output typically costs 3-5x more. The blended rate weights those prices by a typical enterprise ratio of roughly 3-4 input tokens per output token. If your workload is output-heavy (long document generation) your true blended rate is higher than the preset; if it is input-heavy (classification over long documents) it is lower. Use the tier that best matches your actual invoice.
Should I include savings from switching to a smaller open-weight model?
Yes, and it often dominates the analysis. Teams moving on-prem rarely replicate a frontier API model; they deploy a 14B-70B open-weight model fine-tuned for their domain, which serves 80-95% of enterprise tasks at equal quality. That means the fair comparison is often frontier API pricing versus modest on-prem hardware - which is exactly why on-prem breakevens land earlier than most teams expect.
How should spiky or seasonal workloads change my decision?
Owned hardware is priced for peak but paid for continuously, so low average utilization erodes on-prem economics quickly. If your peak-to-average ratio exceeds about 3:1, consider a hybrid design: size on-prem for baseline load where the security requirements live, and burst non-sensitive overflow to cloud APIs. This keeps utilization high on owned GPUs while capping cloud spend to the spikes.
Find your breakeven month, then book a Netray assessment to validate it against real quotes and your measured workload.
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