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Neither option is always cheaper. Cloud GPUs usually win for pilots, bursty or uncertain traffic because you can stop capacity and avoid buying equipment. An on-premises inference server can win when workloads run at predictably high utilization for several years and you already have suitable power, cooling, space, networking, security and operations staff. The only defensible comparison is fully loaded cost per delivered token (or request) at your required latency and quality.
GPU-hour prices are only an input. Your result changes with model and quantization, tokens per second, concurrency, uptime, region, data transfer, staffing, financing and hardware generation. Use the method below to calculate a low-, expected- and high-utilization case for your own workload.
What you are actually comparing
A cloud quotation normally starts with a GPU-hour rate. An owned deployment starts with a server and accelerator purchase price. Neither number is the cost your users experience. Inference cost is the total expense of producing useful output at an agreed latency and quality.
Define one common workload before collecting prices:
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- Model, parameter size, precision or quantization, and serving software.
- Input and output token mix, requests per second, concurrency, and target latency.
- Expected monthly tokens or requests, uptime, availability target, region and data-residency requirements.
- Whether a result counts only when a token is delivered within the latency target; throttled or failed requests should not be treated as productive capacity.
Then calculate:
Cost per million delivered tokens = total monthly inference cost ÷ (delivered monthly tokens ÷ 1,000,000).
For request-based products, use the equivalent cost-per-request calculation and keep the same latency and quality definition for every option. NVIDIA summarizes the principle as: “The most important metric for AI inference TCO is cost per token, or the price-performance actually delivered.”
What cloud GPU pricing includes—and what it leaves out
GPU and host charges
A rented accelerator is attached to a virtual machine or bare-metal host. Include the host CPU, memory, local or attached storage, operating-system and orchestration overhead, and any minimum instance size. A quoted accelerator rate that excludes the host cannot be compared with a complete owned server.
Storage, networking and data transfer
Model weights, container images, logs, caches and checkpoints consume persistent or ephemeral storage. Add inter-zone traffic, load balancers, private links and egress to your users or another provider. Egress can be negligible for a small internal service and material for a public, multi-region API; measure your actual response volume rather than assuming it is free.
Managed services, support and operations
Managed Kubernetes, model endpoints, observability, backups, security controls and premium support can be separate line items. Include the people who design, patch, monitor and tune the service, even if a provider removes some hardware work. A team that already operates the provider’s environment may have a lower incremental labor cost than a team starting from scratch.
Commitments and interruption risk
Google Cloud’s current pricing page lists NVIDIA Tesla T4 (16 GB GDDR6) at $0.35 per GPU-hour on demand, $0.22 with a one-year commitment and $0.16 with a three-year commitment. These are GPU rates, not complete inference prices. Commitments lower the unit rate but create payment obligations if demand falls or the model changes.
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- Multi-Precision Computing — Supports FP64 (7 TFLOPS), FP32 (14 TFLOPS), FP16 (112 TFLOPS) and INT8 precision modes for flexible deployment across training, inference, and scientific simulation workloads
Google Cloud also says Spot discounts can reach 60–91% for most machine types and GPUs. Spot capacity can be reclaimed, so the discount is appropriate only when your serving stack can queue, retry, fail over or tolerate interruption. A production service that requires uninterrupted capacity may need on-demand or committed capacity as a safety floor.
What an on-premises calculation must contain
Complete capital cost
Amortize the entire inference platform, not just the accelerator: server chassis, CPUs, memory, accelerator(s), local storage, network adapters, switches, racks and deployment work. If the server is financed, include interest and fees. If you assign a residual or resale value at refresh, state that assumption explicitly.
Energy and facilities
Measure the server’s actual power draw under your serving workload, then add power-distribution losses and cooling overhead. Account for rack space, leases, backup power, environmental monitoring and any electrical or cooling upgrade. Existing capacity can make these incremental costs small; a new room or service can make them dominant.
Maintenance, labor and downtime
Hardware support, spare parts, warranty extensions, firmware work, security patching, capacity planning and on-call coverage are recurring costs. Estimate the production impact of a failed accelerator, maintenance window or network outage. An owned GPU that is waiting for a replacement still incurs capital cost while producing no tokens.
Refresh and software lifecycle
Inference economics can change faster than a server’s accounting life. Budget for replacing the platform, migrating models and retesting performance. Driver, framework and compiler compatibility can require engineering time even when the hardware remains operational.
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Different accelerators can have radically different delivered economics. The following figures are published examples, not a universal price list; the workload, software stack and utilization behind each comparison must be checked before applying it.
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| Example | Published result | Qualification |
|---|---|---|
| NVIDIA Tesla T4 | 16 GB GDDR6; $0.35/GPU-hour on demand, $0.22 with a one-year commitment, $0.16 with a three-year commitment | Google Cloud current pricing page; GPU rate only |
| Hopper HGX H200 vs. Blackwell GB300 NVL72 | $4.20 vs. $0.12 per million tokens; GPU-hour figures shown as $1.41 vs. $2.65 | NVIDIA vendor analysis; token results depend on its stated workload and serving assumptions |
| NVIDIA B200 vs. H100 | Approximately $0.02 vs. $0.09 per million tokens | NVIDIA InferenceX benchmark, as reported on its April 2026 product page |
The apparent contradiction between a higher hourly price and a lower token price is the point: throughput, memory capacity, batching and latency determine how many useful tokens an hour produces. A cheaper, older accelerator can lose once it needs more cards, more host servers or more power to meet the same service target.
How to calculate your own break-even point
1. Measure delivered throughput
Run the exact model, quantization, prompt mix and serving configuration on each candidate. Record steady-state tokens per second at the target latency and concurrency. Include warm-up, batching limits, context length and error or retry behavior. A synthetic maximum that violates your user-facing latency is not usable throughput.
2. Convert throughput to monthly output
Multiply measured delivered tokens per second by the seconds your service is actually available, then apply the utilization profile. Create at least three cases: low demand, expected demand and high demand. Keep peak bursts separate from the average; a platform sized for peaks may be idle most of the month.
3. Build the cloud total
- Multiply the GPU and host rates by running hours.
- Add storage, snapshots, networking, load balancing, data transfer and managed-service fees.
- Add support and incremental engineering labor.
- Model on-demand, committed and (if technically safe) Spot capacity separately. Include a replacement capacity or failover cost for interruptions.
- Divide the monthly total by delivered monthly tokens.
4. Build the on-premises total
- Amortize server, accelerator, storage and networking capital over the expected service life, net of any stated residual value.
- Add financing, maintenance contracts, software support, power, cooling, space, connectivity and security controls.
- Add staffing, spares, monitoring and the expected cost of downtime or degraded service.
- Include a refresh reserve and migration testing before the platform reaches end of life.
- Divide the resulting monthly equivalent by delivered monthly tokens at the measured utilization.
5. Solve for utilization, not a magic percentage
For owned hardware, fixed monthly cost is spread across the productive GPU-hours or tokens it delivers. For cloud, idle GPUs can often be stopped, so the bill can fall with demand. Let F be monthly owned fixed cost, v owned variable cost per productive hour, c cloud cost per productive hour (including all cloud charges), and H the productive hours needed in a month. The simple hourly break-even is:
F ÷ H + v = c.
Because throughput may differ, use token output instead of hours when comparing unlike accelerators. The break-even utilization is the point at which the owned platform’s fully loaded cost per delivered token equals the cloud alternative. It cannot be stated honestly without your server quote, electricity rate, facility costs, labor, financing, uptime, region, model and token volume.
How region and provider choice change the cloud answer
Cloud prices are not globally uniform. A 2025 Cast AI benchmark reported that Google’s average on-demand eight-A100 instance was 28% cheaper than AWS’s and 18% cheaper than Azure’s in its comparison. The same report said region and availability-zone selection could change cost by as much as six times. Treat those figures as that report’s benchmark, not a permanent ranking.
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Obtain current quotations in the region where data may run. Confirm accelerator availability, quota lead time, inter-zone placement, egress destination and the exact host shape. A nominally cheap region that cannot supply capacity at your launch date can force a more expensive fallback.
When cloud is usually the better economic choice
- Pilots and changing models: You avoid a large purchase before throughput, memory and latency requirements stabilize.
- Bursty or seasonal traffic: Stop or scale down instances during quiet periods and add capacity for short peaks.
- Uncertain growth: Capacity can follow adoption instead of leaving depreciating GPUs idle.
- Multi-region delivery: Place inference near users without building facilities in every geography.
- Limited infrastructure staff: Provider hardware operations, replacement logistics and some security controls reduce your internal burden, although they do not eliminate application operations.
Committed cloud capacity deserves its own model when demand is stable but owning hardware is undesirable. Compare the commitment’s minimum spend with the utilization you can actually guarantee, and price the flexibility you give up.
When owning servers can be cheaper
- Predictably high utilization: A steady baseline spreads capital and facility costs over many productive hours.
- Existing facilities: Available rack power, cooling, networking, physical security and trained operators reduce incremental cost.
- Strict residency or disconnected operation: Keeping data and weights on-site may avoid cloud transfer, compliance work or connectivity risk.
- Latency to local systems: Local inference can remove a network round trip and its associated egress or private-link charges.
- Long, stable service life: A model and software stack that will remain useful for several years can justify amortization; rapid model changes weaken that case.
Ownership is not automatically economical at high utilization if an older accelerator delivers far fewer tokens per watt or per server. Benchmark the complete configuration at the required quality.
Why a hybrid design often wins
Separate a predictable baseline from exceptional demand. Run the baseline on owned servers sized for the utilization you can sustain, then burst overflow to cloud capacity. This can preserve residency for core traffic, absorb launches and outages, and avoid buying enough hardware for a few annual peaks.
Hybrid economics require more than two price columns. Include model replication, secure connectivity, observability across environments, request routing, cache consistency, data-transfer charges and the engineering cost of failover. Define which requests may leave the site and what happens when cloud Spot capacity disappears.
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Common comparison mistakes and fixes
Comparing a GPU-hour with a server invoice
Fix: Add the cloud host and services, and amortize the complete on-premises platform with facilities and labor.
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Using peak throughput as monthly production
Fix: Measure delivered tokens at target latency, then apply a realistic demand and uptime profile.
Ignoring idle time
Fix: Model stopped cloud capacity and the fixed cost of powered-on owned GPUs separately.
Assuming Spot discounts are guaranteed
Fix: Price interruption handling, fallback capacity and lost or delayed requests.
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Fix: Hold model version, quantization, context, output quality, concurrency and latency constant.
Taking a vendor token figure as a promise
Fix: Reproduce the workload or request the test conditions. The H200, GB300, B200 and H100 figures above illustrate how generation changes economics; they do not predict your application.
Leaving refresh risk out of ownership
Fix: Reserve capital and engineering time for replacement, migration and requalification before the current server becomes obsolete.
A practical decision sequence
- Write the workload contract: model, precision, token mix, latency, concurrency, uptime, region and monthly volume.
- Benchmark at least one cloud instance and one complete owned-server configuration under that contract.
- Collect current regional cloud quotes, including on-demand, commitment and any technically viable Spot option.
- Obtain a complete server bill of materials and facility estimates; do not price the accelerator alone.
- Calculate cost per delivered million tokens for low, expected and high utilization.
- Add non-price constraints: residency, outage tolerance, deployment lead time, staffing, security and hardware refresh risk.
- Choose cloud, ownership or a hybrid baseline-plus-burst design, and set a review trigger for utilization, model change or cloud-price change.
Evidence to treat carefully
Lenovo’s 2025/2026 total-cost study states that its baseline costs were as of December 23, 2025 and compares equivalent on-premises Lenovo servers with AWS and GCP/Azure instances. Such studies are useful for identifying cost categories and a defined configuration, but their result depends on the included server, utilization, energy, labor, financing and regional assumptions. Rebuild the model with your own inputs before making a purchase.
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Choose cloud when flexibility has economic value: uncertain demand, bursts, pilots, multiple regions or no operating team. Choose on-premises when a well-benchmarked workload will keep a complete server busy for years, local facilities already exist and residency or latency requirements favor local execution. If neither pattern is clear, a hybrid baseline with cloud bursting limits capital risk while you learn actual token demand. In every case, decide on fully loaded cost per delivered token at the required latency—not on the cheapest advertised GPU-hour.
Quick Recap
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