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Estimate self-hosting cost by fixing a time horizon and workload, then adding amortized hardware, operating expenses, reliability capacity, and a contingency reserve. The core calculation is TCO = amortized CapEx + period OpEx + contingency. Divide that total by the tokens you expect to serve to get cost per million tokens. A GPU price alone is not a useful answer: utilization, context length, staffing, electricity, cooling, redundancy, and demand growth can change the result more than the accelerator’s sticker price. This method lets you compare owned servers with rented GPUs or an API on equal model quality, token mix, latency, uptime, and regional assumptions.
What does the total cost of self-hosting an AI model include?
Total cost of ownership (TCO) covers every expense required to serve inference reliably during a defined period. Separate one-time capital expenditure from recurring operating expenditure so idle capacity and operational overhead remain visible.
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| Cost group | Include | Why it matters |
|---|---|---|
| Capital expenditure | GPUs, CPU host, RAM, local NVMe, networking, chassis or rack, power supplies, installation and facility fit-out | Large upfront cost is converted into a monthly amount through amortization. |
| Power and cooling | Measured or vendor power draw, powered-on hours, electricity tariff, cooling or colocation charges, and facility overhead | High-power accelerators consume money even when traffic is low. |
| Operations | Storage, bandwidth, load balancing, monitoring, backups, security, software support, maintenance, insurance and engineering or SRE labor | A server that is paid for but not operated safely is not a production service. |
| Reliability capacity | Spare GPUs, redundant hosts, failover replicas, replacement parts and capacity reserved for upgrades | Meeting an uptime or latency commitment usually requires more hardware than the average load. |
| Contingency | A reserve for price changes, failures, demand surprises, migration work and downtime | Prevents an optimistic base case from becoming the budget. |
Meta’s 2026 deployment guidance describes a cost-projection methodology covering hosted APIs, cloud deployments and on-premises deployments. It focuses on inference deployment, operation and maintenance; add training, fine-tuning, data preparation and evaluation as separate budgets when they are part of the project.
How should I define the workload before pricing hardware?
Write down the workload first. Hardware and hosting prices are meaningless until you know how many tokens must be processed, how quickly, and with what reliability.
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Set the time horizon
Choose a period such as 36 or 60 months. A short horizon makes an owned server look expensive because the purchase is spread over fewer months; a long horizon increases exposure to failures, refreshes and obsolete hardware. State whether residual value at the end is zero or whether you expect to resell equipment.
Record demand and token mix
- Requests per day and expected growth by month or quarter.
- Input tokens and output tokens per request.
- Context-length distribution, including the percentage of long prompts.
- Peak requests per second and concurrent requests, not just daily averages.
- Target latency, throughput and uptime.
- Geography, data-residency requirements and service hours.
Estimate input, output and cached-token ratios from a sample of real requests. Meta’s 2026 guidance identifies 4:1 and 3:1 input-to-output ratios as common planning rules, but your own traffic should replace those rules as soon as it is available. Reasoning tokens can add computation even when they are hidden from the user, so include them in output or internal-token measurements where the model exposes them.
Convert traffic into served tokens
For each period, calculate:
served tokens = requests × (input tokens + output tokens + billable reasoning tokens)Track cached tokens separately. Prompt or key-value (KV) caching can improve cost effectiveness by orders of magnitude for highly similar prompts, but only if your workload actually reuses prefixes and the runtime preserves those entries. Do not assume a cache benefit that has not appeared in your request logs.
What GPU and server capacity does the model require?
Model weights are the first memory gate, but a production deployment also needs KV cache, framework overhead and a safety margin. Size for the highest context and concurrency you intend to support, not the smallest single request.
Estimate weight memory
Lenovo’s 2026 TCO paper gives an approximate rule of 2 bytes per parameter for FP16 or BF16 weights. On that basis, the weight-only estimates are:
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|---|---|---|
| 7B | About 14 GB | KV cache, runtime, operating system and safety margin |
| 13B | About 26 GB | KV cache, runtime, operating system and safety margin |
| 70B | About 140 GB | KV cache, runtime, operating system and safety margin |
A 70B model therefore cannot be planned as a 140 GB total system. KV-cache memory grows with context length and batch size, and the inference framework consumes additional memory. Leave headroom for fragmentation, temporary buffers and traffic bursts.
Evaluate quantization rather than assuming it
Eight-bit and four-bit quantization reduce weight memory compared with FP16 or BF16 and may allow a model to fit on fewer or smaller GPUs. The trade-off is model quality, speed, kernel support and sometimes a different memory pattern for the KV cache. Benchmark the exact quantized checkpoint, context lengths and concurrency you will operate; a theoretical fit is not evidence of acceptable tokens per second or answer quality.
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Build a sizing worksheet
- Model parameters and precision or quantization.
- Weight memory.
- KV-cache allowance at the maximum context and batch.
- Framework and operating-system overhead.
- Target tokens per second and maximum concurrent requests.
- Number of replicas for high availability and maintenance.
- GPU interconnect, host RAM, CPU, local NVMe and network bandwidth.
The resulting GPU count drives the rest of the design: chassis dimensions, power supplies, rack space, cooling, network topology and installation work. If one GPU cannot meet the memory or throughput target, price the complete multi-GPU server rather than multiplying an isolated accelerator price.
How do I calculate amortized capital expenditure?
List every purchase needed to put the service into production, subtract the residual value you genuinely expect, and spread the remainder across its useful life.
amortized CapEx per period = (purchase cost − residual value) ÷ useful lifePurchase cost includes GPUs, the CPU host, RAM, local NVMe, network adapters and switches, chassis or rack, power supplies, installation and any electrical or facility fit-out. Use the same currency and tax treatment for every line. If procurement, shipping or import costs are material, put them in the installation line instead of silently excluding them.
Include reliability and refresh capacity
A single replica may be adequate for an internal experiment but not for a customer-facing service. Add spare GPUs, a redundant host or a failover replica when the service has an uptime or latency commitment. Reserve capacity for firmware updates, model refreshes and migrations; otherwise maintenance will force an outage or require emergency rental.
Choose a residual-value assumption
Use zero residual value when resale is uncertain. If you assume resale, document the date, percentage and market on which that value depends. A high residual value lowers calculated TCO but can make the result fragile if accelerator demand or interface standards change.
Which operating expenses are easy to miss?
Operating expenditure continues after the server is paid for. Model it monthly or hourly, then multiply by the powered-on schedule and expected traffic.
Electricity and cooling
Use measured consumption when possible; otherwise use the vendor’s power figure and label it as an estimate. Multiply watts by powered-on hours and the local tariff. Apply a facility overhead factor where relevant, and add cooling or colocation fees rather than treating them as free. A lightly utilized server still draws substantial idle power.
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Connectivity and storage
Budget internet or private-network egress, load balancing, object storage, local scratch space, backups and log retention. Long prompts and generated outputs increase bandwidth; model files and checkpoints increase storage. Keep backup retention and cross-region replication as explicit line items.
Software, monitoring and security
Include inference runtime support, observability, alerting, vulnerability scanning, secrets management, access control, patching and incident response. Open-source software can have no license fee while still requiring engineering time and paid support.
People, maintenance and insurance
Estimate engineering or SRE hours for deployment, upgrades, on-call, capacity planning and troubleshooting. Add replacement parts, warranty extensions, insurance and disposal. If a team member spends only part of their time on the service, allocate that fraction consistently across alternatives.
How do utilization, caching and batching change cost per token?
Owned hardware has a fixed cost whether it is serving one request or many. Utilization therefore determines the idle-capacity penalty.
Report both fixed and variable cost:
fixed cost = amortized CapEx + reserved operations + baseline staffing + reliability capacityvariable cost = incremental electricity + bandwidth + storage + usage-based supportcost per million tokens = TCO for the period ÷ (tokens served during the period ÷ 1,000,000)
Batching can raise throughput and spread fixed costs across more tokens, but excessive batching increases latency. Quantization may lower memory and permit more replicas, while prompt and KV caching can reduce repeated computation. Measure the combination under your latency target. A high theoretical throughput that cannot meet peak latency is not usable capacity.
What is the complete TCO formula?
Use one formula for every deployment option and show low, base and high cases.
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TCO(period) = amortized CapEx + period OpEx + contingencyFor each case, vary utilization, electricity price, hardware life and demand growth. Keep the model, token mix, latency target, uptime and geographic assumptions constant when comparing ownership with rental or an API. Present total dollars, monthly dollars and cost per million tokens so decision-makers can see both cash timing and unit economics.
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Can you show an illustrative calculation?
The following is an arithmetic example, not a market quote. Suppose a team chooses a 36-month horizon and expects 1 billion served tokens per month. It buys a complete server package for $60,000, assumes no residual value, and budgets $2,000 per month for electricity, cooling, connectivity, storage, monitoring, maintenance and staffing. It adds a 10% contingency to the calculated subtotal.
- Amortized CapEx is
$60,000 ÷ 36 = $1,666.67per month. - Base monthly cost before contingency is
$1,666.67 + $2,000 = $3,666.67. - Monthly contingency is
$3,666.67 × 10% = $366.67. - Monthly TCO is approximately
$4,033.34, or about$145,200over 36 months. - At 1 billion tokens per month, cost per million tokens is approximately
$4.03.
If demand reaches only 100 million tokens per month, the same monthly TCO becomes about $40.33 per million tokens. If demand grows to 10 billion tokens per month without requiring another server, the unit cost falls to about $0.40 per million tokens. Those changes come from utilization, not from a different GPU.
How should I compare owned hardware, rented GPUs and an API?
Use the same model quality, token mix, latency, uptime, region and data-handling assumptions. A cheap rental that uses a smaller model or lower service level is not an apples-to-apples comparison.
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Use rental prices as comparison inputs
Hugging Face’s pricing table, accessed October 26, 2026, lists approximate hourly rates for the following AWS configurations. Prices are volatile and represent comparison inputs rather than a guarantee of the complete cloud bill.
| GPU | Listed hourly rate | Approximate 730-hour month before other charges |
|---|---|---|
| T4 | $0.50/hour | $365/month |
| A100 | $2.50/hour | $1,825/month |
| H100 | $4.50/hour | $3,285/month |
| H200 | $5.00/hour | $3,650/month |
| B200 | $9.25/hour | $6,752.50/month |
The monthly figures are simple multiplication of the listed rates by 730 hours. They exclude storage, networking, orchestration, support, taxes, idle reservations and any additional GPUs needed for memory or redundancy. A rental calculation should also include the hours required for failover and upgrades.
Interpret scenario break-even results carefully
An OECD 2026 analysis found no evident self-hosting benefit below 100 million tokens per month under its assumptions. It reported break-even at about 2.5 years (roughly 30 months) for 1 billion tokens per month, about 2 months for 10 billion tokens per month and about 1 month for 50 billion tokens per month. These are scenario results, not universal thresholds: changing utilization, hardware prices, electricity, staffing, model size, downtime or residual value changes the answer.
| Comparison axis | Owned capacity | Rented capacity or API |
|---|---|---|
| Cost shape | High upfront CapEx, lower marginal cost when busy | Mostly variable or recurring spend, little or no CapEx |
| Scaling | Limited by purchased hardware and installation time | Usually faster to add or remove capacity, subject to availability and quotas |
| Idle capacity | Paid for continuously | Can be released, but reservations may still incur charges |
| Operations | You own patching, failures, cooling and security | Provider operates infrastructure, while you still manage integration and usage |
| Data residency | Physical location is under your control | Depends on provider regions and contract terms |
| Hardware refresh | You fund migration and replacement | Provider exposes newer hardware as available |
What step-by-step process should I use in a spreadsheet?
A repeatable worksheet prevents hidden assumptions from deciding the result.
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- Choose the horizon. Enter 36 or 60 months, useful life, residual value and the analysis currency.
- Describe demand. Enter requests, input tokens, output or reasoning tokens, cache ratio, peak concurrency and monthly growth.
- Set service targets. Record context length, tokens-per-second target, latency percentile, uptime and service hours.
- Size the deployment. Calculate weight memory, KV-cache allowance and framework overhead; select GPU count, replicas, host RAM, storage and networking.
- Enter CapEx. Add accelerators, hosts, racks, power supplies, installation and facility work; subtract only a documented residual value.
- Enter OpEx. Add power, cooling, colocation, bandwidth, storage, software, monitoring, backups, security, maintenance, insurance and labor.
- Add reliability capacity. Price spare GPUs, redundant hosts, failover replicas and upgrade headroom.
- Calculate three cases. Use low, base and high assumptions for utilization, electricity, hardware life and growth.
- Compute outputs. Show monthly TCO, period TCO, fixed cost, variable cost, tokens served and cost per million tokens.
- Repeat for alternatives. Replace owned CapEx and OpEx with rental or API charges while holding quality, token mix, latency, uptime and region constant.
Which sensitivities should change the decision?
Run a sensitivity table instead of relying on one forecast.
- Utilization: test idle, expected and peak demand; this usually has more influence than a small GPU-price difference.
- Electricity: apply low, contracted and high tariffs, including facility overhead.
- Hardware life: compare early replacement with the planned useful life.
- Demand growth: test flat demand, expected growth and a surge that requires another replica.
- Downtime: price lost service, emergency rental and recovery labor where an outage has business impact.
- Model refresh: include the cost of re-slicing, re-quantizing, testing and temporarily running old and new versions together.
Plot cost per million tokens against utilization. The curve makes the idle-capacity penalty visible and shows the traffic level at which owned capacity begins to compete with rental or API spending.
What mistakes make a self-hosting estimate unreliable?
- Pricing only the GPU: a GPU still needs a host, memory, storage, power delivery, cooling, networking and installation.
- Using weight memory as total memory: KV cache and runtime overhead can exceed the remaining VRAM, especially with long contexts or high concurrency.
- Using average traffic for capacity: peak concurrency and latency targets determine replicas and queueing.
- Ignoring idle time: owned servers incur depreciation and power costs when they are not serving tokens.
- Comparing unlike services: a smaller model, different token ratio, lower uptime or another region invalidates the comparison.
- Forgetting people: deployment, patching, monitoring, on-call and incident response are real costs even with free software.
- Assuming quantization is free: quality, throughput and kernel support must be tested for the chosen checkpoint.
- Mixing inference and training budgets: training, fine-tuning, data preparation and evaluation require separate accounting unless explicitly included.
- Hiding uncertainty: show low, base and high cases and identify which assumptions drive the spread.
When does self-hosting usually make financial sense?
Self-hosting is most defensible when demand is high and predictable enough to keep owned capacity busy, data-residency or control requirements are important, and the team can operate the service. At low or highly variable volume, rental or an API often avoids paying for idle hardware and specialist operations. The decision should follow your calculated cost per million tokens and service requirements, not a generic GPU price or a single break-even claim.
Frequently Asked Questions
Should taxes and financing be included in a self-hosting TCO model?
Yes. Decide whether the comparison is pre-tax or after-tax and apply that treatment consistently to hardware, electricity and cloud bills. If the purchase is financed, show interest and payment timing separately from the underlying equipment cost so financing does not disappear inside depreciation.
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Reconcile the model with actual tokens, power, utilization, incidents and staffing at least monthly during the first quarter, then quarterly. Trigger an immediate refresh after a model change, a tariff change, a new hardware purchase or a sustained demand shift.
What evidence is needed to replace planning assumptions with measured values?
Export request logs for token counts, cache hits, context lengths, concurrency and latency, and collect server power readings over representative busy and idle periods. Keep the measurement window and model version with each value so a later comparison does not mix unlike workloads.
How should I budget a sudden traffic spike?
Keep a burst plan outside the steady-state TCO: pre-approved rented capacity, queue limits, graceful degradation and a trigger for adding hardware. Price that reserve as an availability option rather than assuming the owned cluster can absorb unlimited peaks.
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