ChatGPT vs On-Prem LLM TCO Calculator
Hosted APIs are cheaper at low volume, and owned GPUs are cheaper at high, steady volume. This calculator puts both on one yearly basis so you can see where the lines cross for your workload.
Your numbers
Weighted average of input and output list prices for your expected mix
Share of one engineer for patching, monitoring, model updates and support
Your results
On-prem cost is shown for one server and does not rise with users, so check capacity in the GPU sizing calculator. Excludes software, networking, redundancy, one-time integration work, model evaluation effort and data transfer. Hosted API prices change frequently.
Annual cost vs active users
Sweeps Active users from 25 to 3000, holding your other inputs fixed.
Pressure-test your TCO case
Send your inputs and we will review the assumptions, add what is missing and compare against a benchmarked open-weight setup.
Optional. The tool and every export work without this. No spam.
How to use it
Enter your expected user count, questions per day and total tokens per question including retrieved context. Use the blended API price that matches your input and output mix. For the on-prem side, enter a server price you have quoted, its power draw and the share of an engineer who will run it. The outputs show yearly cost for each path, and the break-even token volume at which they match.
The method
API cost is tokens per year divided by one million, times the blended price. On-prem cost is straight-line depreciation plus power with a facility overhead factor, a staff allocation and annual hardware support. The two paths scale differently: API cost grows with every token, while a server has a fixed cost until you hit its capacity, then needs a second one. That is why the chart shows a flat on-prem line against a rising API line.
- Include staff time, which is often the biggest on-prem line
- Include a spare or second node if the assistant becomes business critical
- Price the API on the model quality you actually need
Reading the result
With the defaults, the hosted API costs far less than the server at 400 users. The lines cross at much higher volume, which is why a pilot should almost always start on an API or a rented GPU. On-prem wins on cost mainly when usage is heavy and constant, such as agents running overnight batches. It can also win on non-cost grounds: data that may not leave your network, export control, latency inside the plant, or independence from provider pricing.
Common mistakes
Comparing the API bill with the server price alone leaves out staff, power and support, which flatters on-prem. The opposite error is ignoring agent loops, which can multiply tokens tenfold and move the break-even a long way. Do not compare a small open-weight model with a frontier API without testing quality. Re-run the numbers each year, as both token prices and GPU prices keep moving.
Frequently Asked Questions
When does on-prem become cheaper?
When yearly token volume exceeds the break-even shown and one server can handle the load. That generally means large user bases, heavy agent workloads or batch processing, rather than light chat usage by a few hundred people.
Is cost the only reason to go on-prem?
No. Data control, export-control rules, air-gapped sites, latency and vendor independence often decide it. Many companies pay more for private deployment because the data cannot be sent to a public service.
What about renting GPUs instead?
Renting converts capex to a monthly fee and suits pilots and uncertain demand. Replace depreciation with the rental cost for a quick comparison, and check that data residency and contract terms fit your needs.
Do open-weight models match ChatGPT quality?
For many ERP lookup, summarization and drafting tasks they are adequate, but results vary by task. Evaluate candidate models on your own questions before using cost as the deciding factor.
If you want the numbers checked against your own prompts and a quoted server, we can benchmark open-weight models on your workload and help you build the case.
Related Tools
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More On-Prem AI tools
See all- GPU Sizing Calculator for LLM Inference
- On-Prem vs Cloud AI Cost Comparison Calculator
- Self-Hosted LLM Hardware Estimator
- Air-Gapped AI Readiness Assessment
- LLM API vs Self-Hosted Cost Calculator
- RAG Infrastructure Sizing Calculator
- Open-Weight Model Selector
- On-Prem AI ROI Calculator
- On-Prem AI Deployment Checklist
Keep exploring
Free tools
- LLM API vs Self-Hosted Cost CalculatorModel your API bill from requests and token mix, compare it against an all-in self-hosted monthly cost, and see monthly and annual savings.
- SAP RISE vs On-Premise TCO CalculatorModel the multi-year total cost of RISE with SAP against an on-premise or self-hosted S/4HANA, using your own cost inputs.
- On-Prem Server Refresh vs Cloud TCO CalculatorPut hardware, support, space, admin time and hosting fees on one page to compare a server refresh with a cloud hosting move over your planning horizon.
ERP fixes and how-tos
- Self-Hosted Odoo AI Assistant: Private Architecture With a Local ModelAdd a private AI assistant to self-hosted Odoo: external API with user API keys, record rules respected, PostgreSQL replica, local model and audit trail.
- How to Add AI to Epicor Kinetic On-Premises Without Moving Your DataReference architecture for a private AI assistant on Epicor Kinetic on-premises: BAQ and REST v2 access, SQL replica, local LLM, roles, audit and rollout.
- How to Securely Give an LLM Access to ERP DataHow a CTO can give an LLM access to ERP data safely: read-only roles, per-user permissions, prompt injection defenses, logging, and where the model runs.
Guides
- ERP Total Cost of Ownership CalculationCalculate the true total cost of ERP ownership including hidden costs. Compare cloud vs on-premise TCO with a 10-year analysis framework and benchmarks.
- On-Prem LLM Deployment Architecture: The Complete Reference GuideReference architecture for on-prem LLM deployment: inference servers, GPU sizing, RAG pipelines, and security zones for regulated manufacturers.
- On-Prem AI for CUI in Small ManufacturersOn-prem AI for CUI: how small defense manufacturers run an LLM on their own GPUs inside the NIST 800-171 boundary, with hardware sizing and real trade-offs.
AI on your ERP
- SAP Joule or a Private LLM Beside SAP: An Honest ComparisonAn honest comparison of SAP Joule and Business AI against a private, self-hosted LLM beside SAP: what each covers, where they overlap, and where CIOs run both.
- SAP BTP and a Private LLM: Where the Generative AI Hub Fits and Where It Doesn'tWhere SAP BTP's Generative AI Hub fits and where a self-hosted LLM belongs instead. CAP, Integration Suite, and an architecture decision framework.
Integrations
- How to use ChatGPT with Infor LNHow to use ChatGPT with Infor LN: exporting data versus live access through ION API, custom GPT actions, enterprise data controls, and when a private model fits better.
- How to use ChatGPT with Infor M3How to use ChatGPT with Infor M3: exports, custom GPT actions over the M3 API or ION, enterprise data controls, and when a private model is the safer choice.
Troubleshooting
- Why your cost per token is too high, and the biggest levers to fix itHigh cost per token almost always traces back to low GPU utilization, the wrong model size, or no quantization or caching. Here is how to find the biggest lever.
- Why GPU utilization is low during LLM inference, and how to raise itLow GPU utilization during LLM inference usually means concurrency is too low, the client is synchronous, or tokenization/network is the real bottleneck. Fix order inside.