The honest total cost of ownership for a 10-person self-hosted LLM deployment in 2026 sits between $4,800 and $11,000 in year one, and $1,200 to $3,600 in year two onward. Those numbers include hardware, electricity, setup time, and ongoing maintenance at realistic hourly rates. They do not include the cost of getting it wrong the first time, which most teams do, and which typically adds 20 to 40 hours of engineering time that nobody budgets for in advance.
Pithy Cyborg | AI FAQs – The Details
Question: What does it actually cost to self-host an LLM for a 10-person team in 2026, and how does the total cost of ownership compare to just paying for API access or a team ChatGPT subscription?
Asked by: Claude Sonnet 4.6
Answered by: Mike D (MrComputerScience) from Pithy Cyborg.
The Real Hardware Cost for 10-Person Team LLM Serving
A 10-person team with moderate LLM usage, roughly 50 to 150 queries per person per day at average prompt lengths of 500 to 2,000 tokens, needs a serving setup that handles light concurrency without queuing becoming the dominant user experience.
The minimum viable hardware configuration for that workload is a single RTX 3090 or RTX 4090 running a Q4_K_M quantized 32B model, or a Q8_0 quantized 14B model, on a dedicated server or workstation. The RTX 3090 used route costs $550 to $650 for the GPU plus $400 to $700 for a compatible workstation chassis, CPU, RAM, and storage if you are building from scratch. Total hardware spend in the 3090 route: $950 to $1,350.
The RTX 4090 route runs $1,600 to $1,800 for the GPU alone plus the same chassis costs, landing at $2,000 to $2,500 total. The 4090 is worth the premium for teams where inference latency matters: it delivers roughly 2x the tokens per second of a 3090 on equivalent models, which is the difference between responses that feel instant and responses that feel like they are thinking.
For a team where multiple people query simultaneously with low latency tolerance, a dual-GPU setup with two RTX 3090s in a single workstation delivers 48GB of combined VRAM that can run a Q4_K_M 70B model fully GPU-resident. Budget $1,100 to $1,300 for the GPU pair plus $500 to $800 for chassis. Total: $1,600 to $2,100. This is the configuration most 10-person technical teams actually need once they measure real concurrency rather than estimating it optimistically.
The Hidden Costs Every Self-Hosting TCO Calculation Gets Wrong
Hardware is the number everyone calculates. Four other cost categories are consistently underestimated or omitted entirely from self-hosting comparisons that make the economics look more favorable than they are.
Electricity is the first omission. An RTX 4090 under inference load draws 350 to 400 watts. A full workstation running 24/7 draws 500 to 600 watts total. At the US average commercial electricity rate of roughly $0.12 per kWh, a continuously running 4090 workstation costs $525 to $630 per year in electricity. For a team that shuts the server down outside business hours, that drops to $175 to $210 annually. Neither number is catastrophic, but neither is zero, and both belong in the comparison against API costs.
Setup time is the second. A competent engineer who has never deployed a local LLM serving stack before spends 8 to 20 hours on initial setup: hardware assembly or configuration, OS setup, driver installation, serving framework deployment, network configuration, user access management, and frontend setup via Open WebUI or equivalent. At a fully-loaded engineering hourly rate of $75 to $150, that is $600 to $3,000 of labor cost that every self-hosting TCO comparison quietly omits. It is not ongoing cost, but it is real first-year cost.
Maintenance time is the third. Model updates, framework updates, driver updates, user access changes, and occasional debugging of inference issues consume roughly 2 to 4 hours per month on a stable deployment. At the same $75 to $150 hourly rate, that is $1,800 to $7,200 annually in ongoing engineering time. This is the number that most frequently flips the TCO comparison against self-hosting for teams without a dedicated technical person who absorbs these tasks as part of their existing role.
Downtime cost is the fourth. Self-hosted infrastructure goes down. Consumer GPUs are not rated for continuous production workloads and driver issues, hardware failures, and update-induced regressions happen at a rate that managed API services do not. If your team’s LLM access is genuinely productivity-critical, the cost of unplanned downtime belongs in the TCO. Most teams discover this after their first weekend outage rather than before their hardware purchase.
How Self-Hosting Actually Compares to API and Subscription Costs
The comparison baseline matters enormously and most self-hosting advocates cherry-pick it. Here are three honest comparisons at the 10-person team scale.
Against ChatGPT Team: OpenAI’s Team plan costs $30 per user per month, or $3,600 per year for 10 users. It includes GPT-4o access, a 32k context window, and no infrastructure management. A self-hosted RTX 3090 setup breaks even against ChatGPT Team in hardware costs alone in the first year. When setup and maintenance labor is included at realistic rates, self-hosting is more expensive in year one for most teams and roughly equivalent in year two onward only if maintenance is absorbed by someone already on payroll.
Against API usage: A 10-person team making 1,000 queries per day at average 1,500 input tokens and 500 output tokens spends approximately $45 to $90 per day on Claude Sonnet 4.6 or GPT-4o API depending on exact token mix. That is $16,000 to $33,000 annually. At that API spend level, self-hosting pays for itself in hardware costs within 30 to 90 days even including setup labor. This is the use case where self-hosting economics are genuinely compelling and it requires meaningfully higher query volume than most 10-person teams actually generate.
The honest answer: self-hosting wins clearly on privacy, on high-volume API cost replacement, and on workloads where data cannot leave the building. It wins narrowly or loses on total cost at moderate usage volumes once labor is included. Calculate your actual API spend for the last 90 days before assuming self-hosting is cheaper. Most teams that do this calculation are surprised by how low their real API costs are.
What This Means For You
- Calculate your actual API spend for the last 90 days before pricing any hardware, because most 10-person teams spend $200 to $600 monthly on LLM APIs at moderate usage, which means self-hosting payback periods are 18 to 36 months once labor is included, not 3 to 6 months as commonly claimed.
- Count setup and maintenance labor at your real hourly rate in every TCO comparison: 20 hours of initial setup plus 3 hours monthly of maintenance is $3,800 to $7,600 in year-one labor at standard engineering rates, and omitting it makes every self-hosting comparison dishonest.
- Rent before you buy using RunPod or Vast.ai at $0.30 to $0.60 per hour for an RTX 3090 or 4090, run your team’s actual query volume for two weeks, and use the real latency and concurrency data from that trial to size hardware accurately rather than estimating from benchmarks.
- Self-host for privacy first, cost second: the TCO case for self-hosting at 10-person team scale is real but narrow, and teams that deploy primarily for cost reasons frequently discover the economics are closer than expected, while teams that deploy for data privacy reasons get that guarantee regardless of how the cost math resolves.