I have been asked the same question by almost every engineering team I work with this year: should we run our LLM inference on-premise or in the cloud? The answer has changed dramatically since 2024. Hardware costs have shifted, new model architectures have emerged, and the operational overhead of managing GPU clusters has become more predictable. In this tutorial, I will walk you through the real costs — not just the cloud pricing page numbers — and help you calculate what makes sense for your specific workload.
The Hardware Landscape in 2026
On-premise LLM deployment in 2026 is dominated by NVIDIA H200 and B100 GPUs, with AMD MI350X gaining ground in price-sensitive markets. The key development is that 4-bit quantised models running on a single H200 can now match the quality of full-precision models from two years ago, making on-premise deployment viable for far more scenarios than previously possible.
| Cost Component | On-Premise (Single H200) | Cloud (AWS p5.48xlarge) | Cloud (Serverless API) |
|---|---|---|---|
| Upfront Cost | $28,000 – $35,000 | $0 (pay-as-you-go) | $0 |
| Monthly Operating | $1,200 – $2,000 | ~$8,500 (24/7 reserved) | $0.15-$0.75/M tokens |
| 12-Month Total | ~$47,000 – $59,000 | ~$102,000 | Varies by usage |
| Break-Even Point | N/A (startup cost) | ~4 months vs on-prem | ~25M tokens/month |
| Scalability | Hard cap (1 GPU) | Elastic (minutes) | Infinite (milliseconds) |
| Latency (P50) | ~150ms | ~180ms | ~250ms |
Step-by-Step: How to Calculate Your Break-Even
Step 1: Estimate Your Monthly Token Volume
I start by measuring the actual inference volume my application needs. A customer-facing chatbot handling 10,000 conversations per day with an average of 500 input and 200 output tokens each generates roughly 7 million input tokens and 2.8 million output tokens daily — or about 210 million input and 84 million output tokens per month.
To measure this, instrument your application with basic logging for one week. Record total input tokens, total output tokens, and peak concurrent requests. This single metric will drive most of your cost comparison.
Step 2: Calculate Cloud API Costs
Using DeepSeek V4 as an example at $0.28/M input and $1.10/M output tokens, our 210M input + 84M output monthly volume costs approximately $58.80 + $92.40 = $151.20/month for the API path. For the same volume with GPT-5 at $2.50/M input and $10.00/M output, the cost jumps to $525 + $840 = $1,365/month.
Step 3: Calculate On-Premise Costs
A single NVIDIA H200 GPU can handle roughly 100 concurrent queries at acceptable latency for a 7B-parameter quantised model. For a 70B model, that drops to about 10 concurrent queries. The hardware cost of $30,000 spread over three years is about $833/month, plus power, cooling, and maintenance at roughly $500/month — totalling about $1,333/month.
When Each Approach Makes Sense
- Choose on-premise when you have predictable, high-volume inference (over 1 million tokens/day) with a single model, strong data privacy requirements, and the engineering talent to manage GPU infrastructure
- Choose cloud GPU instances when you need flexibility across multiple model sizes, variable workloads with peak times, or want to avoid hardware procurement delays
- Choose serverless APIs when you are prototyping, have low or unpredictable volume, need access to frontier models you cannot self-host, or value zero operational overhead above all
Practical Deployment Tutorial
For a simple on-premise setup using Ollama and a quantised model on an H200:
# 1. Install Ollama
curl -fsSL https://ollama.com/install.sh | sh
# 2. Pull a quantised model
ollama pull deepseek-v4-pro:q4_K_M
# 3. Serve with custom parameters
ollama serve &
ollama run deepseek-v4-pro:q4_K_M --num-ctx 8192 --num-gpu 1
# 4. Test inference speed
curl -X POST http://localhost:11434/api/generate -d '{"model":"deepseek-v4-pro:q4_K_M","prompt":"Explain on-premise vs cloud LLM costs in 2026"}'
For cloud deployment with AWS SageMaker, the approach is different — you define an endpoint configuration and let AWS handle the scaling. The important thing is to measure, not guess. The worst decision you can make is choosing a deployment strategy based on pricing page numbers alone without accounting for your actual usage patterns. For more on choosing the right model for deployment, see our AI models comparison guide and our AI agent platforms comparison.