LLM Hosting Cost Calculator

An LLM hosting cost calculator estimates the infrastructure expenses of running a large language model yourself, rather than calling a managed API like OpenAI or Anthropic. Self-hosted LLMs require GPU compute — either rented from a cloud provider or owned on-premises — and the economics differ significantly from pay-per-token managed services. Our LLM hosting cost calculator covers three deployment categories: cloud GPU rental (pay per GPU-hour on RunPod, Vast.ai, Lambda Labs, AWS), managed serverless inference (pay per token on Together AI, Groq, Fireworks, DeepInfra), and on-premises hardware (amortized purchase cost plus colocation fees). All calculations run locally in your browser — no data is sent to any server.

1. Select Your Model and Quantization: Choose from popular open-weight models (Llama 3.1, Mistral, Qwen, Gemma, DeepSeek, Phi) at FP16/BF16, INT8, or INT4 precision. The calculator automatically computes VRAM requirements and checks compatibility with each deployment option.
2. Choose a Deployment Option: Pick from cloud GPU providers (RunPod, Vast.ai, Lambda Labs, AWS), managed serverless services (Together AI, Groq, Fireworks AI, DeepInfra), or on-premises hardware configurations. Each option shows its per-hour GPU rate or per-million-token pricing.
3. Set Your Usage Parameters: Enter active hours per day, GPU utilization, requests per day, and average input/output token counts per request. For on-prem options, specify your hardware cost, monthly power and colocation fees, and amortization period.
4. Read Your Cost Breakdown: Get monthly cost, annual cost, cost per request, cost per 1K tokens, and a side-by-side comparison of all deployment options sorted by price — so you can instantly identify the most cost-effective choice for your model and volume.

• GPU-Hours vs. Token-Based Billing: Cloud GPU rentals charge per GPU-hour regardless of actual utilization — a GPU sitting idle at 20% utilization costs the same as one at 80%. Managed inference services charge per token consumed, so you only pay for actual usage. Low-volume workloads typically favour managed inference; high-volume, consistent workloads favour dedicated GPU rental.
• VRAM Determines Your GPU Tier: Model VRAM requirements depend on parameter count and quantization precision. Llama 3.1 70B at FP16 needs ~140 GB VRAM (2× A100 80GB). At INT4 it fits in ~40 GB (a single A100 40GB). Quantization can cut hardware costs by 50–75% with minimal quality loss for most inference tasks.
• Throughput and GPU Utilization: The cost per request depends heavily on GPU utilization — the more requests you batch and the higher your utilization rate, the lower your effective cost per token. Inference servers like vLLM and TGI use continuous batching to maximize throughput on a given GPU.
• On-Prem Break-Even Analysis: On-premises hardware becomes cost-effective when amortized monthly cost (hardware ÷ years × 12 + power + colocation) drops below cloud rental costs for the same GPU capacity. For H100 SXM, on-prem typically breaks even versus cloud at 18–30 months of continuous use.

For development and experimentation, Vast.ai marketplace GPUs (RTX 4090 at ~$0.37/hr) offer the best cost-per-VRAM for 7–13B models. For production inference at moderate scale (under 1M tokens/day), managed services like Groq or Together AI eliminate infrastructure overhead and often beat cloud GPU rental costs. For high-volume production workloads (10M+ tokens/day), dedicated cloud GPU rental (RunPod, Lambda Labs) or on-premises hardware provides better economics, full control, and data privacy. Use this LLM hosting cost calculator to find your cross-over point by adjusting the requests per day until the managed and self-hosted options converge in the comparison table. All inputs are processed 100% locally — your infrastructure and usage data never leaves your device.