Self-Hosted AI Cost Calculator

The break-even point depends on your model, GPU efficiency, and ops overhead. For small models like Llama 3.1 8B or Mistral 7B, self-hosting on a cheap L4 or RTX 4090 becomes cost-competitive at roughly 500,000–2 million tokens/day. For large 70B models requiring multiple A100s, the break-even is typically 5–20 million tokens/day. High utilization (70%+) and efficient batching are critical to achieving these numbers.

The GPU hourly rate is only part of the real cost. Self-hosting also requires: monthly DevOps engineering time to manage deployments, monitor uptime, update models, and handle incidents (typically $500–$3,000/month); networking and storage for model weights, logs, and inter-service traffic; model serving framework setup and tuning (vLLM, TGI, Triton); and potential downtime costs if SLA expectations are high. Always add 30–60% on top of the raw GPU cost when comparing against API pricing.

GPU utilization is the percentage of available GPU compute that's actively processing inference requests during active hours. A GPU at 30% utilization means 70% of your paid GPU time is idle. At 30% utilization, your effective cost per token is 3× higher than at 90% utilization on the same hardware. Request batching and continuous batching frameworks like vLLM dramatically improve utilization and are essential for making self-hosting economically competitive.

Quantization reduces model weight precision from FP16 (16-bit) to INT4 or Q4 (4-bit), cutting VRAM requirements by ~70%. This allows running a 70B model on 2× A100s instead of needing 4×, or a 7B model on a single consumer GPU. The trade-off is a 10–20% throughput reduction and minor quality degradation on complex reasoning. For most production inference workloads, INT4 quantization is a good trade-off when hardware is the constraint.

For a simple single-model deployment on a managed GPU cloud, budget 5–20 hours/month of engineer time for monitoring, updates, and incident response. At a fully-loaded $80–$150/hr rate, that's $400–$3,000/month. For complex multi-model deployments with strict SLAs, the overhead can be 40–80 hours/month. Use a conservative estimate initially (10–20 hours/month) and track actual time over the first quarter to refine your projection.

Yes. The self-hosted AI cost calculator runs entirely in your browser using client-side JavaScript. Your workload volumes, infrastructure choices, cost inputs, and all results are processed locally on your device and are never sent to any server, stored, or shared. No login or account is required, and the tool is completely free with no usage limits.

For low-cost experimentation and small models: Vast.ai (RTX 4090 at ~$0.35/hr) and RunPod (L4 at ~$0.44/hr). For production inference with 7–13B models: RunPod or Lambda (A10G/A100 at $0.76–$1.64/hr). For 70B+ models: RunPod H100 ($3.49/hr) or Lambda 2×A100 ($2.58/hr). For enterprise with committed pricing: AWS, GCP, or Azure with reserved instances (typically 30–60% cheaper than on-demand). For maximum cost efficiency on stable workloads: on-premises hardware with 3–4 year amortization.