Set up AI infrastructure at three companies and most online advice is complete garbage. Everyone says "cloud is expensive, buy local hardware" without doing actual math or considering that your $40k H100 might sit in a box for 6 months because the data center buildout got delayed.
When Local Hardware Actually Makes Sense
Your break-even calculation depends on consistent utilization, not peak capacity. Together AI charges $3.50 per million tokens for Llama 3.1 405B. RunPod H100s cost $2.99/hour. Compare these rates with Lambda Labs pricing and Vast.ai marketplace to find the best deals. Do the math for your actual usage patterns, not theoretical maximums.
For a startup processing 1 million tokens daily, that's $105/month on Together AI versus like $300+ monthly just to amortize a local RTX 5090. Cloud wins until you hit enterprise scale or need 24/7 inference.
My first local setup was an RTX 4090 build for $2,200. Seemed brilliant until I actually calculated hourly costs: hardware alone was $0.38/hour assuming 8 hours daily use. Add power, cooling, and the 20+ hours I spent fighting CUDA driver compatibility issues, and I was paying more than cloud rates for a machine that crashed every time Windows updated. Check NVIDIA's driver support matrix before buying anything.
Enterprise Local Makes Sense (If You Have The Infrastructure)
We spent about $180k on H100s last year. The exact number doesn't matter when you're watching cloud bills hit $25k monthly for training workloads. Break-even was around 8 months, saving us $200k+ annually now. But it required:
- Data center space with 20kW power (good luck finding that)
- Redundant cooling ($40k just for installation)
- Network gear that actually works with InfiniBand
- DevOps engineer who knows hardware ($120k/year)
- Backup plans for when shit inevitably breaks
Hardware was only 60% of actual costs. Factor everything and our "cheap" local setup cost around $300k first year. Would do it again though - cloud bills were insane.
Cloud Hidden Costs Are Real Too
AWS SageMaker lists $3.36/hour per H100 - just for the GPU. Then they hit you with storage fees, data transfer costs, ML instance charges, and suddenly you're paying 40% more than advertised. Typical AWS bullshit.
Google Cloud H100s cost $11.27/hour for the 80GB version. Expensive as hell but at least includes networking and managed services. No surprise $2,100 or whatever insane power bills or debugging hardware failures at 3am.
Tried Azure ML for six months. Listed $8.32/hour per H100, actual bills were 60% higher due to storage, networking, and "premium support" that wasn't optional for enterprise accounts. Microsoft being Microsoft.
The Usage Pattern Reality Check
Most AI workloads aren't constant. Development is bursts of training followed by days of staring at loss curves. Inference is unpredictable - you get featured somewhere and suddenly need 10x capacity for a week, then back to normal.
Local hardware optimizes for peak capacity, cloud optimizes for average usage. Your RTX 5090 sitting idle 80% of the time costs the same as running full blast. Cloud bills scale with actual usage (when they're not charging you for stopped instances).
Monitored our usage for six months: training jobs maybe 30% uptime, inference peaked evenings and died on weekends. Cloud probably would've cost 40% less for the same workload, but good luck explaining variable bills to your CFO.
The 2025 Cloud vs Local Landscape
Cloud GPU availability got way better in 2025. RunPod has H100s available instantly at $2.99/hour. Together AI delivers inference fast enough for production. Paperspace and CoreWeave also improved availability significantly. No more waiting weeks for capacity allocations like the dark days of 2023's GPU shortage.
Local hardware is still a supply chain nightmare. H100s are 8-12 week delivery if NVIDIA even approves your order. RTX 5090s get scalped to $3,500+ when they're available. When hardware finally arrives, driver support for new models takes months and breaks existing setups.
Sweet spot shifted: cloud for development and traffic spikes, local for steady production workloads over 100 GPU-hours monthly. Hybrid works best - local for baseline, cloud when you need to scale fast.
What Actually Matters: Total Cost Per Token
Stop thinking hardware prices, start thinking cost per token. Local RTX 5090 at full utilization: maybe $0.50 per million tokens. Together AI Llama 3.1 70B: $0.88 per million tokens. OpenAI GPT-4.1: $2.50 per million tokens. Use cost calculators and TCO analysis tools to model your actual workloads.
But "full utilization" is fantasy. Real utilization averages 40-60% if you're lucky. Real cost per token doubles. Cloud looks expensive until you factor in idle hardware, power bills, cooling costs, maintenance headaches, and the opportunity cost of tying up $50k in depreciating hardware.
