Cloud GPUs vs. In-House Hardware
Buy or rent a GPU? This question torments everyone who is seriously involved in machine learning. Let's analyze the economics using specific figures and real company cases.
The Real Cost of Owning a GPU
Many people only see the price of a video card and think: "I'll buy it once and use it for years." But the reality is much more complicated and expensive.
Direct hardware costs:
- GPU: $8,000-30,000 per card (depending on model)
- Server: $8,000-20,000 (motherboard, CPU, RAM, storage)
- Networking: $3,000-8,000 (switches, cables)
- Cooling system: $5,000-15,000 (air conditioners, ventilation)
- UPS and electical: $2,000-5,000
Operating expenses (annually):
- Electricity: $300-800/month per card (depending on tariffs)
- Maintenance: 12-18% of the cost of equipment
- Replacement of failed components: $2,000-5,000
- DevOps/System Administrator Salary: $80,000-120,000
- Server Room Rent/Maintenance: $500-2,000/month
Total: A GPU server with 8 A100 cards will cost $350,000 - $450,000 initially, plus $60,000 - $100,000 in operating expenses annually.
ROI Analysis for Startups
Let's consider an AI startup developing a document analysis system:
Purchase scenario:
- Initial investment: $280,000
- Operating expenses: $70,000/year
- Payback period at 80% load: 24-30 months
- Risk of obsolescence in 3-4 years
Rental scenario:
- Initial investment: $0
- Variable expenses: $8,000-25,000/month depending on load
- Ability to scale without additional investment
- Always up-to-date hardware
Key insight: Startups rarely use 100% of GPUs. The actual load is 25-45% due to uneven development, testing and experimentation. This makes the purchase economically unprofitable in most cases.
Hidden factors
Time to launch: Purchasing and setting up your own infrastructure takes 2-4 months. Renting a GPU takes 5-15 minutes.
Expertise: Maintaining a GPU cluster requires Senior DevOps specialists ($100,000+ salary). In the cloud, this expertise is included in the price.
Scalability: Doubling your own capacity requires months of planning and major investments. In the cloud, it's a few clicks.
Technological risks: GPUs become obsolete quickly. NVIDIA releases new architectures every 2-3 years with exponential performance gains.
Hybrid model — the golden mean
Many successful companies choose a hybrid approach:
Base load (60-70%) — own hardware for predictable tasks
Peak loads (20-30%) — cloud GPUs for processing surges
R&D and experiments (10-20%) — rent for testing new approaches
Example: Spotify uses its own GPUs for core recommendation algorithms, but rents capacity for training new models and A/B testing music preferences.
Decision Matrix
Buy if:
- Sustainable high utilization (75%+ year-round)
- Long-term projects (4+ years)
- Specific latency requirements (<10ms)
- Strong compliance requirements
- Affordable capital ($500,000+) and IT team
Rent if:
- Unpredictable or seasonal load
- Limited start-up capital
- Fast-changing performance requirements
- Focus on product, not infrastructure
- Need for different GPU types for experimentation
2025 trends
Gartner research shows that by the end of 2025 85% of AI projects will use cloud GPUs as a primary or secondary platform.
New consumption models:
- GPU-as-a-Service with automatic scaling
- Serverless ML — pay only for the time the code is executed
- Spot markets — exchange trading of GPU capacities
Financial innovations:
- GPU leasing with the right to buy
- Pay-per-accuracy models for turnkey solutions
- Insuring against technological obsolescence
Conclusion
In a world of accelerating technological change, flexibility is often more important than short-term savings. GPU leasing provides this flexibility, allowing companies to focus on creating value rather than managing infrastructure.
The right choice depends on the specifics of the business, but the trend is clear: hybrid and cloud models of computing resource consumption are the future.