Quick Verdict: Spot vs Reserved vs Dedicated
Spot GPU instances cost 60-80% less than on-demand pricing but can be terminated with 30-120 seconds notice. Reserved instances lock in capacity for 1-3 years at 30-50% discount but require upfront commitment. Dedicated GPU servers from GigaGPU provide guaranteed bare-metal resources at monthly pricing without long-term lock-in or termination risk. For production AI inference that must stay online, dedicated servers are the only option that guarantees both availability and predictable cost.
Pricing Model Comparison
Spot instances use market-based pricing where unused cloud capacity is sold at steep discounts. Prices fluctuate based on demand, and your instance is reclaimed when capacity is needed. This unpredictability makes spot unsuitable for serving production AI endpoints.
Reserved instances guarantee capacity for a fixed term. You commit to 1 or 3 years of a specific GPU instance type in a specific region. The discount is significant (30-50%) but inflexibility is the trade-off. If your GPU requirements change, you are stuck with the original commitment.
Dedicated servers provide guaranteed hardware with monthly contracts. No termination risk, no long-term lock-in, and consistent bare-metal performance for private AI hosting.
Comparison Table
| Factor | Spot Instances | Reserved Instances | Dedicated Servers |
|---|---|---|---|
| Pricing | 60-80% off on-demand | 30-50% off on-demand | Fixed monthly rate |
| Availability Guarantee | None (can be terminated) | Guaranteed for term | Guaranteed monthly |
| Commitment Length | None | 1-3 years | Monthly |
| Performance Consistency | Variable (shared) | Variable (shared) | Guaranteed (bare metal) |
| GPU Selection Flexibility | Limited by availability | Locked at purchase | Choose and change |
| Root Access | OS-level only | OS-level only | Full bare-metal |
| Suitable for Production | No (interruption risk) | Yes (if term matches need) | Yes |
Workload Suitability
Spot instances work for fault-tolerant batch jobs: training runs with checkpointing, offline batch inference, data preprocessing, and experimentation. If your PyTorch training job can resume from a checkpoint, spot instances save substantially. Never use spot for real-time LLM inference.
Reserved instances suit organisations with predictable, unchanging GPU needs for 1-3 years. If you know you need 4x RTX 6000 Pros for the next two years, the reservation discount is worthwhile. However, GPU generations change rapidly, and a 3-year commitment to today’s hardware may not be optimal in 2027. Review GPU selection guides before committing.
Dedicated servers suit production inference, development environments, and any workload requiring consistent availability without long-term lock-in. Scale from one GPU to multi-GPU clusters as demand grows.
Cost Scenarios
For a 70B model inference service running 24/7, spot instances average 40% cheaper than dedicated but require fallback infrastructure to handle terminations, eliminating much of the savings. Reserved instances cost 15-25% more than dedicated over the same period while adding inflexibility. Dedicated servers win on risk-adjusted cost for always-on production workloads. See the benchmarks section for performance data.
Recommendation
Use spot for training and batch jobs. Use reserved only if your organisation requires cloud-specific features and can commit for 1-3 years. For production AI inference, GigaGPU dedicated servers deliver the best combination of availability, performance, and cost flexibility. Follow our self-hosting guide and vLLM deployment documentation. Explore the infrastructure blog for more hosting strategies.
