Picking the wrong infrastructure or tooling early can waste months and drain budgets fast. The AI development landscape has exploded—from cloud platforms to open-source frameworks—making it harder to know what actually fits your use case. Here's how to evaluate your options systematically.
Start with Your Actual Requirements
Before comparing tools, lock down what you're building. Are you training large language models, running computer vision inference at scale, or managing MLOps pipelines? The infrastructure that works for fine-tuning BERT won't serve real-time object detection equally well.
Document your constraints:
- Model size and type: Transformer, CNN, reinforcement learning agent, or ensemble?
- Data volume: Gigabytes, terabytes, or streaming data?
- Latency tolerance: Real-time (milliseconds) or batch processing (hours)?
- Budget ceiling: Monthly spend cap for compute, storage, and licensing?
- Team expertise: Python-first, or do you need no-code interfaces?
These answers eliminate 60–70% of unsuitable options immediately.
Evaluate Compute Infrastructure
Cloud platforms dominate AI development, but they're not interchangeable. AWS SageMaker, Google Vertex AI, and Azure ML each excel in different scenarios—and pricing varies wildly.
Key comparison points:
- GPU/TPU availability: Check current pricing per hour for the chip you need (NVIDIA H100s run $2–4 per hour, A100s $0.50–1.50). Verify the provider actually has stock; shortages are common.
- Pre-built vs. custom: Managed services like SageMaker save setup time (weeks, sometimes) but cost 20–40% more than raw compute. DIY Kubernetes clusters on bare metal are cheaper but require DevOps skills.
- Spot/preemptible pricing: Using interruptible instances cuts costs 60–80% but introduces restart risk—only viable for fault-tolerant training jobs.
- Data transfer costs: Moving 10 TB between services or regions can cost $1,000+. Factor in egress early.
Most teams run 2–4 small experiments before committing to serious infrastructure. Start with a $500–1,000 monthly pilot to validate your choice.
Framework and Library Compatibility
Your model framework locks you into certain tooling ecosystems. PyTorch users will find less friction on Lambda Labs or Paperspace. TensorFlow-heavy teams should evaluate TPU availability (Google Cloud offers native support; AWS doesn't).
Check these frameworks for your use case:
- PyTorch: Fastest iteration, best for research, dominant in NLP
- TensorFlow: Strong production support, TPU integration, enterprise adoption
- JAX: Lightweight, fast, research-focused; smaller community
- ONNX Runtime: Cross-platform inference, good for deployment
Switching frameworks mid-project costs 4–8 weeks of rework. Choose based on your team's existing skill set and your final deployment environment.
MLOps and Orchestration
Once prototyping ends, you need orchestration. Platforms like Weights & Biases, Kubeflow, and Airflow handle experiment tracking, hyperparameter tuning, and pipeline management. Costs range from free (open-source Airflow) to $5,000+/month (managed enterprise platforms).
Essential features to verify:
- Experiment logging and reproducibility
- Automated hyperparameter search
- Model versioning and registry
- Integration with your chosen cloud provider
- Team collaboration (comments, shared dashboards)
Skipping MLOps saves upfront dollars but creates chaos at scale—most teams regret this after 50+ model iterations.
Total Cost of Ownership
Calculate beyond GPU hourly rates. Include:
- Storage: $0.02–0.05/GB/month for object storage
- Data egress: $0.02–0.10/GB (often overlooked)
- Managed services markup: 25–50% premium over raw compute
- Personnel time: ML engineers at $80–200/hour spent managing infrastructure
A "cheap" setup that requires 20 hours/month of DevOps work is often more expensive than a $1,000/month managed platform. Benchmark your team's hourly cost against automation value.
Getting Specific Comparisons
Directly testing multiple platforms costs time but reveals real differences. Run your smallest representative training job on 2–3 finalists. Measure end-to-end wall-clock time, final cost, and friction points. You'll eliminate theoretical advantages and see practical tradeoffs immediately.
Mercoly lets you compare and find trusted AI and machine learning development providers side-by-side, saving weeks of research and vendor calls.
Frequently Asked Questions
Q: Should I use managed services or build on Kubernetes myself? Managed services cost 30–40% more but eliminate DevOps overhead—choose managed if your team has no Kubernetes expertise and time-to-model matters. DIY Kubernetes makes sense for teams with 2+ dedicated infrastructure engineers.
Q: How do I avoid vendor lock-in? Use containerized models (Docker + ONNX), store code in version control, and avoid proprietary APIs. You'll pay a 5–10% performance penalty, but switching costs drop from months to weeks.
Q: What's a realistic monthly budget for a small ML team? $2,000–5,000/month covers compute, storage, and basic MLOps for a 3-person team training 10–20 models monthly. Add 50% if you need managed services instead of DIY infrastructure.
Compare infrastructure options today using real project requirements—not assumptions.