ML Platform Evaluation Criteria

Evaluating enterprise ML / AI platforms is a recurring exercise. The marketing pages converge; the differences emerge in specific operational dimensions. The criteria below cut through marketing.

Criteria

• Cost at projected volume: per-token + storage + compute, projected against your numbers
• Training capability: PEFT, full fine-tune, RLHF / DPO support
• Inference throughput: tokens/sec on your specific models
• Fine-tuning UX: dataset upload, training config, eval visibility
• Multi-tenant support: per-tenant fine-tunes, isolation
• Data residency: regions, single-tenant options
• Data integration: with Snowflake / Databricks / S3 / etc.
• Observability quality: built-in monitoring vs separate stack
• Model marketplace: which open / proprietary models available
• Custom serving: bring your own model / runtime
• Lock-in risk: how portable is your work?
• Ecosystem maturity: documentation, community, tooling

Scoring

Per-criteria score 1-5; weight by your priority; total. Most teams find scores diverge less than expected — the right answer is usually based on which criteria you weight highest, not on absolute platform quality.

Common patterns:

• AWS-aligned: SageMaker / Bedrock
• Azure-aligned: Foundry
• GCP-aligned: Vertex AI
• Multi-cloud / Spark-aligned: Databricks Mosaic
• Cost-anchored: self-hosted

Verdict

For ML platform evaluation, weighted scoring against 12-15 criteria gives a defensible decision. Most platforms converge on capability; differentiation is in cloud alignment, cost economics, and specific niches. Self-hosted dedicated GPU is increasingly the right answer when criteria weight cost + control + residency.