Platform Evaluation Chain AI Prompt

Step 1: Requirements gathering — document the platform requirements: data volume (current and 3-year projection), workload types (batch ETL, streaming, ad-hoc SQL, ML), latency SLAs, team size and SQL vs code preference, compliance requirements (data residency, SOC2, HIPAA), and budget range.
Step 2: Candidate selection — identify 3 candidate platforms based on the requirements. Typical candidates: Snowflake vs Databricks vs BigQuery, or Airflow vs Prefect vs Dagster. Eliminate options that fail hard requirements immediately.
Step 3: Evaluation criteria scoring — score each candidate on: performance (benchmark on representative workloads), total cost of ownership (compute + storage + egress + seats), developer experience (ease of use for the team), ecosystem (integrations with existing tools), operational burden (managed vs self-hosted), and vendor risk.
Step 4: Proof of concept — run a 2-week PoC for the top 2 candidates. Use a representative subset of actual workloads. Measure: query performance, pipeline development speed, operational effort, and cost.
Step 5: TCO modeling — build a 3-year TCO model for each finalist: compute, storage, licensing, personnel, migration, and training costs. Include the cost of not choosing this platform (opportunity cost).
Step 6: Risk assessment — for each finalist: vendor lock-in risk, migration complexity, scaling limits, support quality, and financial stability of the vendor.
Step 7: Write the platform recommendation document: requirements summary, evaluation matrix, PoC results, TCO comparison, risk assessment, final recommendation with rationale, migration plan, and success metrics.