cost-and-provider-mapper

Map DSDS architectures across AWS, GCP, and Azure equivalents and estimate cost-impact tradeoffs. Use when planning provider portability, comparing cloud options, and explaining cost/performance impli

SKILL.md

GitHub

Cost And Provider Mapper

Overview

Translate component selections across cloud providers and reason about cost and performance implications. Produce comparable options instead of one-off provider-specific designs.

Workflow

  1. Build component inventory
  • List all architecture components with scale assumptions and usage patterns.
  • Mark critical latency and durability requirements.
  1. Map cross-provider equivalents
  • For each component, provide AWS, GCP, and Azure equivalent services.
  • Flag components with no clean one-to-one mapping.
  1. Identify primary cost drivers
  • Compute, storage, network egress, data transfer, and managed-service premiums.
  • Call out cost-sensitive workload patterns (bursty, diurnal, spike).
  1. Evaluate tradeoffs
  • Compare expected latency behavior, operational complexity, and portability risk.
  • Highlight lock-in points and migration friction.
  1. Produce recommendation pack
  • Deliver at least two viable provider profiles with pros/cons.
  • Include assumptions and confidence level.

Output format

  1. Component mapping table
  2. Cost driver summary
  3. Tradeoff analysis
  4. Recommended options
  5. Assumptions and risks

References

  • Read references/provider-mapping-notes.md for mapping and tradeoff checklist.
  • Use these repo sources:
  • canonical-catalogue/DSDS Canonical Catalogue - Provider mapping (quick).csv
  • canonical-catalogue/DSDS Canonical Catalogue - Metrics & SLIs.csv
  • planning/IMPLEMENTATION_PLAN.md (Phase 9.4)