Weighted Decision Matrix

Options scored against weighted criteria — useful for making the reasoning inspectable, dangerous when the arithmetic is mistaken for the decision.

Technology Cloud-Agnostic

Options across the top, criteria down the side, weights agreed before anyone scores anything. The output is not the winning total; it is a visible record of what mattered and how each option was judged, which is what a reviewer can argue with.

The shape

Criterion Weight A: Managed Kafka B: Self-managed Kafka C: Cloud pub/sub
Ordering guarantees needed 25% 5 — per-partition 5 — per-partition 2 — no global order
Replay / retention (30 days) 20% 5 5 2 — 7 days max
Operational burden 20% 4 — vendor runs it 1 — needs 2 FTE 5 — fully managed
Existing team skill 10% 3 3 2
3-year TCO 15% 2 — ₹3.4 cr 3 — ₹2.6 cr + staff 4 — ₹1.9 cr
Exit / portability 10% 4 — open protocol 5 1 — proprietary API
Weighted total 4.05 3.65 2.75

Decision: Option A. Why the number is not the reason: replay for 30 days is a hard requirement from the reconciliation process, and option C fails it outright at 2 — it should have been screened out before scoring rather than allowed to accumulate points elsewhere. Between A and B the totals are close enough to be noise; the deciding factor is that two dedicated operations staff do not exist and are not being funded.

When you produce it

For a contested decision with three or more genuine options and stakeholders who disagree about what matters. Not for a decision that is already obvious — the matrix will be reverse-engineered to justify it and everyone will know.

Who reads it

The review board, who should challenge the weights rather than the scores. Finance and procurement, in a vendor selection. The next architect, who needs to know what was actually being optimised for.

What good looks like

  • Weights agreed and published before scoring. This is the entire integrity of the method.
  • Hard requirements are screened first as pass or fail, not scored — otherwise an option that cannot meet a mandatory need wins on points elsewhere.
  • Each score carries a one-line justification, as above.
  • The narrative states the real reason, and says so where it differs from the arithmetic.
  • Close totals are treated as ties, not as results.

Common mistakes

  • Tuning the weights until the preferred option wins. Visible to everyone, and it destroys trust in every later matrix.
  • Scoring mandatory requirements instead of screening on them.
  • False precision — a total of 4.05 against 3.65 is not a meaningful margin.
  • Presenting the matrix as the decision. It is the working; the judgement is still yours to make and to own.