Technology Selection: [Capability or component name]

Technology selection and build-versus-buy evaluation for one capability: problem and requirements, candidate options (build, buy, open source, managed service), a weighted decision matrix, proof-of-concept results, risks, a cost summary and the decision with its ADR link. Based on the common weighted decision matrix practice. Replace every [bracketed] value and every row marked Example; the worked example evaluates a product search capability.

1. Problem and context

[Describe the problem in two or three paragraphs. Include the current state, the pain it causes and the trigger for this evaluation.]

Example: The web shop uses SQL LIKE queries for product search. Median search latency is 1.8 s at peak, typo tolerance is absent, and analytics show that 31% of searches return zero results. Search-led conversion is 40% below the industry benchmark our product team tracks. The catalogue will grow from 120,000 to 600,000 SKUs after the marketplace launch in Q3, which the current approach cannot support.

2. Requirements and evaluation criteria

2.1 Must-have requirements (knock-out)

Any option that fails one of these is excluded and not scored.

2.2 Scored criteria and weights

Weights are agreed before scoring and sum to 1.00. Scores use a 1 to 5 scale defined in the scale table.

3. Options considered

3.1 Option notes

For each option, record what was learned beyond the matrix.

• OA Build: [Strengths, gaps, effort estimate.] Example: Full control and no licence fee; ranking and typo tolerance need about 14 engineer-weeks; merchandiser tooling would be built from scratch.
• OB Buy: [Notes.] Example: Best relevance out of the box; pricing is per 1,000 searches, so cost rises with traffic; export of synonyms and rules is possible through the API.
• OC Open source: [Notes.] Example: Mature engine and large community; we would own cluster upgrades and on-call; no merchandiser UI.
• OD Managed service: [Notes.] Example: Same engine as OC with upgrades and snapshots handled by the provider; still no merchandiser UI.

4. Weighted decision matrix

The weighted score of option $j$ is the sum over criteria of weight times score:

where $w_i$ is the weight of criterion $i$ and $s_{ij}$ is the score of option $j$ on criterion $i$ (1 to 5). The maximum possible score is 5.00.

Example arithmetic for OB Buy: $S_B = 0.30(4) + 0.20(3) + 0.15(4) + 0.15(5) + 0.10(4) + 0.10(2) = 1.20 + 0.60 + 0.60 + 0.75 + 0.40 + 0.20 = 3.75$.

Example arithmetic for OD Managed: $S_D = 1.20 + 0.60 + 0.60 + 0.60 + 0.30 + 0.30 = 3.60$.

4.1 Sensitivity check

A difference of less than about 0.25 between the top two options is within scoring noise. Re-run the matrix with plausible alternative weights and record whether the ranking changes.

The second row is computed by moving 0.10 of weight from C1 to C6: for OB, $3.75 - 0.10(4) + 0.10(2) = 3.55$; for OA, $3.55 - 0.10(5) + 0.10(5) = 3.55$.

5. Proof-of-concept results

PoC scope: [dataset, query set, load profile, duration, team]. Example: 120,000 real SKUs, 5,000 logged queries replayed, 300 QPS for 30 minutes, two engineers for two weeks per option.

Findings: [Summarise what surprised the team and which scores changed after the PoC.] Example: OA missed the latency target until a dedicated read replica was added; this cost is reflected in C2.

6. Risks

7. Cost summary

Detailed figures are in [TCO analysis link]. Currency: [USD], horizon [3] years, nominal values.

8. Decision

Rationale: [Two to four sentences.] Example: OB leads the matrix and was the only option to meet every PoC target including merchandiser set-up. OD is a close second and remains the fallback; its lower lock-in did not outweigh the missing merchandiser tooling, which would cost an estimated 10 engineer-weeks to build.

9. Next steps

Standard and licence note

Based on common industry practice for technology selection and the weighted decision matrix (weighted-sum scoring). Original text written for this collection; example products, scores and figures are illustrative. Template licence: MIT.