The 8 Metrics DevOps Teams Should Use to Decide When to Kill a Tool

Too many platforms, not enough time. If your on-call rotations include deciphering which tool to touch first, or your CI/CD templates import five different SDKs to do one thing, you have a decision to make: keep, consolidate, or kill. This article gives engineers and infra teams a practical, reproducible scoring model — centered on eight technical metrics — to decide when a tool is adding value or becoming technical debt.

Why a scoring model — and why now (2026)

In late 2025 and heading into 2026, two clear trends changed the calculus for tool ownership. First, vendor consolidation and acquisitions accelerated; buyers are seeing overlapping features across platforms as vendors integrate acquired stacks into larger offerings. Second, major cloud providers and incumbent vendors embedded advanced automation and AI features directly into their platforms, reducing the need for many point products.

That combination increases pressure on engineering teams to justify each tool’s existence. A structured, measurable approach prevents a noisy, subjective “gut” decision and creates a repeatable playbook for deprecation and migration.

Top-level rule (inverted pyramid): Kill a tool only when it fails to deliver measurable value across critical technical and operational metrics

Use the model below to get a single score that reflects both technical impact and business value. Scores guide a decision: Keep, Consolidate / Re-evaluate, or Deprecate.

The 8 metrics DevOps teams should use

Each metric is scored 0–5 (0 = catastrophic, 5 = excellent). Multiply by the metric weight (customizable) to compute a weighted total out of 100. I’ve included measurement tips, examples, and when a low score is a red flag.

1) MTTR impact (weight default: 20%)

Why it matters: If a tool increases Mean Time To Recovery for incidents — by being brittle, opaque, or slow to instrument — it actively harms reliability.

• How to measure: Correlate incident tickets where the tool is in the causal chain. Calculate average incident duration with and without the tool in the path.
• Signals: Long handoffs between systems, repeated paging tied to the tool, runbooks that frequently say “restart X”.
• Scoring guide: 5 = tool reduces MTTR or is neutral and provides authoritative data; 0 = tool consistently lengthens resolution or obscures root cause.

2) Integration surface & API quality (weight default: 15%)

Why it matters: A tool that is easy to integrate and automate is less costly to operate. A rich API surface enables CI/CD, IaC, and scripted migrations.

• How to measure: Count first-class integrations (SSO, SIEM, cloud provider), evaluate API completeness (CRUD for core entities), and test idempotency.
• Signals: SDKs for major languages, REST/GraphQL APIs, webhooks, Terraform provider or Helm chart; or conversely, only a web UI and manual steps.
• Scoring guide: 5 = first-class APIs + IaC provider + SDK; 0 = manual-only UI with no automation path.

3) Automation friendliness (weight default: 15%)

Why it matters: Automation reduces ongoing human effort and enables reproducible environments via IaC and CI/CD.

• How to measure: Presence of Terraform modules, CloudFormation/ARM templates, official CLI, CI/CD actions, test harnesses, and idempotent operations.
• Signals: A maintained Terraform provider, documented rate limits, predictable error codes, and example pipelines.
• Scoring guide: 5 = full IaC support + CI/CD reference pipelines; 0 = manual-only or brittle automation with frequent failures.

4) Team adoption & operational usage (weight default: 20%)

Why it matters: Tools sitting idle still cost money. Usage and adoption are direct proxies for value.

• How to measure: Active users (SSO logs), frequency of API calls, number of repos referencing the tool in IaC/CI, and tickets/issues filed against it. Measure trends over 3–6 months.
• Signals: Broad, regular use across teams and positive feedback in retros; or niche usage by just one team with zero new onboardings.
• Scoring guide: 5 = wide adoption and increasing usage; 0 = single-team legacy usage and no growth.

5) Total cost of ownership (weight default: 15%)

Why it matters: Direct subscription fees are only the start. Hidden costs include engineering time, integration maintenance, and duplicated capabilities.

• How to measure: Combine subscription + infrastructure spend + estimated engineering hours spent maintaining integrations (use time tracking or runbook frequency as proxies).
• Signals: Rising invoice with static usage, frequent support escalations that require engineering time, duplicated functionality across other owned platforms.
• Scoring guide: 5 = net positive ROI and low maintenance; 0 = costly both financially and in engineering time with duplicated capabilities available elsewhere.

6) Observability & telemetry export (weight default: 5%)

Why it matters: If you can’t instrument, trace, or export telemetry, the tool becomes a blind spot.

• How to measure: Can you stream logs/metrics/traces to your central observability platform? Are audit logs available for retention and SIEM export?
• Signals: Native exporters, OpenTelemetry support, or at least structured logs via API; or locked-in logs accessible only via their UI.
• Scoring guide: 5 = first-class telemetry + export; 0 = opaque UI-only metrics and no exports.

7) Security, compliance & vendor lock-in risk (weight default: 5%)

Why it matters: A tool that weakens your security posture or locks your data into proprietary formats is high risk.

• How to measure: Check for SOC2 / ISO certifications, exportable data formats, fine-grained RBAC, SSO support, encryption at rest/in-flight, and contractual exit clauses.
• Signals: Lack of audit trails, closed data formats, or contractual terms that complicate exit.
• Scoring guide: 5 = strong compliance posture + easy exit; 0 = significant risks or no export path.

8) Technical debt & maintainability (weight default: 5%)

Why it matters: Custom glue code, brittle adapters, or single-person knowledge silos increase ongoing maintenance burden and risk.

• How to measure: Count bespoke integrations, check PR frequency for adapters, identify single maintainers, and search for custom scripts in infra repositories.
• Signals: Hand-coded adapters in your repos, frequent ad-hoc patches, or undocumented processes.
• Scoring guide: 5 = minimal custom code and cross-trained owners; 0 = heavy bespoke integration and at-risk knowledge silos.

Putting scores to work: sample scoring model and thresholds

Default weights (you should tune these to your org):

• MTTR: 20%
• Adoption: 20%
• Integration/API: 15%
• Automation friendliness: 15%
• Cost: 15%
• Observability: 5%
• Security: 5%
• Technical debt: 5%

Compute total = sum(metric_score × weight). Example thresholds:

• >= 70: Keep and invest — the tool provides clear value and integrates well with IaC/CI workflows.
• 40–69: Re-evaluate — consider consolidation, negotiate pricing, or run a targeted migration pilot.
• < 40: Deprecate — prepare a deprecation plan and migrate data and workflows off the tool.

How to run the evaluation (practical, reproducible steps)

1. Inventory sources: SSO logs for active users, cloud billing for direct spend, IaC repos for references, CI pipelines for usage, incident tickets annotated with tool tags, and runbook frequency.
2. Automate evidence collection: Write scripts to scan repos for provider names (terraform, helm charts, pipeline YAML). Example: ripgrep "provider_name" across your mono-repos to find usage points.
3. Correlate incidents: Tag incidents where the tool was a factor. Use on-call systems (PagerDuty, Opsgenie) and your ticketing system to extract MTTR per tool.
4. Measure API & IaC support: Check for official Terraform provider, Helm charts, Ansible modules, SDKs, or a documented HTTP API and CLI. Try a basic CRUD operation via API — if you can’t automate it in CI, score low on automation.
5. Run adoption queries: Query SSO logs: active users per 30-day window; query git history for recent commits touching provider modules.
6. Estimate engineering burden: Interview owners — ask for time spent weekly on maintenance. Use time-entry as a cross-check.
7. Compile a scorecard: Record raw evidence and numeric scores. Store the scorecard in a shared doc or repo for auditability.

From score to action: deprecation playbook (technical checklist)

Scoring only gets you to a decision. The heavy lifting is the deprecation plan. Use this checklist as your runbook.

• Stakeholder alignment: Notify product owners, security, legal, finance, and platform engineering. Agree on timelines and success metrics.
• Freeze new onboardings: Immediately stop new teams from adopting the tool unless there is a compelling exception process.
• Inventory consumers: Map all producers and consumers (APIs, IaC, CI pipelines). Use automated repo scans and runtime telemetry.
• Create migration recipes: Provide IaC modules for the target platform, CI/CD templates, and data export/import scripts.
• Run pilots and canaries: Migrate a low-risk service as a canary. Implement a feature-flagged rollout and monitor MTTR and errors.
• Prepare rollback paths: Keep compatibility shims for at least one release cycle. Document rollback scripts and thresholds for aborting the migration.
• Archive data & access: Export logs and configuration in open formats (JSON/CSV), and define retention policy post-deprecation.
• Cutover and measure: After migration, monitor the same metrics used in the evaluation for 90 days and measure savings, incident counts, and developer velocity improvements.

Real-world example (hypothetical, practical)

Platform team "Northstar" maintained an internal feature-flag service that had grown complex. They ran the scoring model and got a 33/100: low automation (no Terraform), high MTTR impact (frequent stale flags leading to incidents), narrow adoption (only three teams), and significant bespoke integrations.

Using the deprecation playbook, they:

• Stopped new onboards, exported flag state as JSON, and wrote a Terraform module that mapped feature flags into the chosen SaaS provider or a standard open-source alternative.
• Piloted migration for a non-critical service, rolled forward when metrics showed MTTR unchanged and lower maintenance time.
• After 90 days, they reduced monthly costs by 22% and reclaimed two FTE-hours per week previously spent on flag maintenance.

This example illustrates the power of a data-driven decision plus a controlled migration plan.

Advanced strategies and 2026 best practices

By 2026, successful teams use these additional tactics:

• Policy-as-code for onboarding: Use policy checks in CI to block new tools unless they meet minimum API/IaC/telemetry requirements. This prevents tool sprawl at source.
• Automated repo scanning: Add a nightly job that scans repos for undisclosed dependencies and flags orphaned integrations.
• Negotiation leverage: Use scorecards to negotiate pricing or request vendor commitments (e.g., Terraform provider roadmaps) before renewing contracts.
• Exit clauses & export tests: Require a test export before procurement, then automate a scheduled export to ensure data portability remains functional.
• Embrace open standards: Prefer tools that support OpenTelemetry, Open Policy Agent, and well-documented IaC providers to minimize lock-in risk.

"If a platform cannot be scripted into your CI/CD or reproduced via IaC, it becomes a long-term liability."

Checklist: Quick evaluation run (30–90 minutes)

1. Scan repos for provider references (ripgrep / git grep).
2. Query SSO for active users in last 30 days.
3. Pull last 6 months of billing data for direct spend.
4. Query incident tickets for references to the tool and compute average incident duration.
5. Attempt a single automated CRUD using the tool’s API or CLI within a disposable CI job.
6. Assign a provisional score for each metric and compute the weighted total.

Common objections and how to answer them

• "But Slack/PMs say the tool is beloved" — Translate qualitative praise into quantifiable usage and outcomes. High NPS but zero automation or high engineering cost is a red flag.
• "It’s mission-critical" — If mission-critical, expect the tool to have a high MTTR benefit score, first-class APIs, and exportability. If it doesn’t, plan migration as a safety imperative, not a convenience.
• "Vendor promises roadmaps" — Get roadmap commitments in contracts and add interim acceptance tests (e.g., a working Terraform provider) before extending renewals.

Measuring success after a kill

Track these KPIs for 90 days post-deprecation:

• Monthly cost reduction (cloud + subscriptions)
• Change in MTTR and incident frequency
• Engineering time reclaimed (runbook edits, integration maintenance)
• Developer onboarding time for teams previously dependent on the tool

Final thoughts

Tool decisions are technical and human. The strength of this model is not to be a blunt instrument — it’s to make intangible risks visible, prioritize what matters to your team, and create a defensible, repeatable decision process.

In 2026, with consolidation and cloud-native capabilities eating into point-solution value, teams that adopt a scoring-based approach will reduce cost, cut tech debt, and accelerate standardization across IaC and CI/CD workflows.

Actionable takeaways

• Run the 8-metric scorecard for every platform you own — aim for an initial quick-run score within 2 weeks.
• Prioritize MTTR, adoption, and automation when deciding which tools to keep.
• Automate evidence collection (repo scans, SSO queries, incident correlation) to make future evaluations trivial.
• Create a deprecation playbook that includes migration IaC modules and rollback plans.

Call to action

If you want a ready-made scoring spreadsheet, repo-scanning scripts, and an IaC deprecation template tailored to platform teams, reach out to the simpler.cloud team. We’ll share a reproducible scorecard and a practical deprecation playbook you can run against your stack in 30 days.

Related Reading

• Modern Observability in Preprod Microservices — Advanced Strategies & Trends for 2026
• Multi-Cloud Failover Patterns: Architecting Read/Write Datastores Across AWS and Edge CDNs
• News & Analysis 2026: Developer Experience, Secret Rotation and PKI Trends for Multi‑Tenant Vaults
• How ‘Micro’ Apps Are Changing Developer Tooling: What Platform Teams Need to Support Citizen Developers
• Making Mood-Driven Content: Using 'Grey Gardens' and 'Hill House' Aesthetics for Music Videos
• LibreOffice for STEM: Creating Scientific Notation and Equation Templates
• API Blueprint: Social Account Threat Signals Feeding Credential Revocation Systems
• One Charging Station for Your Phone, Watch and Smart Glasses: Which 3-in-1 Pad Works Best?
• VistaPrint Coupon Roundup: Best Promo Codes for Business Cards, Invitations and Merch