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Observability for a Lean Stack: Metrics and Signals to Identify Underused Platforms

oopensoftware

2026-01-29

9 min read

Use telemetry and business KPIs to spot underused platforms, prove cost savings, and run safe decommissioning in 2026.

Hook: Your bill keeps growing while teams say they don't use the tool — observability can prove it

Platform sprawl is an operational tax: more integrations, more alerts, more cognitive overhead, and recurring invoices for software that quietly sits idle. For DevOps, platform engineering, and SRE teams in 2026, the real question isn't whether a tool is nice — it's whether it's materially used and delivering measurable value. This article shows the exact telemetry and business signals that correlate with underused tooling, how to instrument them, and how to build a defensible report your engineering leadership and procurement teams will accept.

The top-line answer (most important first)

If a platform shows low direct usage, minimal business impact, and a poor cost-to-value ratio — confirmed by correlated telemetry and business KPIs across at least 90 days — it is a candidate for elimination or consolidation. The rest of this guide explains exactly which signals to collect, how to collect them, and how to build a defensible report your engineering leadership and procurement teams will accept.

Why this matters in 2026: trends that make telemetry-driven pruning essential

OpenTelemetry as de facto standard: By 2025–2026, most cloud-native stacks standardize on OpenTelemetry-compatible metrics/traces/logs, making cross-platform signal correlation practical. See broader observability patterns in consumer platforms at Observability Patterns We’re Betting On.
FinOps and platform cost accountability: Organizations now require cost-per-feature and cost-per-seat calculations before renewing contracts; pair these practices with an Analytics Playbook to standardize cost attribution.
AI-assisted observability: Modern AIOps surfaces anomalous low-usage patterns automatically, but human-validated signals are still required for contractual decisions.
Consolidation pressure: Tool vendors continue to increase functionality via acquisitions, which creates overlap and opportunities to eliminate stand-alone products. For operational playbooks focused on reducing surface area and sustainable ops, see Beyond Instances: Operational Playbook for Micro-Edge VPS, Observability & Sustainable Ops.

Which signals reliably indicate underutilized tooling

Group signals into three categories: direct usage, operational/cost, and business impact. You need evidence from all three to make a strong case.

Direct usage signals

Monthly Active Users (MAU) / Daily Active Users (DAU): Number of unique users who performed an action in the tool per period.
API key / token activity: Number of unique API keys that saw activity in 90 days.
Feature/event counts: Counts of core events (runs, jobs, feature flag evaluations, tests, dashboards viewed).
Seats assigned vs seats active: License seats assigned compared to seats actually active (login or action).
Integrations in use: Number of upstream/downstream integrations actively exchanging data.

Operational and cost signals

Cost per active user / cost per run: Monthly spend divided by MAU, or cost divided by job runs.
Support tickets and incident correlation: Number of support tickets and incidents referencing the tool; low tickets + low integration suggests low operational value, but low tickets with high invoices is a red flag.
Log and trace volume: Is the tool producing logs/traces/metrics? Very low ingestion for a billed product is suspicious.
Time-to-resolution (TTR) for tool-related issues: If teams never open tickets, there may be no reliance; conversely, many tickets with low adoption means poor ROI.

Business signals

Revenue or conversion influence: Percent of revenue actions or conversion funnels that touch the platform.
Feature dependencies: Number of product features or automations that require the tool.
Contractual constraints: Renewal terms, minimum commitment clauses, or mandatory data residency that would complicate removal.
Strategic alignment: Whether the platform aligns with team roadmaps and architecture direction (e.g., preference for self-hosted or cloud-native alternatives).

How to instrument — precise telemetry you should collect

Instrumentation must be lightweight and consistent across tools. If you already use OpenTelemetry, extend it with a short set of attributes and metrics per tool. If not, add an application-level usage event pipeline (e.g., analytics events sent to a consolidated observability backend).

Standard tagging scheme (must-have attributes)

team: owning team or product area (e.g., team=payments)
platform: canonical platform name (e.g., platform=a-b-testing)
environment: prod|staging|dev
action: high-level event (login, run-job, view-dashboard, api-call)
customer_or_workspace: tenant id for multi-tenant usage
cost_center: finance tag used for chargebacks

Visual system diagrams and canonical attribute choices help teams stay consistent — see evolving diagram practices at The Evolution of System Diagrams in 2026.

Metric types to emit

Counter for usage events: tool_action_total{action="run",platform="ci"}
Gauge for active seats: tool_active_seats{platform="ide"}
Histogram for response times: tool_api_latency_seconds
Distribution for job sizes: tool_job_duration_seconds

Example OpenTelemetry span attributes

attributes:
  "platform.name": "feature-flag-service",
  "platform.action": "eval",
  "team.owner": "growth",
  "tenant.id": "workspace_123",
  "cost.center": "eng-growth"

If you instrument edge or on-device agents, refer to patterns in Observability for Edge AI Agents in 2026 when selecting attributes and privacy-safe metadata.

Practical instrumentation snippets

Prometheus-friendly counter example (instrumented server-side):

// Go (promhttp)
var toolAction = prometheus.NewCounterVec(
  prometheus.CounterOpts{Name: "tool_action_total", Help: "Tool actions by platform and action"},
  []string{"platform", "action", "team"},
)

// increment when a user runs a job
toolAction.WithLabelValues("ci", "run", "platform-eng").Inc()

Example OpenTelemetry event (pseudo-code):

span.add_event("tool.action", {
  "platform.name": "analytics-warehouse",
  "action": "query",
  "user.id": "u-123",
  "workspace": "acct-456"
})

If you need to feed on-device telemetry into a central analytics store, see techniques for Integrating On-Device AI with Cloud Analytics.

Concrete queries and dashboard panels to detect underuse

These queries assume a central metrics store (Prometheus) and a logs/analytics datastore (like ClickHouse, BigQuery, or Elasticsearch).

PromQL: 90-day active users per platform

sum by(platform) (increase(tool_user_action_total[90d]))

PromQL: Cost per active user

sum(cost_allocation_monthly{platform=~".*"}) by (platform)
/ 
(sum by(platform) (increase(tool_user_action_total[30d])))

SQL: Seats assigned vs seats active (90-day)

SELECT
  platform,
  COUNT(DISTINCT seat_id) AS seats_assigned,
  COUNT(DISTINCT CASE WHEN last_active >= DATE_SUB(CURRENT_DATE, INTERVAL 90 DAY) THEN seat_id END) AS seats_active,
  ROUND(100.0 * seats_active / seats_assigned, 2) AS pct_active
FROM seat_inventory
GROUP BY platform;

Alert examples

Alert when MAU < 10 AND cost > $2000/month for 60 days
Alert when seat utilization < 20% for 90 days

Decision framework: turning signals into a recommendation

Use a decision matrix weighted across four dimensions. Score each platform 1–5:

Usage (30%): MAU, API calls, runs
Cost (25%): monthly spend, cost per active user
Business impact (30%): revenue influence, feature dependencies
Risk & lock-in (15%): data gravity, compliance, migration complexity

Example: Platforms with a weighted score < 2.0 should be moved to a 'decommission candidate' board and evaluated with a migration runbook. For migration runbooks and orchestration considerations, cross-reference cloud-native orchestration best practices at Cloud-Native Workflow Orchestration.

How to build a defensible elimination report

Your stakeholders want a short executive summary plus reproducible data. The report should include:

Executive summary: Recommendation and topline savings (12 months).
Usage evidence: MAU/DAU graphs, seat utilization, API call heatmap over 90–180 days.
Cost analysis: license, cloud cost, support, staff time per month.
Business dependencies: features integrated, customers using it, critical automations.
Risk assessment: migration effort, data exportability, compliance.
Migration & rollback plan: timeline, owners, success criteria, fallbacks.
Decision matrix: score and rationale for the recommendation.

Make all charts reproducible: include the queries (PromQL/SQL) and the date ranges used. Stakeholders often ask for raw numbers — provide CSV exports as appendices. For applied analytics playbooks and dashboard examples see Analytics Playbook for Data-Informed Departments.

Example case study (anonymized, plausible workflow)

In late 2025 a fintech company reviewed a standalone A/B testing service. Telemetry showed:

MAU: 18 users (platform: experimentation)
Seat assigned: 120 (many dormant)
API calls: < 100/month
Cost: $6,000/month license + $1,200/month infra
Business impact: 2 product experiments referenced the tool, both had been ported to a home-grown feature-flagging system earlier.

After running the decision matrix, the platform scored 1.3. The migration plan consisted of a 60-day freeze on new experiments, export of experiment results (CSV), a shadow mode where new experiments were simultaneously run on the home-grown system for validation, and contract non-renewal at the next billing cycle. The result: $84k/year reclaimed, reduced integration surface area, and cleaner instrumentation in OpenTelemetry for remaining experimentation pathways. This kind of outcome aligns with the consolidation and observability patterns discussed in Observability Patterns We’re Betting On.

Safe decommissioning runbook (operational checklist)

Stakeholder alignment: Confirm owners, affected teams, and customer impacts.
Data export: Export all data and metadata (metrics, logs, audit trails).
Freeze writes: Disable new tenants/experiments; place tool in read-only if possible.
Shadow migration: Run critical flows in parallel on replacement solution for 2–4 weeks.
Monitoring: Track feature parity and error rates; establish rollback triggers.
Contract & billing: Notify vendor, confirm contract termination windows, document cost savings.
Archive & delete: Archive data per retention policy, then permanently delete per compliance.
Update CMDB & runbooks: Remove platform entries, update diagrams, and retrain on replacement flows.

For patch orchestration and safe shutdown patterns that reduce the risk of a failed decommission, see Patch Orchestration Runbook.

Governance and automated detection you should implement

To avoid recurring sprawl, embed this process into your platform governance:

Tag every platform in your CMDB with the standard tags (team, platform, cost_center). Operational playbooks for micro-edge fleets recommend consistent tagging—see Beyond Instances.
Quarterly utilization audits driven by a FinOps + Platform Engineering playbook.
Automated low-use alerts: When utilization thresholds are breached for 90 days, create a ticket to review the platform.
Procurement guardrails: New purchasing requests must include expected MAU, integrations, and a 12-month sunset evaluation.

Advanced strategies and future predictions (2026+)

Expect two big shifts in the next 12–24 months that change how you measure underuse:

Signal fusion: Observability platforms will increasingly fuse telemetry with business events (CRM, billing) so that cost-per-conversion becomes a standard metric out of the box. Techniques for feeding disparate event sources into central analytics stores are covered at Integrating On-Device AI with Cloud Analytics.
AI-driven recommendations: AIOps tools will suggest consolidation candidates, but human governance will still be required for contractual and compliance decisions.

Adopt these advanced moves now:

Instrument downstream business events: Tie tool usage to business outcomes (purchases, sign-ups) using the same telemetry pipeline.
Use feature flags to decouple dependencies: Feature flagging enables safe switch-off experiments to measure business impact before full removal. Also see orchestration strategies at Cloud-Native Workflow Orchestration.
Automate cost attribution: Connect your cloud billing and vendor invoices into the observability platform so cost signals surface in the same dashboard as usage. An analytics playbook helps operationalize this.

Actionable takeaways (what to do this week)

Implement the standard tagging scheme for platforms in your CMDB and telemetry pipeline.
Create three dashboards: Usage, Cost-per-user, and Dependency graph for all paid platforms.
Run the 90-day utilization queries for the top 10 costliest platforms and score them with the decision matrix.
If any platform scores < 2.0, draft a short elimination report and circulate to procurement and platform owners.

Rule of thumb: Tools that cost more than $2,000/month and have < 10 MAU for 90 days are almost always candidates for consolidation — but always validate business dependencies before cutting the cord.

Closing: justify elimination with reproducible telemetry and a safe plan

Eliminating platforms is a technical and organizational process. In 2026, you can use unified telemetry (OpenTelemetry + centralized metrics), FinOps practices, and a clear decision framework to make defensible choices. The combination of direct usage signals, cost signals, and business impact evidence gives you the story leaders need: data-backed savings and a controlled migration path.

Call to action

Start with one platform this quarter. Instrument the minimal signals above, run the 90-day analysis, and prepare a one-page elimination report. If you'd like a template or an audit playbook tailored to your stack, request our Platform Utilization Audit Kit for engineers and procurement teams — it includes dashboards, PromQL/SQL queries, and a decommissioning checklist you can run within 7 days. For micro-edge and field guidance, review Beyond Instances, and for edge agent observability read Observability for Edge AI Agents.

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