Detect and retire: scripts and workflows to reduce tool sprawl in DevOps stacks

Detect and retire: scripts and workflows to reduce tool sprawl in DevOps stacks

Hook: If your deployment pipelines are fragile, cloud bills keep spiking, and teams debate which chat, CI or observability tool to use—you don’t need another vendor. You need a reliable way to detect underused platforms, measure their real cost, and safely retire them with automation.

The problem in 2026: why tool sprawl matters now

By early 2026 the explosion of AI-first SaaS, low-code integrations and cheap trial tiers drove a tidal wave of new tools into engineering and product teams. The result is the same pattern we saw in 2024–25 but amplified: overlapping capabilities, brittle integrations, and unpredictable total cost of ownership (TCO). Teams spend time integrating and maintaining platforms instead of shipping features.

What’s different in 2026:

• Wider adoption of FinOps and SaaS spend management tools, but many organizations still lack automated inventory pipelines.
• SSO and SCIM logging is now a mainstream source of truth for SaaS usage, making programmatic discovery practical.
• Regulatory pressure (data residency, security audits) and consolidation waves in late 2025 increased incentives to retire redundant platforms.

Goal: practical automation patterns

This article gives an operational playbook you can run this week: scripts to assemble a tool inventory, metrics to compute utilization and TCO, and a repeatable retirement pipeline that minimizes risk and audit fallout.

High-level workflow

1. Discover and inventory every SaaS and internal tool (automated).
2. Enrich inventory with usage and cost signals.
3. Score and rank tools for retirement candidates.
4. Run an automated retirement pipeline (notifications, exports, disable, cancel).
5. Verify savings and update governance artifacts.

1) Discovery: building a programmatic tool inventory

Start from canonical sources of truth and cover blind spots. Prioritize automation: manual spreadsheets fail once a quarter.

Primary discovery sources

• SSO providers (Okta, Azure AD, Google Workspace logs) — list connected apps, provisioning via SCIM, last authentication dates.
• Billing and procurement systems — corporate card feeds, billing APIs, and accounts-payable records.
• Network and proxy logs — discovered SaaS endpoints from web proxies, CASB logs, ZTNA telemetry.
• Source control & CI/CD — CI pipeline config (GitHub Actions, GitLab CI), Terraform state, and provider resources reveal managed SaaS and infra.
• Employee feedback — a lightweight web form or Slack command to declare tools the team depends on (captures shadow IT).

Example: Quick Okta-based inventory script (Python)

Okta SSO is a rich source: it lists apps, user assignments, and lastLogin. Save app IDs to your inventory DB.

#!/usr/bin/env python3
import os, requests
OKTA_DOMAIN = os.environ['OKTA_DOMAIN']
API_TOKEN = os.environ['OKTA_API_TOKEN']
headers = {'Authorization': f'SSWS {API_TOKEN}', 'Accept': 'application/json'}
apps = []
url = f'https://{OKTA_DOMAIN}/api/v1/apps'
params = {'limit': 200}
while url:
    r = requests.get(url, headers=headers, params=params)
    r.raise_for_status()
    data = r.json()
    for app in data:
        apps.append({
            'id': app['id'], 'label': app['label'], 'status': app['status'],
            'lastUpdated': app.get('lastUpdated'), 'signOnMode': app.get('signOnMode')
        })
    url = r.links.get('next', {}).get('url')
print({'count': len(apps)})
# save to inventory store (DB or S3)

Similar scripts for Azure AD (Microsoft Graph), Google Workspace Admin SDK, and CASB APIs are equally useful.

2) Enrichment: measure utilization and cost

Raw inventory must be enriched with signals to be useful. Two classes of signals matter:

• Utilization signals — active users (30/90/180-day), API calls, daily active devices, recent logins, role assignments.
• Cost signals — subscription fees, overage charges, storage costs, integration maintenance time (estimates), and provisioning infra costs if deployed in-cloud.

Computing practical utilization metrics

Use a consistent window (90 days is a good baseline). Example metrics to compute for each tool:

• Active users (90d): unique users who authenticated or performed an action.
• License utilization: active users / purchased licenses.
• Requests per day: average API or webhook events per day.
• Critical integrations: number of upstream/downstream systems depending on the tool (count webhooks, connectors, Terraform modules).
• Cost per active user (TCO): (monthly subscription + attributed infra & maintenance) / active_users.

Example SQL (pseudo) to compute license utilization:

SELECT
  tool_id,
  purchased_licenses,
  COUNT(DISTINCT user_id) FILTER (WHERE last_login >= now() - interval '90 days') as active_users,
  (COUNT(DISTINCT user_id) FILTER (WHERE last_login >= now() - interval '90 days')::float / purchased_licenses) as utilization
FROM tool_inventory
LEFT JOIN user_auth_log USING (tool_id)
GROUP BY tool_id, purchased_licenses;

Pulling cost data

Use cloud billing APIs and vendor invoices. For cloud resources, Cloud Cost APIs (AWS Cost Explorer, Azure Cost Management, GCP Billing) can attribute spend to tags or accounts that map to tooling. For SaaS, parse invoices or use expense management APIs (Coupa, SAP Concur) to attribute vendor spend to teams.

3) Scoring: how to pick retirement candidates

Transform metrics into a score so you can prioritize. Keep the scoring transparent and auditable.

Sample scoring model

• UtilizationScore = 1 - min(1, active_users_90d / purchased_licenses)
• CostScore = normalize(monthly_cost_per_tool)
• IntegrationRisk = min(1, critical_integrations / 5)
• SecurityRisk = boolean flag (1 if noncompliant with policies in last 12 months)

FinalScore = 0.4*UtilizationScore + 0.3*CostScore + 0.2*IntegrationRisk + 0.1*SecurityRisk

Rank tools by FinalScore descending. Flag anything with FinalScore > 0.7 as a candidate for retirement review.

4) Retirement pipeline: automated, safe, and auditable

Retirement should be a controlled, reversible workflow. Build it like a CI/CD pipeline:

1. Open a retirement RFC (ticket, PR in a Tool Governance repo).
2. Run pre-checks automatically (data export capability, legal holds, integrations impacted).
3. Notify and pause — stakeholders have defined SLA to respond (e.g., 7 days).
4. Automate export and archival of data.
5. Disable provisioning and new seat purchases.
6. Set read-only mode, then disable access after a freeze window.
7. Cancel subscription and validate invoice closure.
8. Post-retirement verification and update inventory and cost dashboards.

Example GitHub Actions retirement workflow (YAML)

name: retire-tool
on:
  workflow_dispatch:
  repository_dispatch:
jobs:
  pre-check:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout governance repo
        uses: actions/checkout@v4
      - name: Run pre-check script
        run: |
          python scripts/pre_check.py --tool ${{ github.event.client_payload.tool_id }}
  run-retirement:
    needs: pre-check
    if: ${{ needs.pre-check.result == 'success' }}
    runs-on: ubuntu-latest
    steps:
      - name: Export data
        run: python scripts/export_tool_data.py --tool ${{ github.event.client_payload.tool_id }} --out s3://retire-exports/${{ github.event.client_payload.tool_id }}
      - name: Set read-only
        run: python scripts/set_readonly.py --tool ${{ github.event.client_payload.tool_id }}
      - name: Disable provisioning
        run: python scripts/disable_provision.py --tool ${{ github.event.client_payload.tool_id }}
      - name: Cancel subscription
        run: python scripts/cancel_subscription.py --tool ${{ github.event.client_payload.tool_id }}
      - name: Update inventory
        run: python scripts/update_inventory.py --tool ${{ github.event.client_payload.tool_id }} --status retired

Each script should be idempotent, logged, and run with least privilege. Keep secrets in your CI secrets store and use short-lived credentials where possible.

Data export checklist

• Confirm export formats supported (CSV, JSON, PST, DB dump).
• Preserve metadata and timestamps for audits.
• Store exports in an immutable, access-controlled archive (object storage with lock).
• Verify exports are restorable with an automated sample restore test.

5) Post-retirement: validate savings and close loops

After retirement, verify that subscriptions are canceled and costs stopped. Run a 30/60/90 day billing check to confirm no residual charges. Update the inventory, architecture diagrams, runbooks and internal catalogs.

Measure impact

• Direct savings: canceled subscription line items.
• Indirect savings: reduced integration maintenance (estimated developer-hours), consolidated support contracts, fewer security alerts.
• Operational improvements: reduction in mean time to repair (MTTR) if fewer tools to troubleshoot.

Operational governance: people and process

Tool cleanup combines FinOps, SRE, security, procurement and product. Adopt a repeatable governance loop:

• Create a Tool Review Board that meets monthly to accept or reject retirement candidates.
• Define SLAs for stakeholder responses and a default retirement policy for low-risk tools.
• Embed inventory ownership in team charters—teams must justify owned tools quarterly.

Runbooks and policy examples

Policies reduce negotiation friction. Example rule: "Any SaaS with utilization < 10% and > $2k/mo cost is auto-flagged; if no stakeholder responds in 10 business days it moves to retirement preparation."

Real-world example: a 2025 cleanup that saved 35% of SaaS spend

Case study (anonymized): A mid-size cloud platform team noticed rising SaaS spend in late 2024/early 2025 after adopting several AI tools and observability vendors. They built an automated pipeline during Q3–Q4 2025 that combined Okta logs, billing APIs and network telemetry. After six months they retired:

• Two redundant observability vendors (lowered cost 20%).
• Three low-usage collaboration tools (cut recurring charges by 10%).
• One legacy vendor that required expensive integrations—migrated to SaaS-native features and recouped engineering hours.

Outcome: 35% reduction in SaaS line items and measurable reduction in incident surface due to fewer integrations. They invested those savings back into automation and a centralized feature flag platform.

Advanced strategies and predictions for 2026+

As we move through 2026, expect these trends to shape tool cleanup:

• More standardization of SSO and usage telemetry: vendors increasingly expose usage APIs and SCIM event hooks by default, making discovery more accurate.
• Policy-as-code for tool governance: expect policy engines that evaluate inventory changes against compliance and cost rules before approving procurement.
• AI-assisted inventory reconciliation: LLMs will help map vendor capabilities and recommend consolidation candidates, but human review remains essential for risk assessment.
• Stronger FinOps integration: inclusion of opportunity cost (engineer-hours) and performance impact in TCO calculations will make retirement decisions more business-driven.

Practical checklist to run this week

1. Extract SSO app list and save to a canonical inventory store (scripted).
2. Pull 90-day active user numbers for each app and compute license utilization.
3. Aggregate monthly spend from billing APIs and compute cost per active user.
4. Rank tools using a simple scoring model and open retirement RFCs for top candidates.
5. Automate one full retirement run for a low-risk tool to validate the pipeline.

Common pitfalls and how to avoid them

• Pitfall: Relying only on purchase records—misses shadow IT. Fix: combine SSO and proxy logs.
• Pitfall: Cancelling tools without export verification. Fix: run restore test on an export snapshot.
• Pitfall: Not involving legal/security early. Fix: embed compliance checks in pre-check scripts.
• Pitfall: One-off scripts without version control. Fix: keep inventory automation in Git and run via CI (GitOps pattern).

Actionable templates and resources

Include these in your toolkit:

• Inventory schema (JSON): id, label, vendor, team_owner, purchase_account, monthly_cost, purchased_licenses, active_users_90d, integrations_count, compliance_flags, status.
• Pre-check script templates: verify exports, check legal holds, list integrations.
• Retirement pipeline (CI workflow): export & verify -> set read-only -> disable provisioning -> cancel -> verify billing.

Final takeaways

• Automate discovery and enrichment using SSO logs, billing APIs, and network telemetry—don’t rely on memory or spreadsheets.
• Score objectively with utilization, cost and integration risk to prioritize candidates.
• Build a reversible, auditable retirement pipeline—export, freeze, disable, cancel, verify.
• Tie tool cleanup to FinOps and security goals to unlock budget and reduce operational risk.
• Start small: retire one low-risk tool end-to-end to validate your workflow, then scale.

“Tool sprawl is a governance problem—one you can automate and measure.”

Call to action

Ready to reduce unnecessary cost and complexity? Start by running the Okta inventory script and compute 90-day utilization for your top 20 SaaS vendors. If you want a jumpstart, download our (free) retirement pipeline templates, or contact our engineering operations team for a hands-on workshop to implement a GitOps retirement pipeline in 30 days.

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