Tool-sprawl diagnostic kit for platform teams

Stop guessing which platforms are costing you time and money — run a diagnostic

Platform teams in 2026 are under relentless pressure: faster releases, stricter security, and unpredictable cloud and SaaS spend. Yet the easiest drag on velocity — tool sprawl — is often invisible. This diagnostic kit gives you scripts, metrics, and a compact decision framework to identify underused platforms, estimate their hidden costs, and prioritize consolidation or retirement with confidence.

What you’ll get in this kit

• Practical scripts to inventory SaaS and cloud platforms from identity and billing sources
• Operational and financial metrics that reveal underuse and hidden TCO
• A weighted decision framework to decide: Consolidate, Retire, or Re-platform
• Playbook snippets for safe retirement and consolidation

Why tool sprawl matters in 2026

By late 2025 platform engineering and FinOps disciplines converged: organizations expect platform teams to own not just developer productivity but predictable spend and compliance. At the same time, the explosion of specialized SaaS and AI-assisted tooling made it easy to add point solutions. The result: more integrations, more incidents, and more subscriptions nobody actively uses.

Tool sprawl costs you in three concrete ways:

• Direct spend: recurring SaaS subscriptions, excess cloud resources, duplicate tooling fees
• Operational overhead: maintenance, integrations, onboarding, and incident remediation
• Security & compliance risk: unmanaged apps, stale credentials, and data fragmentation

Step 1 — Inventory: scripts to discover what you actually have

A trustworthy diagnostic starts with inventory. Use identity logs (SSO), billing exports, and cloud APIs — those three sources reveal most of the story. Below are pragmatic scripts and query snippets you can run today.

1) SaaS inventory from your SSO (Okta example)

SSO providers are the single best signal for SaaS usage because every app integrating with your org’s identity has logins and provisioning events.

# Python: fetch Okta apps and active user counts (schematic)
import requests
OKTA_BASE = "https://your-org.okta.com"
TOKEN = "${OKTA_API_TOKEN}"
headers = {"Authorization": f"SSWS {TOKEN}", "Accept": "application/json"}

apps = requests.get(f"{OKTA_BASE}/api/v1/apps", headers=headers).json()
for app in apps:
    print(app['label'], app['id'], app.get('status'))

# Next step: fetch logs for last 90 days to measure active users per app

Use the SSO logs to compute monthly active users (MAU) per app, login frequency, and provisioning metadata (SCIM, SAML, OIDC). If an app has zero logins in 90 days but a paid seat count, it’s a red flag.

2) SaaS spend from invoices & procurement

Collect invoices from procurement or finance systems. If you centralize SaaS charges on a corporate card, automate pulling CSVs and normalize them into a single table. A simple BigQuery or Athena table keyed by vendor and month makes trend analysis trivial.

-- BigQuery: monthly spend per vendor (billing CSV imported)
SELECT vendor, SUM(amount) AS monthly_spend
FROM `org_billing.saas_invoices`
WHERE invoice_date BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 12 MONTH) AND CURRENT_DATE()
GROUP BY vendor
ORDER BY monthly_spend DESC;

3) Cloud inventory and tagging hygiene

Cloud consoles can hide idle resources. Start with billing export + tagging and augment with cloud-native audits.

# AWS example: list EC2 instances with no recent CPU or network activity
aws ec2 describe-instances --query "Reservations[].Instances[].[InstanceId,State.Name,Tags]"

# AWS Cost Explorer (CLI): get cost by service
aws ce get-cost-and-usage --time-period Start=2025-07-01,End=2026-01-01 --granularity MONTHLY --metrics BlendedCost --group-by Type=DIMENSION,Key=SERVICE

Export billing to a warehouse (BigQuery, Snowflake) and join with asset inventories to calculate cost per service and cost per owner.

4) Integration map: graph the edges

Count integrations per platform (webhooks, API keys, connectors). An app with many inbound/outbound integrations is high-impact; an app with many connectors and low usage is a maintenance liability.

# Example schema for integrations
-- integrations(owner, source_app, dest_app, connector_type, created_at)
SELECT source_app, COUNT(*) AS outbound_connectors
FROM integrations
GROUP BY source_app
ORDER BY outbound_connectors DESC;

Use integration maps together with ticketing data to prioritize the highest-maintenance products.

Step 2 — Metrics that expose underused platforms and hidden TCO

Inventory gives you items; metrics turn items into decisions. Below are high signal metrics, how to compute them, and recommended thresholds to flag candidates for consolidation or retirement.

Key metrics

1. Active user rate = MAU / seats provisioned

   Threshold: < 10% -> candidate for retention review; < 5% -> candidate for retirement.

2. Cost per active user (CPU) = monthly cost / MAU

   Helps compare tools with different pricing models. High CPU suggests inefficiency.

3. Integration complexity = number of inbound + outbound connectors

   High complexity increases operational risk. Combine with usage to compute maintenance risk.

4. Shadow maintenance estimate = (avg weekly engineering hours for incidents + integration upkeep) * hourly rate * 4

   Estimate using ticketing systems (Jira labels like "tool: X") and on-call rotations.

5. Security & compliance score (0–100) based on SSO lifecycle, MFA enforcement, data residency, and open IAM roles

   Any unmanaged app (no SSO) should get a low score; low-score products carry remediation cost.

6. Overlap score = similarity to other tools (functional overlap percent via taxonomy)

   Tools that duplicate 50%+ features with a mainstream platform are consolidation opportunities.

Sample SQL: CPU and active user rate from billing and SSO

-- Join your SaaS invoices and SSO usage tables
SELECT
  s.vendor,
  s.monthly_spend,
  u.mau,
  SAFE_DIVIDE(s.monthly_spend, u.mau) AS cost_per_active_user,
  SAFE_DIVIDE(u.mau, u.provisioned_seats) AS active_user_rate
FROM `org_billing.saas_monthly` s
JOIN `org_identity.sso_app_usage` u
  ON s.vendor = u.app_label
WHERE s.month = DATE_TRUNC(CURRENT_DATE(), MONTH)
ORDER BY cost_per_active_user DESC;

Step 3 — Decision framework: consolidate, retire, or re-platform

Use a reproducible scoring model to reduce emotion in decisions. The model below weights financial, operational, and security signals. Tune weights to your org’s priorities.

Scoring model (example)

• Financial signal (weight 35%): normalized monthly cost + trend (last 12 months)
• Usage signal (weight 30%): active user rate, feature adoption
• Operational signal (weight 20%): integration complexity, incident count
• Security signal (weight 15%): SSO presence, MFA, data classification

Score range 0–100. Map scores to outcomes:

• 0–30: Retire — low usage, manageable cost, low strategic value
• 31–60: Consolidate — medium usage, partial overlap or duplicative capabilities
• 61–100: Invest / Re-platform — high usage or strategic value; consider cost negotiation or optimization

Pseudocode: compute recommendation

# Pseudocode for scoring
financial = normalize(monthly_cost) * 0.35
usage = normalize(active_user_rate_inverse) * 0.30  # inverse: low usage => high score for retirement
operational = normalize(integration_count + incident_count) * 0.20
security = normalize(security_risk) * 0.15
score = financial + usage + operational + security
if score <= 30:
    decision = 'RETIRE'
elif score <= 60:
    decision = 'CONSOLIDATE'
else:
    decision = 'INVEST or RE-PLATFORM'

Normalize() is min-max scaling across inventory. Document and automate the score so every platform has a repeatable decision history in your platform catalog and platform repo.

Step 4 — Prioritize with an Impact vs Effort matrix

After each product gets a recommendation, prioritize with two axes:

• Impact: monthly cost reduction + reduced maintenance effort + security improvement
• Effort: migration complexity, data export/import, stakeholder change management

Prioritize low-effort, high-impact retirements as quick wins. Put complex migrations into a backlog with milestones and guarded pilot phases.

Playbook: safe retirement and consolidation

Retiring a platform often triggers the most fear. Use a playbook to remove operational friction and reduce rollback risk.

Retirement checklist (minimum viable)

1. Stakeholder alignment: owners, legal, security, and top users
2. Export data & verify integrity — create final, immutable snapshots
3. Notify users and close the window for objections (30–90 days depending on impact)
4. Cut write access, keep read-only for a defined archival period
5. Redirect integrations: create adapters or rewire consumers to replacement platforms
6. Update runbooks, service catalog, and internal docs
7. Shut down automated provisioning and billing; cancel subscriptions on last day of archival window
8. Post-mortem after 30/90 days and verify cost reduction

Automation snippets for retirement

# Example: disable SCIM provisioning in Okta for an app (curl)
curl -X POST "https://your-org.okta.com/api/v1/apps/${APP_ID}/lifecycle/deactivate" \
  -H "Authorization: SSWS ${OKTA_API_TOKEN}" -H "Accept: application/json"

# Example: revoke API keys programmatically
curl -X DELETE "https://api.example-saas.com/v1/keys/${KEY_ID}" -H "Authorization: Bearer ${ADMIN_TOKEN}"

Automate these steps in your platform repo to run exactly the same way each time. Capture every action in an audit log and fold lessons back into an automation playbook or micro-app that operators can reuse.

Case study snapshots (anonymized)

These short examples show the diagnostic in practice.

Case A — Engineering org with 220 tools

Inventory revealed 220 unique tools. Scoring flagged 27 apps with MAU < 5% and monthly SaaS spend of $42k. After prioritizing low-effort retirements, the team retired 12 apps in 3 months, saving $18k/month and removing 36 integration points. Engineering incidents related to connectors dropped 22%.

Case B — Platform team with duplicated observability

Two observability vendors coexisted. CPU analysis showed one had very high cost per active user but was used by a single team. Consolidation to the org-standard product and negotiated contract renewal reduced spend 35% and simplified on-call.

Advanced strategies & future-proofing (2026 focus)

As we move deeper into 2026, several trends change how you treat tool sprawl:

• AI Ops accelerates discovery: AI-driven observability and SSO analytics can auto-classify app functionality and propose consolidation candidates.
• FinOps + SaaS Management integration: By early 2026, many organizations integrated FinOps tooling directly with SaaS management platforms to get real-time CPU and ASG (active seat governance).
• Platform catalogs become the single source of truth: Embedded service catalogs with lifecycle automation let platform teams enforce provisioning and prevent shadow tools. See our notes on platform catalogs.
• Security-first procurement: Regulators and enterprise risk teams increasingly require SSO integration, data classification, and supply-chain attestations before purchase — making discovery easier.

To future-proof your platform strategy, invest in:

• Automated onboarding pipelines that include cost and security gates
• A canonical platform catalog with clear owners and lifecycle states (approved, deprecated, retired)
• Tagging and telemetry standards enforced at provisioning time — don’t let tags be optional; bake them into your IaC and runbooks (cloud-native guidance helps)
• Periodic diagnostic automation — run this kit quarterly

Common objections and how to rebut them

• “But teams love that tool.” — Measure love: if usage is localized, consider targeted migration or specialized retention with funded ownership.
• “Data migration is risky.” — Use immutable exports and run dual-write during migration windows. Start with low-risk pilots.
• “We negotiated a contract.” — Contract obligation doesn’t mean impossible to retire. Negotiate early-exit credits, migration windows, or seat reductions when possible.

Actionable takeaways — run this diagnostic in two weeks

1. Week 1: Pull SSO logs, billing exports, and cloud billing to a warehouse; run the supplied SQL queries to compute MAU and CPU.
2. Week 2: Compute scores for your inventory, map quick wins (low effort, high impact), and schedule retirement for 2–3 pilot apps.
3. Quarterly: Automate the score and publish a platform catalog update to all stakeholders.

“If it isn’t measured, it isn’t managed.” — Use identity, billing, and integration graphs as your three pillars for measurable platform decisions.

Final checklist before you act

• Do you have a canonical owner and runbook for every app?
• Are SSO and billing signals joined in a single place for analysis?
• Is there a repeatable scoring model in your platform repo?
• Do you have a communications and rollback plan for retirement?

Next steps — call to action

Tool sprawl is solvable with repeatable measurement and low-friction automation. Start by running the scripts in this kit against your SSO and billing exports. If you want an opinionated template, export your top 50 vendors (SSO + billing) and map the scores — share that CSV with your platform leadership and run a 90-day pilot to retire or consolidate the top 5 candidates.

Ready to get hands-on? Download the companion scripts, SQL queries, and a ready-made scoring workbook from deployed.cloud/kit or request a short audit for your top 50 tools. Implement the diagnostic, show measurable savings in 90 days, and make platform sprawl a thing of the past.