How to Audit Your Tool Stack with Observability: A Practical Checklist for Teams

Start here: your tool stack is costing more than you think

Too many SaaS subscriptions, duplicated integrations, and blind spots in usage are slowing teams and inflating bills. If your developers, infra, and finance teams are arguing about which tool to keep — or no one knows who’s using what — it's time for an observability-driven tool audit. This guide gives you a practical checklist, queries, and scripts to turn logs, metrics, billing exports, and developer surveys into a defensible consolidation plan.

Why an observability-first audit matters in 2026

Late 2025 and early 2026 saw two clear trends: (1) organizations accelerated SaaS adoption and (2) the cost and complexity of maintaining many specialized platforms rose faster than the productivity gains. Observability platforms became more standardized, and teams began to treat telemetry as the single source of truth for decisions spanning reliability, developer experience, and cost.

Observability-driven audits combine telemetry (metrics, traces, logs) with billing and usage analytics to show which tools are actually producing value — not just generating invoices. The result: faster consolidation decisions, higher developer productivity, and measurable ROI.

What this checklist will help you answer

• Which tools are actively used vs. dormant?
• Where features overlap across tools (and cause fragmentation)?
• Which tools produce measurable developer productivity gains or reliability improvements?
• How much can you save by consolidating? What’s the ROI and risk?

High-level audit workflow (inverted pyramid)

1. Inventory all tools and subscriptions.
2. Instrument missing telemetry and collect usage events.
3. Analyze usage, overlap, and performance from logs/metrics/billing.
4. Survey developers to capture qualitative value.
5. Decide & pilot — consolidate where ROI and risk profiles align.
6. Measure the pilot and iterate.

Checklist: concrete steps, tools, and scripts

Step 1 — Inventory every tool and integration

Start broad. Collect vendor names, subscription tiers, owners, billing lines, and integrations. Use your cloud billing export, HR/SSO (SCIM) provider, and IAM logs to find active identities and integrations.

• Export vendor invoices and billing CSVs for the last 12 months.
• Query your SSO (e.g., Okta, Azure AD) for apps with active assignments.
• Pull installed integrations from GitHub, GitLab, and CI/CD providers.

# Example: list SSO apps via Okta API (bash)
OKTA_DOMAIN=your-org.okta.com
API_TOKEN=xxxx
curl -s -H "Authorization: SSWS $API_TOKEN" \
  "https://$OKTA_DOMAIN/api/v1/apps" | jq '.[] | {label,id,signOnMode}'
  

Step 2 — Ensure telemetry coverage

You can’t measure what you don’t collect. For each tool, identify telemetry sources and gaps:

• Does the SaaS offer usage APIs or audit logs?
• Are integrations instrumented to emit events (e.g., webhooks, audit logs) into your observability pipeline?
• Do you have tenant-level or org-level usage metrics (e.g., active users, API calls)?

If a provider doesn’t expose usage data, plan an access or alternative measurement (e.g., proxy API calls through an API gateway where you can count usage).

Step 3 — Gather billing data (examples)

Aggregate cost by vendor, project, and service. Here are quick examples for common clouds.

# AWS Cost Explorer (CLI) - monthly cost by service
aws ce get-cost-and-usage --time-period Start=2025-12-01,End=2026-01-01 \
  --granularity MONTHLY --metrics "UnblendedCost" \
  --group-by Type=DIMENSION,Key=SERVICE

# GCP BigQuery sample (billing export table)
-- Replace `project.billing_export.gcp_billing_export_v1`
SELECT service.description AS service, sku.description AS sku, 
       SUM(cost) AS cost
FROM `project.billing_export.gcp_billing_export_v1`
WHERE usage_start_time BETWEEN '2025-12-01' AND '2026-01-01'
GROUP BY service, sku
ORDER BY cost DESC
  

Step 4 — Quantify active usage

Define an active user for each tool (login, API call, event). Aggregate monthly active users (MAU) or weekly active users (WAU) per tool and calculate cost per active user.

# Python: cost per active user from billing CSV and usage CSV (pandas)
import pandas as pd
billing = pd.read_csv('billing.csv')  # columns: vendor,cost,month
usage = pd.read_csv('usage.csv')      # columns: vendor,active_users,month
merged = billing.merge(usage, on=['vendor','month'])
merged['cost_per_active'] = merged['cost'] / merged['active_users']
print(merged.groupby('vendor')['cost_per_active'].mean().sort_values())
  

Interpretation: high cost_per_active is a signal to investigate (high cost, low usage, or both).

Step 5 — Detect feature overlap using telemetry and integrations

Create a feature matrix (rows: features; columns: tools). Populate with telemetry-driven evidence:

• Which tools generate the same event types (e.g., incident created, ticket created)?
• Count duplicated integrations (e.g., two bug trackers connected to same repo).

# PromQL: count HTTP requests per tool label (service label provided by sidecar)
sum(rate(http_requests_total{job=~"tool-.*"}[5m])) by (job)
  

Use traces to see where workflows cross tools. A single developer flow that hits three different tools indicates potential consolidation value.

Step 6 — Measure reliability and efficiency signals

Not all value is monetary. For each tool, measure operational impact:

• Alerts/noise: alert volume and false-positive rate
• Incident correlation: time to correlate and remediate when a tool is involved
• Onboarding time: average time to grant access and complete initial setup

# Loki / Grafana log query: error counts per tool
{app=~"tool-.*"} |= "ERROR" | json | count_over_time({app=~"tool-.*"}[1d]) by (app)

# Example: Splunk search for incident creation sources
index=incidents sourcetype=incident_logs | stats count by source_tool
  

Step 7 — Run developer and stakeholder surveys

Telemetry is objective; sound is subjective. Use short surveys to capture perceived value and friction. Score and combine with telemetry.

# Example survey template (keep it 5 questions, 1-5 scale)
1) How often do you use TOOL_NAME? (Never-Always)
2) How much does TOOL_NAME increase your productivity? (1-5)
3) How often does TOOL_NAME cause friction (broken integrations, confusion)? (1-5)
4) How easy was onboarding to TOOL_NAME? (1-5)
5) Would you support replacing TOOL_NAME with an alternative? (Yes/No)
  

Score interpretation: combine mean productivity score with active usage and cost_per_active to rank tools.

Step 8 — Build a consolidated decision model

Create a decision matrix with weighted criteria. Example weights (adjust for org goals):

• Direct cost (30%)
• Active usage / productivity (25%)
• Feature uniqueness (20%)
• Operational overhead (15%)
• Strategic alignment / vendor risk (10%)

# Pseudocode to compute score per tool
score = 0.3 * normalized_cost + 0.25 * normalized_productivity \
      + 0.2 * uniqueness + 0.15 * operational_overhead + 0.1 * strategic_risk
# Lower score = better candidate for elimination (if normalized_cost high and productivity low)
  

Step 9 — Pilot consolidation and measure improvement

Run a small pilot that consolidates one redundant capability. Instrument before/after and define success criteria:

• Cost delta over 90 days
• Change in mean time to onboard a new developer (days)
• Change in incident correlation time (hours)
• Developer satisfaction delta from follow-up survey

Example SLA: reduce duplicated alerts by 50% and save 30% of monthly spend on alerting tools within 90 days.

Step 10 — Governance, contracts, and renewal strategy

Audit contract terms and renewal windows. Use observability windows as negotiation leverage:

• Delay renewal until you complete the pilot
• Ask for short-term flexibility or feature toggles during migration
• Negotiate credits for unused seats uncovered by telemetry

Practical scripts and queries cheat sheet

Below are focused examples you can copy-paste into your environment. Replace placeholders.

Aggregate SaaS seats and active users (bash + jq)

# Example: combine seat CSV and idp assignments to find inactive seats
# seats.csv columns: vendor,seat_count
# idp_apps.json from Okta shows app assignments per user
jq -r '.[] | {label,id}' idp_apps.json > okta_apps.json
python3 analyze_seats.py seats.csv okta_apps.json
  

BigQuery: find top 10 most expensive SKUs (GCP billing export)

SELECT service.description AS service, sku.description AS sku, SUM(cost) AS cost
FROM `project.billing_export.gcp_billing_export_v1`
WHERE usage_start_time >= '2025-01-01'
GROUP BY service, sku
ORDER BY cost DESC
LIMIT 10;
  

AWS: cost by linked account and tag (Cost Explorer / CUR)

# If you have CUR exported to S3, use Athena or Redshift to query tags
# Example Athena SQL (CUR composite table)
SELECT line_item_usage_account_id, resource_tags_user_project AS project,
       SUM(line_item_blended_cost) AS cost
FROM aws_billing_cur
WHERE line_item_usage_start_date BETWEEN date '2025-12-01' AND date '2026-01-01'
GROUP BY line_item_usage_account_id, resource_tags_user_project
ORDER BY cost DESC
LIMIT 50;
  

PromQL: detect tool-generated error spikes

# Error rate per tool over last 24h
sum(increase(http_request_errors_total{job=~"tool-.*"}[24h])) by (job)
  

Loki/Grafana: top services by 'integration failed' logs

{app=~"tool-.*"} |= "integration failed" | json | count_over_time({app=~"tool-.*"}[7d]) by (app)
  

Elasticsearch: count duplicate integrations per repo

GET /logs-*/_search
{
  "size": 0,
  "aggs": {
    "by_repo": { "terms": { "field": "repo.keyword", "size": 1000 },
      "aggs": { "by_tool": { "terms": { "field": "tool.keyword", "size": 10 } } }
    }
  }
}
  

How to compute ROI quickly

ROI on consolidation is a combo of direct cost savings and indirect operational savings. A simple model:

# Variables:
monthly_cost_removed = current_tool_monthly_cost
migration_cost = one_time_migration_cost
monthly_op_ex_gain = estimated monthly savings from reduced overhead
# ROI (months to recover) = migration_cost / (monthly_cost_removed + monthly_op_ex_gain)
  

Example: $5,000/mo removed + $2,000/mo ops savings, migration cost $24,000 => recover in 24k / (5k+2k) = ~3.4 months.

Common pitfalls and how observability avoids them

• Pitfall:
• Pitfall:
• Pitfall:

Observability turns subjective tool debates into objective, repeatable decisions: cost + usage + reliability = clarity.

Case study snapshot (anonymized, 2025–2026)

A mid-size platform team audited 75 SaaS subscriptions. Using the checklist above they found:

• 20% were inactive (unused seats) — reclaimed within 30 days.
• 3 tools had overlapping CI pipeline features; consolidating reduced pipeline complexity and cut incident correlation time by 40%.
• Measured ROI: migration cost recouped in 3 months; recurring savings 22% of the prior SaaS budget.

Key to success: instrumentation of audit logs into a centralized observability platform and a developer survey aligned to telemetry metrics.

Advanced strategies and future-looking tips for 2026+

• Use behavioral telemetry: workflow traces that show how many tools a single task touches — prioritize consolidation where workflows touch multiple tools often.
• Leverage AI summarization:
• Adopt FinOps + DevEx collaboration:
• Automate seat reclamation:

Actionable takeaways — what to run this week

1. Export last 12 months of invoices and run the cost_per_active Python script above.
2. Query your SSO for assigned apps; remove orphaned assignments older than 90 days (soft-disable first).
3. Enable or centralize audit logs from your top 10 highest-cost SaaS tools into your observability platform.
4. Send a 5-question developer survey to active engineering teams and correlate results to telemetry.
5. Pick one low-risk consolidation candidate and run a 90-day pilot with clear telemetry-based KPIs.

Wrap-up: make tool audits repeatable and data-driven

By combining logs, metrics, billing exports, and developer surveys you replace opinions with evidence. Use the scripts and queries above to accelerate the audit, then institutionalize the process: quarterly reviews, seat automation, and a cross-functional decision matrix. That’s how teams cut costs without sacrificing developer productivity or reliability.

Next steps — a clear call-to-action

Ready to run your first observability-driven tool audit? Start with one high-cost, low-usage vendor from your billing report this month. If you want a jumpstart, download our audit worksheet (CSV + BigQuery templates) and the ready-to-run scripts referenced above. Measure baseline telemetry, run a 90-day pilot, and report results to your FinOps+DevEx guild.

Take action now: pick one vendor, collect its last 12 months of invoices and audit logs, and run the cost-per-active-user script. If you’d like, share your anonymized outputs (we’ll help interpret them and propose a consolidation plan).

Related Reading

• How Coinbase Sidelined a Senate Vote: Inside the Company’s Washington Playbook
• Maximize Black Ops 7 Double XP Weekend: An Esports Player's Grind Plan
• Media Industry Shakeups and Worker Wellbeing: Substance Use Risks During Layoffs and Reorganizations
• Packaging That Sells: Designing Gift-Ready Kits for Winter Makers
• How to Migrate Your Club Forum Off Reddit: Pros and Cons of Digg, Bluesky and New Platforms