- Desktop GIS costs extend far beyond licensing: analyst time, hardware, single-point-of-failure risk, and scaling constraints
- Migration typically costs 1.5-3x first-year vs staying—break-even at 18-36 months depending on workflow volume
- Best candidates: high-volume recurring workflows (12+ runs/year), multi-analyst teams, cloud data sources
- Stay on ArcPy if: low volume, single analyst, heavy Esri ecosystem integration, or workflows require constant change
Your team has run ArcPy scripts for years. They work. The analysts know them. Why would you invest in migration when you have real deliverables to ship?
It's a fair question. Migration costs real money and time. The benefits are often oversold by vendors with products to move. This post gives you a framework for deciding whether migration makes sense for your specific situation—and helps you make the case (or argue against it) with actual numbers.
The True Cost of Desktop GIS
The licensing invoice is the visible cost. The invisible costs are larger:
1. Analyst Time on Repetitive Execution
Every time a workflow runs, an analyst is tied to the machine. Open ArcGIS Pro. Navigate to the script. Set parameters. Click Run. Wait. Verify. Export. A 4-hour script ties up a $75/hour analyst for 4 hours. That's $300 in labour per execution—often for tasks that could run unattended overnight.
8 runs/month × $300/run × 12 months = $28,800/year
Just in attended execution time
2. Hardware and Desktop Dependency
Desktop GIS requires capable workstations. ArcGIS Pro's system requirements push toward high-end machines. Each analyst needs their own licensed workstation. Hardware refresh cycles every 3-4 years add up.
3 workstations × $3,000 × refresh every 4 years = $2,250/year
3. Single Point of Failure
What happens when the senior analyst who wrote the scripts leaves? Or goes on holiday during a critical deadline? Desktop scripts create key-person dependencies that don't show up in budget spreadsheets until they become crises.
Business continuity risk is hard to quantify but very real. One major project slip due to key-person absence can exceed annual migration costs.
4. Scaling Constraints
Desktop GIS scales linearly with headcount. Need to process 5× more data? Hire 5× more analysts. Buy 5× more licenses. The alternative—making existing analysts work nights and weekends—isn't sustainable.
Total Cost Example
A 3-analyst team running ArcPy scripts might spend $15K on licenses, but $50K+ on attended execution time, hardware, and scaling constraints. The licensing invoice is 23% of the true cost.
Migration Cost Reality Check
Vendors undersell migration costs. Here's what actually happens:
| Cost Category | One-Time | Annual | Notes |
|---|---|---|---|
| Workflow audit and design | $15-25K | — | Document current state |
| Code translation | $30-60K | — | Per major workflow |
| Platform setup | $10-20K | — | Cloud infrastructure |
| Team training | $8-15K | — | Python, cloud patterns |
| Cloud compute | — | $3-12K | Depends on volume |
| Maintenance (15-20%) | — | $8-15K | Updates, debugging |
| Typical Total | $63-120K | $11-27K | Varies by scope |
Year 1 almost always costs more than staying on ArcPy. This is the honest truth that vendors don't emphasise. Migration is a capital investment that pays back over time—not instant savings.
BREAK-EVEN CALCULATION
Current annual cost: $65K (licenses + attended time + hardware)
Migration investment: $90K one-time + $18K/year
Post-migration annual cost: $18K (cloud + maintenance)
Annual savings: $65K - $18K = $47K
Break-even: $90K ÷ $47K = 1.9 years
ROI Calculation Framework
Use this framework to calculate ROI for your specific situation:
Step 1: Calculate Current Annual Cost
- • ArcGIS licenses (all tiers, all users)
- • Analyst time on workflow execution (hours × rate)
- • Hardware costs (workstations, annualised)
- • Maintenance and support contracts
Step 2: Estimate Migration Investment
- • Audit and design: $15-25K
- • Translation: $30-60K per major workflow
- • Infrastructure: $10-20K
- • Training: $8-15K
Step 3: Estimate Post-Migration Annual Cost
- • Cloud compute (based on projected usage)
- • Maintenance (15-20% of build cost)
- • Remaining analyst time (now exception-handling only)
Step 4: Calculate Break-Even
Break-even = Migration Investment ÷ (Current Annual - Post-Migration Annual)
If break-even exceeds 3 years, reconsider. Technology changes. Team priorities shift. Long payback periods carry execution risk.
When NOT to Migrate
Migration isn't always the right answer. Here's when staying on ArcPy makes more sense:
Low-Volume Workflows
If a workflow runs 4 times per year and takes 2 hours each time, that's 8 hours of analyst time—$600 annually. No migration ROI justifies a $60K investment to save $600.
Single-Analyst Operations
If one analyst handles all geospatial work and has capacity to spare, the "scaling constraint" argument doesn't apply. Migration adds complexity without solving a real problem.
Deep Esri Ecosystem Integration
If workflows depend on ArcGIS Enterprise, Portal, Web Maps, and the full Esri stack, migrating the Python code doesn't eliminate the dependency. You'd need to migrate the entire ecosystem.
Constantly Changing Requirements
If every workflow execution requires tweaking the logic, automation doesn't help. You'd be constantly updating cloud pipelines instead of desktop scripts. Same work, different platform.
Team Resistance
If the GIS team is hostile to the change and leadership won't invest in proper training, the migration will fail. Technical success requires human adoption. Factor in change management costs honestly.
Decision Criteria
Migration makes sense when multiple criteria align:
The Decision Matrix
4-5 criteria met: Strong candidate. Proceed with detailed assessment.
2-3 criteria met: Possible candidate. Pilot one workflow first.
0-1 criteria met: Not a good fit. Optimise existing ArcPy instead.
Migration is an investment, not a quick win. Year 1 costs more than staying. Break-even takes 18-36 months. The ROI is real—but only if your workflows meet the criteria.
The honest answer is that some teams should stay on ArcPy. Low-volume workflows, single-analyst operations, deep Esri ecosystem dependencies—these are legitimate reasons to keep doing what works.
But if you're running high-volume workflows, scaling is constrained, and your data lives in the cloud, migration unlocks capacity that desktop GIS structurally cannot provide.
In Part 2, we'll cover the technical translation: which ArcPy functions map to which open-source equivalents, where GeoPandas falls short, and when you need hybrid architectures.
Workflow-Automatisierung Einblicke erhalten
Monatliche Tipps zur Automatisierung von GIS-Workflows, Open-Source-Tools und Erkenntnisse aus Enterprise-Deployments. Kein Spam.