System Cost Estimator
What will this architecture cost at its real operating envelope?
A useful system cost estimate starts with workload and reliability requirements, derives the resources they need, then applies explicit unit-price assumptions. The number is only meaningful when its formulas and uncertainty remain visible.
Step 1
Model
Set traffic, measured instance capacity, storage growth, replication, egress, utilization, and failure reserve. Then edit every illustrative price, regional factor, commitment assumption, and uncertainty range.
Step 2
Observe
See the scenario-ready fleet, durable data footprint, transfer volume, monthly cost range, exact cost drivers, and the reliability trade-off behind each optimization.
Step 3
Challenge
Start with planned demand, then inject a traffic spike, replication growth, egress shock, and zone failure. Identify which challenge changes architecture, which changes only the bill, and where the current fleet loses reserve.
Architecture economics lab
Price the operating envelope
Size the resources first, then apply editable unit economics. Challenge the plan to see which cost and reliability assumptions break.
Challenge the healthy plan
Each scenario changes a different architecture or pricing driver.
Planned demand: Price the architecture at the planned peak with the selected failure reserve intact.
Workload shapes the architecture
Demand, storage growth, and reserve targets determine the resources the design must carry.
Assumptions shape unit economics
Edit every price. These are neutral teaching assumptions, not a provider quote.
Illustrative USD assumptions · revised July 25, 2026
Excludes taxes, support contracts, free tiers, migration labor, and negotiated pricing.
Region cost profile
Scenario-ready monthly run rate
$3,188
$38,254 annualized at month 12
Failure reserve intact
3 instances remain above the serving requirement at the planned peak.
Confidence envelope
±15% around the modeled run rate
$2,710–$3,666
Architecture consequence
The selected challenge flows through capacity, data copies, and transfer.
1 · Peak demand
3.8K req/s
1.0× planned demand
2 · Compute fleet
13 instances
Existing plan is sufficient
3 · Durable state
27.2K GB
3 copies at month 12
4 · Outbound path
12K GB/mo
1.0× transfer assumption
Current surviving capacity
13 instances · 5.5K req/s
Challenge utilization
69.9% of safe serving capacity
Monthly cost drivers
Every bar exposes the units and formula behind the estimate.
Compute fleet
13 instances × 730 h × $0.14/h × 1.00 region factor − $186 commitment savings
$1,143
35.8%
Replicated storage
27.2K GB-copies × $0.025/GB-month × 1.00
$679
21.3%
Outbound transfer
12K GB × $0.065/GB × 1.00
$780
24.5%
Managed platform
$350 editable base assumption × 1.00
$350
11%
Operations and telemetry
8% × $2,952 infrastructure subtotal
$236
7.4%
Decision trade-offs
Optimization changes risk ownership, not just the bill.
Commit stable compute
Saves $201/month in this model, but committed capacity remains payable when demand falls.
Keep durability explicit
Extra copies cost $453/month. Reducing them lowers storage cost while narrowing failure tolerance.
Protect latency headroom
The 65% target and 25% reserve drive 13 instances. Raising utilization reduces cost but increases queueing risk.
Attack the largest driver first
Compute fleet is currently largest at $1,143/month. Optimize its unit or volume without hiding the architecture consequence.
Model boundary
This estimate is a transparent comparison model, not a quote. Validate sustained throughput, billing granularity, data transfer paths, discounts, and operational labor against your actual architecture before making a commitment.
Scenario
Planned demand