Cloud Cost Estimator
Estimate cloud infrastructure costs across AWS, GCP, and Azure with savings analysis. Enter values for instant results with step-by-step formulas.
Formula
Total Cost = (Compute Γ Hours Γ Instances) + (Storage Γ GB) + (Transfer Γ GB) + (Database Γ GB)
Cloud costs are calculated by summing compute (instance hourly rate Γ hours Γ count), storage (per GB/month), data transfer (egress per GB), and database services. Regional multipliers and commitment discounts (reserved/spot) modify the base rates.
Worked Examples
Example 1: Startup SaaS Application
Problem: A startup runs 4 medium instances 24/7, 200GB storage, 50GB monthly egress, and 20GB database on AWS US-East. Calculate monthly costs and potential savings.
Solution: On-Demand Calculation:\nCompute: 4 Γ $0.0464/hr Γ 720 hrs = $133.63\nStorage: 200 GB Γ $0.023 = $4.60\nData Transfer: 50 GB Γ $0.09 = $4.50\nDatabase: 20 GB Γ $0.115 = $2.30\nTotal On-Demand: $145.03/month\n\nReserved Instance (1-year, 40% savings):\nCompute: $133.63 Γ 0.60 = $80.18\nOther costs unchanged: $11.40\nTotal Reserved: $91.58/month\n\nAnnual Comparison:\nOn-Demand: $145.03 Γ 12 = $1,740.36\nReserved: $91.58 Γ 12 = $1,098.96\nSavings: $641.40/year (37%)
Result: $145/mo on-demand | $92/mo reserved | Save $641/year with commitment
Example 2: Data Analytics Platform
Problem: A data team needs 8 large instances for processing (4 hours/day), 2TB storage, 500GB monthly egress, and 100GB database on GCP.
Solution: Usage Hours: 4 hrs Γ 30 days = 120 hrs/month\n\nOn-Demand Calculation:\nCompute: 8 Γ $0.0832/hr Γ 120 hrs = $79.87\nStorage: 2000 GB Γ $0.020 = $40.00\nData Transfer: 500 GB Γ $0.12 = $60.00\nDatabase: 100 GB Γ $0.17 = $17.00\nTotal: $196.87/month\n\nPreemptible Option (80% compute discount):\nCompute: $79.87 Γ 0.20 = $15.97\nOther costs unchanged: $117.00\nTotal Preemptible: $132.97/month\n\nSavings with Preemptible: $63.90/month (32%)\n\nNote: Data transfer is significant portion (30%)
Result: $197/mo standard | $133/mo preemptible | Data transfer dominates costs
Example 3: Enterprise Multi-Region Deployment
Problem: An enterprise runs 20 xlarge instances across 3 regions, 5TB storage, 2TB monthly egress, and 500GB database on Azure.
Solution: Average Regional Multiplier: 1.12\n\nCompute (20 instances Γ 720 hrs):\n20 Γ $0.192 Γ 720 Γ 1.12 = $3,096.58\n\nStorage: 5000 GB Γ $0.024 Γ 1.12 = $134.40\n\nData Transfer: 2000 GB Γ $0.087 Γ 1.12 = $194.88\n\nDatabase: 500 GB Γ $0.125 Γ 1.12 = $70.00\n\nTotal On-Demand: $3,495.86/month\nAnnual: $41,950.32\n\nWith 3-Year Reserved (60% on compute):\nCompute: $3,096.58 Γ 0.40 = $1,238.63\nOther: $399.28\nTotal: $1,637.91/month\nAnnual: $19,654.92\n\nSavings: $22,295.40/year (53%)
Result: $3,496/mo on-demand | $1,638/mo reserved | Save $22K/year with 3-year
Frequently Asked Questions
How do cloud providers charge for compute resources?
Cloud compute pricing is based on: Instance type (vCPUs, memory, GPU) with hundreds of options. Usage model - On-demand (pay by the second/hour), Reserved (1-3 year commitment for 30-70% savings), Spot/Preemptible (up to 90% savings but can be interrupted). Region affects pricing significantly (US generally cheapest). Additional factors include operating system (Windows costs more), dedicated vs shared tenancy, and sustained use discounts. AWS, GCP, and Azure all offer free tiers and credits for new customers.
How is cloud storage priced?
Cloud storage pricing includes: Capacity charges per GB/month (typically $0.02-0.025 for standard storage). Storage classes (hot/warm/cold/archive) with different access costs. Request charges for PUT, GET, LIST operations. Data retrieval fees for archive storage. Redundancy options (single zone vs multi-region). Lifecycle management can automatically move data to cheaper tiers. Object storage (S3, GCS, Blob) is cheapest for large files. Block storage (EBS, Persistent Disks) costs more but provides better IOPS.
What are the hidden costs in cloud computing?
Common hidden costs include: Data egress (often underestimated). Load balancer hours and data processed. NAT gateway data processing ($0.045/GB on AWS). Elastic IPs when not attached to instances. Snapshot storage accumulation. CloudWatch/Monitoring logs. Premium support plans (3-10% of spend). Cross-AZ data transfer. IP address charges (AWS now charges for public IPv4). Idle resources (development environments left running). Always review detailed bills and use cost allocation tags.
How do I estimate costs for a new cloud project?
Estimation approach: 1) Define workload requirements (compute, memory, storage, network). 2) Use provider pricing calculators (AWS Calculator, GCP Calculator, Azure Calculator). 3) Add 20-30% buffer for unexpected costs. 4) Account for data transfer patterns. 5) Plan for growth (month-over-month scaling). 6) Consider managed services vs self-managed trade-offs. 7) Factor in operations/monitoring costs. Start with on-demand, measure actual usage for 3 months, then optimize with reserved/spot instances.
Which cloud provider is cheapest?
It depends on workload: Compute-heavy: GCP often cheapest due to sustained use discounts and preemptible pricing. Storage-heavy: All similar for standard storage; GCP Nearline/Coldline competitive for archival. Data-intensive: Consider egress carefully; can dominate costs. AI/ML: GCP TPUs competitive; AWS SageMaker comprehensive; Azure has strong enterprise integration. Serverless: Pricing models differ significantly (request-based vs duration-based). Best approach: Use each provider's free tier, benchmark your actual workload, and consider multi-cloud for optimization.
How do managed services affect cloud costs?
Managed services trade cost for convenience: Managed databases (RDS, Cloud SQL): 20-40% premium over self-managed but includes backups, patching, HA. Managed Kubernetes (EKS, GKE, AKS): $0.10/hour for control plane plus node costs. Serverless (Lambda, Cloud Functions): Can be cheaper for sporadic workloads, expensive for continuous. Managed caching (ElastiCache, Memorystore): Premium over self-managed Redis/Memcached. Consider total cost of ownership: managed services reduce ops overhead, fewer late-night pages, but less control and potentially higher raw costs.