Azure pricing is more simple than Amazon

• 10c/GB in and 15c/GB out, regardless of service
• Data withing data center is unmetered
• Between data centers is at full rate

Compute

• 12c/h deployed instances (ready state+)
• Same charge regardless of
• Instance is actually running
• In staging or production

Storage

• 15c/GB/m
• 1c per 10,000 REST Calls
• CDN charges (TBD)
• Storage is pro-rated by average daily amount
• Blocks cost money even if not PUT (don't orphan blocks)
  
• Overhead can be expensive in tables
• Entity pairs, name / values, names also take space
  

Storage

• 1c / 10000 REST calls
• Appears inconsequential, but hand add up
• Using a queue is at least 3 transactions (enqueue, deque, delete)
• Batch transactions to reduce cost

SQL Azure

• $9.99 / m for 1GB
• $99.99 / m for 10GB

SQL Azure - Instance Based

• Prorated by time (midnight UTC)
  
• CPU like other roles
• No transaction charge, but beware of throttle

SQL Azure - Accounting

• Included in cac: shema/objects
• ... missed rest too fast

SQL Azure - Determining Usage

• sys.bandwidth_usage, ...

Session State

• Cheaper in SQL Azure than in tables due to transactions

Pricing Tips

• Bandwidth: compress response, minify/optimize (aptimize.com)
• Reduce resource size: compress data in blog storage
• User affinity groups to geolocate services

Compute

• Add or remove instances as required
• start same number of instances in staging
• warm up instances
• swap deployments
• more (went too fast)

Storage

• Batch requests
• Model usage
• Compress blog data
• Set content-encoding when putting blob
• use caching to reduce request transaction count
• use CSS sprites and Data URIs to reduce transaction count

Where do static resources live:

• In web role?  incurs additional free but resource consuming requests
• Blob: storage cost and transaction cost

Use CSS sprites

• Can massively reduce round-trips and transaction counts in Azure

SQL Azure

• Vertically partition out large data columns
• Support dynamic partitioning if possible
• Consider just-in-time partitioning
• Pull archive data out of the cloud to cheaper on premise storage or to azure storage
• Manipulate db from cloud using System.Data.SqlClient.SqlBulkCopy