It can be difficult to manage cloud costs. As a result, you are likely wasting 30-45 percent or more of your cloud spend. Cloud governance, IT, and finance teams need to understand where costs are coming from, allocate those costs to the appropriate departments, and find ways to reduce waste and save money. In this webinar, we will show you how to manage cloud costs and optimize spend.

1. 12 WAYS TO MANAGE CLOUD COSTS
AND OPTIMIZE CLOUD SPEND

2. Panelists
• Kim Weins
• VP Marketing, RightScale

3. 30%
15%
% of Cloud Spend Wasted
Cloud Users Underestimate Wasted Spend
Source: RightScale 2017 State of the Cloud Report
Self-Estimated
Wasted Spend
Additional
Wasted Spend Measured
by RightScale

4. 3%
16%
19%
20%
23%
33%
30%
30%
31%
45%
4%
15%
20%
24%
25%
31%
33%
35%
38%
52%
Use Google Preemptible VMs
Use AWS Spot Instances
Move workloads to cheaper cloud/region
Select cloud or region based on cost
Track AWS RIs to make sure they are used
Purchasing AWS RIs
Look for storage volumes not in active use
Shut down workloads during certain hours
Automate shut down of temporary workloads
Monitor utilization and rightsize instances
How Companies are Optimizing Cloud Costs
2017 2016
Companies Increase Focus on Cloud Costs
Source: RightScale 2017 State of the Cloud Report

5. Two Solutions from RightScale
VIRTUAL
SERVERS
PUBLIC
CLOUDS
IAAS+/PAAS
SERVICES
PRIVATE
CLOUDS
BARE METAL
SERVERS
CONTAINER
CLUSTERS
MULTI-CLOUD ORCHESTRATION AND GOVERNANCE
RIGHTSCALE OPTIMA
Collaborate across cloud governance teams,
business units, and resource owners to
manage and optimize cloud spend
RIGHTSCALE CMP
Orchestrate, automate, and govern
applications across any cloud, any cloud
service, any server, and any container.

6. RightScale Optima: Key Capabilities
5
Cost
Aggregation
Analysis
Allocation &
Reporting
Forecasting
Budget Controls
Provisioning Policies
Recommendations
Collaboration
Dynamic Policies

7. Cost Review Example
30-45% Savings
6

8. • Tagging
• Cost Allocation
• Reporting
• Forecasting
• Budget Enforcement
• Provisioning Policies
Manage Cloud Costs Optimize Spend
• Underutilized Resources
• Unused/Idle Resources
• Dev Environments
• Regional Cost Differentials
• Superseded Instance Types
• Discounting Options
12 Ways
7

9. MANAGING CLOUD COSTS

10. Tagging: Define Required Global Tags
Tag Type Examples Purpose
Environment env:dev, env:test,
env:stage, env:prod
Used to identify the environment type
Billing bu:bigbu
cc:sales
region:emea
One ore more tags used to allocate
costs
Application app:bigapp
svc: jenkins
One or more tags to define the
application or service
Compliance dataresidency:germany
compliance:pii
compliance:hipaa
One or more tags to define and
compliance requirements
Optimization schedule:24x7
schedule:12x5
maxruntime:14days
One ore more tags to use in automated
optimization

11. • Tag all types of resources that you can
• Resource naming conventions not enough
• Use the same tags for all clouds/environments
• Be exact
• Spelling, spaces, punctuation, upper/lowercase matters
• Use existing automation tools to apply tags
Tagging Tips

12. • Sample rollout process
• Stage 1: Define and communicate required tags
• Stage 2: Scheduled reports on missing tags by team/app
• Stage 3: Instant alerts on missing tags with 12 hour shutdown warning
• Stage 4: Instant alerts and shutdown
Tagging Rollout Process

13. • Account-Based vs. Tag-Based
• Each billing unit has own account(s)
• Shared accounts with costs allocated via tagging
• Purchase/Allocation of Discounts
• Centralized purchase – everyone saves
• Allocate savings proportionally (blended rates)
• De-centralized purchase – buyer saves
• Allocate savings to buyer (unblended rates)
• Handling “upfront” payments for purchase commitments
• Allocate at purchase time based on current usage levels
• Amortize and allocate based on actual usage
• Markups for IT overhead
Cloud Cost Allocation Considerations

14. • Combine Push and Pull
• Schedule automated reports by team/group
• Enable ad-hoc access to latest and greatest cost data
• Frequency
• Typically weekly
• Daily for variable or fast changing environments
• Highlight anomalies
• Significant changes outside normal ranges
Reporting

15. • When to forecast
• Annual budget cycle
• Rolling forecast cycle
• When budget is exceeded for x months
• For new applications/projects/initiatives
• Approaches to Forecasting
• Projected growth patterns
• Change cloud provider
• Change instance types
• Other “what-if”
Forecasting

16. • Set Budgets
• Use forecasts to create budgets
• Budget Alerts
• Alert when current or projected monthly spend exceeds budgets
• Provision-time controls
• Soft limits
• alert if over budget
• request approval before launch if over budget
• Hard limits
• prevent launch if over budget
Budget Enforcement

17. • Tag requirements
• Allowed clouds/regions/services/instance types
• Allowed schedules
• Automated cloud placement by cost/compliance/requirements
• Budget enforcement
Automated Provisioning Policies

18. OPTIMIZE CLOUD SPEND

19. RIGHTSIZING

20. The High Cost of Overprovisioning
m3.xlarge
$.266
$2330/year
m3.large
$.133
$1165/year
m3.medium
$.067
$587/year
Save
50% Save
75%

21. 0
10
20
30
40
50
60
70
80
90
100
CPU Util%
Mem Util%
Don’t Provision to the Peak
Don’t provision to the peak!
Provision for normal loads
and auto-scale for peak

22. Underutilization is Rampant
Memory utilization
CPUutilization
20-40%: Size -1
<20%: Size -2

23. Underutilization is Rampant
Memory utilization
CPUutilization
High CPU utilization
Low memory utilization
Low CPU and memory
utilization
Custom VMs
Downsize

24. Custom Sizing Example (GCP)
4 vCPU
20 GB
You Need Standard VM Custom VM
GCP Cost = $.280/hr GCP Cost = $.163
Savings = 42%
8 vCPU
30 GB
4 vCPU
20 GB

25. IDLE RESOURCES

26. Idle Instances

27. Unused Resources

28. Don’t Forget Storage
Volumes
Snapshots
Attached Unattached

29. Finding Unattached Volumes
Cost
• AWS = $200+ per month
• Azure = $40-120 per month
• Google = $80-340 per month

30. DEV ENVIRONMENTS

31. Development Environment Usage Hours
Mon Tue Wed Thu Fri Sat Sun
00:00
11:59
24x7
168 hours
12x5
60 hours
35%

32. Development Environment Shutdown Dates
Needed
for 3 days
Left running for 4 days
Left running for 7 days
Left running for 14 days
25% waste
57% waste
79% waste

33. A Little Waste Adds Up
Typical
m3.large ($.133)
24x7
16 days
Optimized
m3.medium ($.067)
12x5
14 days
$51.07
$11.26
78%
less
$102,140
$22,520
Per launch 100 devs * 20x/yr
Save
$79K

34. Scheduling Workloads in RightScale

35. • Training
• Demos
• Sandboxes
• Test
• Staging
Other Temporary Workloads

36. EXPENSIVE REGIONS

37. Regional Differences in AWS Example
Region Location
Instance
Size
Hourly
Cost
Cheaper
Region Location
Hourly
Cost % savings
us-west-1 NorCal m3.large $0.15 us-west-2 Oregon $0.13 14%
eu-central-1 Frankfurt m3.large $0.16 eu-west-1 Ireland $0.15 8%
ap-southeast-1 Singapore m3.large $0.20 ap-southeast-2 Sydney $0.19 5%
ap-northeast-1 Tokyo m4.large $0.17 ap-northeast-2 Seoul $0.17 5%
Expensive
Region
Monthly
Spend
Cheaper
Region
%
savings
Monthly
savings
us-west-1 $5,000 us-west-2 14% $700
eu-central-1 $0 eu-west-1 8% $0
ap-southeast-1 $2,000 ap-southeast-2 5% $100
ap-northeast-1 $0 ap-northeast-2 5% $0
$800

38. Regional Differences in Azure Example
Region Location
Instance
Size
(Linux)
Hourly
Cost Cheaper Region Location
Hourly
Cost
%
savings
East US Virginia D1v2 $0.07 East US 2 Virginia $0.06 12%
North Central US Illinois D1v2 $0.07 South/West Central US Texas $0.06 12%
Central US Iowa D1v2 $0.07 South/West Central US Texas $0.06 12%
West US California D1v2 $0.07 West US 2 $0.06 12%
Canada Central Toronto D1v2 $0.08 Canada East Quebec City $0.07 9%
West Europe Netherlands D1v2 $0.08 North Europe Ireland $0.07 14%
East Asia Hong Kong D1v2 $0.11 Southeast Asia Singapore $0.09 15%
Japan East Tokyo D1v2 $0.11 Japan West Osaka $0.09 13%
Australia East NSW D1v2 $0.09 Australia Southeast Victoria $0.08 7%

39. SUPERSEDED INSTANCE
TYPES

40. Superseded Instance Types
Cloud Provider
Previous
Generation
Current
Generation Size mapping
% savings
(East)
AWS T1 T2 same size 35%
AWS M1 M3 same size 24%
AWS M2 R3 downsize one 32%
AWS C1 C3 same size 60%
AWS CR1 R3 same size 24%
AWS HS1 D2 downsize one 40%
Azure D* D*v2 same size 4%

41. DISCOUNTING OPTIONS

42. Your Reality is Constant Change
Family A
2 vCPU
4 GB
Family C
8 vCPU
8 GB
Now
Family A
2 vCPU
4 GB
Family B
2 vCPU
8 GB
Family B
2 vCPU
8 GB
Family A
2 vCPU
4 GB
Family A
4 vCPU
8 GB
Future
Family A
2 vCPU
4 GB
Family B
2 vCPU
8 GB
Family B
1 vCPU
4 GB
Family B
1 vCPU
4 GB
Family A
2 vCPU
4 GB
Family A
2 vCPU
4 GB

43. AWS
RIs
Azure
CPP
Google
SUD/ CUD
IBM
Monthly
Length of
commitment
1 or 3 years CPP
Compute pre-purchase
for 1 year
(must have EA)
SUD: No commitment Monthly: Commit by
month
Payment No Upfront
Partial Upfront
All Upfront
All Upfront No Upfront By month
Range of discount
levels
RI (1Y) 24-58%
RI (3Y) 32-75%
CPP 19-63% SUD: Up to 30%
CUD: 37% (1Yr) or 55%
(3Yr)
Monthly: About 10%
Commitment
Discount
“Grouping”
*Region +
Instance type +
OS +
**Network
*Regional benefit
**Network can be
modified
Datacenter + Instance
type + Instance size +
OS
Has time flexibility
Region +
# vCPUs +
# GBs RAM
*Across any instance
size/type
Any specific individual
resource
Comparing Commitment Discounts by Cloud

44. Commitment Discounts Differ in Flexibility
m3.lg
m3.lg
m3.lg
m3.lg
m3.xl
m3.xl
m3.2xl
c4.lg
c4.xl
c4.2xl
c4.2xl
r3.xl
r3.4xl
r3.xl
D2 v2
D2 v2
D3 v2 F3 v2
G4 v2
F3 v2
D2 v2
D2 v2
G3 v2
n1-std-2
n1-std-2
n1-std-2
n1-std-2
n1-std-4
n1-std-4
n1-std-8
n1-
highmem
-4
n1-
highmem
-8
n1-
highmem
-8
n1-
highcpu-
4
n1-
highcpu-
16
n1-
highcpu-
4
Region Datacenter Region
AWS RIs Azure CPP Google CUD

45. Azure Gives Time Flexibility
D2 v2
D2 v2
D2 v2
D2 v2
D2 v2
D2 v2
D2 v2
D2 v2
D2 v2
D2 v2
D2 v2
D2 v2
D2 v2
D2 v2
D2 v2
D2 v2
D2 v2
D2 v2
D2 v2
D2 v2
D2 v2
D2 v2
D2 v2
D2 v2
D2 v2
D2 v2
D2 v2
D2 v2
D2 v2
D2 v2
D2 v2
D2 v2
D2 v2
D2 v2
D2 v2
D2 v2
Day 1 Day 2 Day 3 Day 1 Day 2 Day 3 Day 1 Day 2 Day 3
Buy blocks of 744 hrs/month
of an instance type/size/OS/region and
use any time in the month

46. About Google Sustained Use Discount (SUD)
• No commitment. The more you use an instance family during
the month, the higher the discount.
Usage Level
% of Billing Cycle
Incremental Rate
% of On-Demand Baseline
Sample Rate
n1-standard-1
Total Cost
0-25% 100% $0.050 $9.00
25-50% 80% $0.040 $7.20
50-75% 60% $0.030 $5.40
75-100% 40% $0.020 $3.60
Monthly Cost
at 100% usage
30% discount $25.20

47. • Commit to # of vCPUs and GBs of RAM
• 1 yr or 3 yr
• Can be used for any instance type or size in a region
• SUD still applies for non-committed use
Google CUD is Based on Family/Size

48. Think “Commitment Discount” Coverage
100 instances
50 Instances under CD
50% CD coverage 50% On-Demand pricing

49. Usage/Cost Pattern for a Commitment Group
48
Production and 24x7 dev usage
Weekday dev usage

50. Think “Commitment Discount” Coverage
49
Target CD Coverage may range from 50-90%
Depends on level of change planned and flexibility of commitments

51. You Need to Monitor “CD” Utilization

52. • Interested in managing cloud costs and optimizing spend?
• sales@rightscale.com
Q&A