1. 3/1/2011

2. Team Members  Alan Chiu Product management, enterprise software, storage, distributed systems  Danielle Buckley Product management, business development, management consulting  Evan Rosenfeld Machine learning, mobile / web app architecture  Gabriel Yu Enterprise software development, web systems

3. Hypotheses needed for cloud compute marketplace  Cloud IaaS has become a fungible commodity  Large supply of excess capacity  Willingness to purchase from various providers  It’s possible to create a cloud marketplace

4. Cloud compute marketplace Build a cloud marketplace Direct sales to both buyers and sellers Many different customer segments on buy- side and sell-side Huge dependency on technical platform

5. We got out of the building…  Interviewed potential buyers Zynga, Xambala, Greplin, Pulse, KISSMetrics, SumoLogic, Zencoder, Desktone, All Covered…  Interviewed potential sellers Savvis, AWS, Azure, Yahoo, Addepar…  Interviewed industry experts VMware, Zuora, NetApp, SolarWinds, telco consultant…

6. … And found a challenging missionary market  Diverse IaaS products  Non-trivial switching costs  Amazon default for many  Long-term vendor relationships dominate Enterprise IAAS

7. Cloud Services Match Maker Pivot away from technical platform Help buyers find the best provider Removed financial, consumer segments Act as channel for sellers

8. We ran AdWords campagns and talked to customers…  Ran Google AdWords campaign to test landing pages and copy  Talked to more customers Seller value proposition Revenue Qualified lead-gen Demand aggregator for public cloud; Turnkey solution for selling excess capacity Buyer value proposition Value-add services Easy buying: enable buyer to purchase the right product from the right provider Cost savings Breaking from vendor lock-in

9. … And struggled to identify a “hair on fire” problem  Low search volume for IaaS comparison  Interest from public sellers in new channel  Private seller IT not revenue-driven  Variable workloads impact opex

10. Low search traffic implies “missionary” sales effort

11. Automated Cloud Capacity Planning Pivot 1: Capacity Planning Pivot 2: Focus on enterprises with variable workload

12. We focused on demand creation and sales…  Researched demand prediction models  Explored sales models with experts  Talked to more customers

13. … And came up with a 2-tiered model  Found traction for capacity planning business  Identified sales strategy Field sales model to large enterprise Inside sales model for lower end offering

14. Inside sales model for entry level customer  $1,000 / mo  5% attrition rate month- to-month  20 month average lifetime  $20,000 LTV  Annual Sales Cost (inside sales): $1.3M Leads cost: $8.3K MarComm: $240k Advertising: $37k 5 Inside sales reps: $1M 2 Tradeshows: $200K  Annual New Revenues: $4.8M Sales ModelEstimated Customer LTV

15. Field sales model for enterprise level customer  $20,000 / mo  2% attrition rate month- to-month  50 month average lifetime  $1M LTV Annual Sales Cost (Field Sales): 3 Field Sales Reps: $1.5M Cost Annual New Revenues: $3M Sales ModelEstimated LTV

16. Enterprise sales process Market targeting Buy-side (Hypothesis: significant IT spend, variable utilization, price sensitivity) Sell-side (Hypothesis: Less security sensitive, unused capacity, high IT competency) Identify ultimate buyer and value prop Ultimate buyer ((Hypothesis: CEO/ CIO for startups; VP Engineering/ CIO for larger enterprises) Buy side value prop (Hypothesis: better matching to needs, $ savings, predictability) Sell side value prop (Hypothesis: channel, unloading excess capacity, revenue source, simplicity) Make calls Ideally to target buyer (Hypothesis: CEO/ CIO for startups; VP Engineering/ CIO for larger enterprises; Sell-side SIO and BU VP) May be time consuming sales cycle (Hypothesis: ~6months) May include pilot / POC (Hypothesis: likely, particularly for large enterprises) Technical due diligence, customer reference checks Due diligence (Hypothesis: technical due diligence, uptime, credibility (funding, etc), security) Closing, contracting

17. Capacity Planning · High variability in usage Service Matching Companies new to cloud SLA Monitoring Companies with high SLA requirements · IaaS Integrators / consultants Inside and field sales · Development Costs · Infrastructure costs – AWS · Support costs Subscription charge to buyers Pricing table scales based on # of servers and # of seats, with tiers · For enterprise, higher touch model with field sales Customers · Reduced cloud infrastructure cost · Increased visibility on service level Integrators: · Increased revenue Develop capacity planning algorithm Develop IaaS vendor relationships Marketing and sales · Technology partners – cloud vendors, management tools · System integrators / Consultants · IP– prediction · Developers · Inside sales force · Field sales force · Biz dev (channel and technology partners) Agora – FINAL Cloud Lifecycle Management Partner with Integrators Leverage both inside and field sales Position product for lifecycle management

18. We got out of the building, and built a business model…  Decided to use two-tier sales model  Attended AWS meet-up  Interviewed IT consultants  Analyzed competitor and comparable models  Selected strategic direction

19. …and validated a 2-tier sales model with integrator support  Ecosystem of cloud IT consultants / integrators willing to engage  Our product makes integrators money  Concerns about 2-tier sales model, though some examples of success  Income statement passed test of reason

20. Agora Evolution Service Matching Automated Capacity Planning SLA Monitoring True market

21. Addressing $5.4B market Stage 1: Demand Prediction Stage 2: Service Matching Stage 3: Usage Monitoring/Co ntrol Stage 4: Lifecycle Management Relevant Category IT Capacity Planning, Job Scheduling Lead-gen on cloud spend Server Management BSM/ALM Sizing Estimate Capacity Planning: $258M (2008) -> $392M (2011) Job Scheduling: $1.2B (2008) -> $1.6B (2011) Forrester 10% affiliate fee on $13.1B cloud spend = $1.3B IDC 2010 $430M (2008) -> $500M (2011) Forrester $637M (2008) - > $1.6B (2011), accelerating 36% YoY growth rate Forrester Total$2B$1.3B$500M$1.6B

22. We came a long way  Key Lessons Early days for compute market Opportunity for tools to support move to PaaS/ SaaS adoption Customer engagement crucial  Our product now: a tool set for managing cloud compute usage Service matching Capacity planning Usage monitoring & control Targeting ~30% savings for customer  Potential for a viable business

23. Thanks!

24. Appendix: Canvases

25. Week 1

26. Week 2

27. Week 3

28. Week 4

29. Week 5

30. Week 6

31. Week 7

32. Capacity Planning · High variability in usage Service Matching Companies unfamiliar with using cloud infrastructure SLA Monitoring Companies with high SLA requirements with their customers · Integrators / consultants specialized in cloud infrastructure Inside sales and field sales · Development Costs · Infrastructure costs – AWS · Support costs Subscription charge to buyers Pricing table scales based on # of servers and # of seats, with tiers · For enterprise segment, higher touch model with field sales force · Reduced cloud infrastructure cost · Better compute needs matching · Increased visibility on service level Integrators: · Increased budget for consulting services Design and refine capacity planning and match making algorithms Develop and maintain cloud infrastructure vendors relationships Develop brand as go-to place for cloud lifecycle management · Technology partners – cloud vendors, management tools · System integrators / Consultants · Intellectual property – prediction algorithm · Developers · Inside sales force · Field sales force · Biz dev (channel partners and technology partners) Agora – V8 Week 8