Get Datadog Dash 2022 Survey insights by Sedai: their top challenges, computing architectures, and how autonomous cloud systems have an impact. Check here!
At Datadog Dash 2022 in New York, we conducted a survey of attendees who gave us insight into their top challenges, compute architectures, and expectations of how autonomous cloud systems will impact their top objectives.
Here are the three most important takeaways from the survey results:
1) The #1 challenge of Datadog users? Managing costs
The Data:
We asked Datadog users what their number one challenge was. They told us their major concerns:
- #1 - 41% said Cost Reduction
- #2 - 31% said Ops Productivity
- #3 - 13% said Developer Productivity
- #4 - 8% said Other
- #5 - 5% said Performance Improvement
- #6 - 3% said Availability Improvement
Our take:
The high rating of cost management reflects a mix of:
- Recession concerns. In October 2022, The Conference Board was predicting a 96 percent likelihood of a recession in the US within the next 12 months, caused by Federal Reserve interest rate hikes.
- Persistent challenges in managing cloud costs. Surveys such as the Flexera State of the Cloud Survey have found self-reported cloud waste of 35%. Usage data from Datadog highlighted low utilization rates with the median Kubernetes deployment uses ~20-30% of requested CPU and 30-40% of requested memory.
If you are a Datadog Kubernetes user and cost is one of your top concerns, watch our Kubernetes Cost Workshop in which we share how Datadog users can reduce costs by 50%.
2) Datadog users expect a 48% gain from autonomous
The Data:
After asking Datadog users' what their #1 priority was, we then asked them what the impact of autonomous operations would have on this goal (e.g., cost):
- 27% said a 1-25% gain
- 36% said a 26-50% gain
- 32% said 51-100%
- 5% said >100%
The overall average works out to around 48% taking midpoints of these ranges.
Our take:
Directionally Datadog users expect autonomous to make a material shift, which would be in line with Sedai's experience e.g., ecommerce company & Datadog user fabric co-incidentally reduced latency by 48% after applying autonomous management. We talked with one team at Dash who noted that current automation methods consisting of setting rules after optimization of a given release were not working in an environment where development teams made regular releases. As they put it "automation is great but it's creating more work for our team". Their developers were releasing new code without working with the ops team to optimize for performance and cost which was driving their Kubernetes costs up.
3) Datadog users primarily run modern apps
The Data:
We also surveyed Datadog users on their compute technologies. At a service level:
- #1 - 44% run EKS
- #2 - 38% run other Kubernetes options
- #3 - 33% run AWS Lambda
- #4 - 28% run AWS ECS
- #5 - 10% run non-Lambda serverless
- #6 - 10% ran other compute platforms
Overall:
- 77%+ run containers (including Kubernetes & ECS)
- 72% run Kubernetes ((including EKS and other Kubernetes flavors)
- 36% run serverless (Lambda or other serverless options)
Our take:
Datadog users are sophisticated users of modern apps.
How Datadog users can realize the promise of autonomous
Datadog users can now add autonomous management capabilities, improving the performance, cost and availability of their applications while avoiding the time & cost of traditional automated approaches. Watch the video below for a walk through the limitations of current approaches, top use cases for autonomous with Datadog, and how to get setup and use Sedai with Datadog using the integration available in the Datadog marketplace.