DevOps trends 2023: Focusing on optimization rather than growth. Part 2

DevOps appeared 10+ years ago and has grown in popularity since then. And performing deep dives into DevOps trends is a must-have activity for every dignified cloud-found professional.

The DevOps Market Research report claims that the Global DevOps Market size, at a compound annual growth rate of 18.95%, is estimated to reach USD 12,215.54 million by 2026. And According to Google’s research, 77% of organizations rely on DevOps to deploy software to boost product/service quality, time-to-market, and teams’ productivity. That is why many IT businesses are eager to implement DevOps in their teams, with the main focus on optimizing cloud environments, without which no growth can happen.

Let’s look at the hot DevOps trends for you to expect in 2023 and beyond. And this is only Part 2 of the DevOps trends list. Part 1 is here to draw a complete picture of a methodology capable of skyrocketing businesses’ performance and revenue.

DevOps trends 2023

1. Unlocking new approaches to observability with eBPF and WASM

New technologies to unlock new approaches to observability, monitoring, and security within the service meshes and ingress domains are in the spotlight now. On December 2, 2021, the Cilium project announced the Cilium Service Mesh beta testing program to promote the idea of a “mesh without sidecar,” extending the Cilium eBPF product proxy functionality:

L7 routing and load balancing,
TLS termination,
access policies,
health checks,
logging and tracing,
and built-in Kubernetes login.

Source: isovalent.com

This technology is designed to break free from the sidecar model and allow existing proxy technology to be integrated into the kernel namespace concepts, making them a missing piece of the attractive container puzzle picture we use daily.

In a nutshell, eBPF promises to solve one of the main problems of the service mesh – poor performance in the presence of many fine-grained microservices. However, supplementary services are increasing exponentially along with the number of microservices. With an application containing hundreds of interconnected, load-balanced microservices, the overhead becomes enormous, promoting the need for modifications and leveraging sophisticated third-party platforms to improve control over bloated infrastructures.

2. Embracing AI/MI models to optimize performance and improve security

AI for DevOps is about moving towards more automation and proactive mechanisms that allow teams to innovate faster. AI for DevOps is designed to empower developers with machine learning capabilities, which represents a shift from manual processes with occasional deployments and stagnant innovation rounds to fast CI/CD pipeline flow with continuous monitoring. AI/MI-powered DevOps experiences:

Automatically detected and rapidly fixed operational issues
Improved product quality via continuous monitoring
Upgraded performance and reduced costs
Improved security.

Artificial intelligence and machine learning are firmly embedded in many DevOps teams today. As many as 62% practice ModelOps, 51% use AI/ML to validate code, 40% use “bots” or different AI/ML models to test their code, while only 5% have no plans to include AI/ML in their DevOps practices, according to the GitLab report.

3. Welcoming Chaos Engineering to DevOps

“Chaos engineering helps us ensure that we’re investing in the right things,” said Netflix resilience engineer Ales Plsek.

The more complex applications grow, the more common various problems with their maintenance become. To build more resilient applications and solve problems before they lead to reputational and cost-loss incidents, DevOps teams are increasingly addressing “chaos engineering.” Interest in this methodology has skyrocketed since Netflix first released the code for its Chaos Monkey in 2012. The Chaos Monkey was followed by open source tools such as Litmus, Chaos Toolkit, and PowerfulSeal to provide reliable and customizable solutions capable of experimenting with chaos. Plus, AWS Fault Injection Simulator is a managed service for experimenting with error injection in AWS that makes it easy to improve application operation, visibility, and elasticity.

Gartner expects that by 2023, 40% of organizations will implement chaos engineering practices as part of DevOps initiatives, reducing unplanned downtime by 20%.

4. Prioritizing optimization to growth

Gartner’s annual global CIO survey, which polled over 2,000 CIOs, revealed that CIOs’ future technology plans remain focused on optimization rather than growth. CIOs’ top areas of increased investment for 2023 include cyber and information security (66%), business intelligence/data analytics (55%), and cloud platforms (50%).

Generally seen as the definition of a successful evolution, growth refers to increasing revenue due to adding resources. A cloud-based IT project takes a lot of resources (cloud services, DevOps specialists, etc.) to sustain constant growth. The key difference between growth and optimization is increasing revenue without attracting significant resources but improving the efficiency of the already-existing system. It should look like costs decrease while adding revenue in a perfect world. It would be great, but how? When we recall the main idea of DevOps, we will get our answer. DevOps is all about automation, streamlining, and accelerating software delivery processes that cannot happen without a well-architected optimization strategy that, alongside with best DevOps and Cloud practices & technologies implementation, will focus on bringing financial awareness into infrastructure management.

5. FinOps: bringing financial management to the cloud

Clouds continue to grow, and so does the problem of increasing cloud costs. As a result, cloud optimization is the top issue for the sixth year. Cloud cost management should be as much a part of the project lifecycle as development.

A straightforward fusion of finance and engineering departments is not an option. Unlike traditional on-premises systems, where software development costs can be easily tracked, cloud operating costs are much more difficult for non-cloud experts to understand. In addition, most cloud providers offer a pay-as-you-go model for their cloud services, which means that services scale quickly and almost infinitely as workloads grow. This is why FinOps, the cultural model of cloud financial management, matters. With the right FinOps strategy, teams can overcome the barrier of cloud cost misunderstanding and get spending under control.

For FinOps to work, you need to:

Provide (or improve) cloud infrastructure visibility/visualization
Eliminate cloud waste
Choose the right size for your instances
Organize the optimal schedule of the machines
Apply an effective spot management strategy
Reserve resources or/also skillfully use savings plans.

6. Highlighting observability to take clouds under control

Like any transformation program, DevOps evolution begins with scanning the existing environment, understanding the situation, and simply getting rid of everything that has been the bane of your cloud architecture’s existence.

DevOps observability is essential to gain high visibility into complex, multi-account, and multi-cloud ecosystems, understand changes, and map progress steps ahead. Monitoring enables a complete picture of performance. Observability provides deep cloud visibility and awareness of what is happening inside the infrastructure, collecting data and transforming it into rich, visualized, actionable insights, enabling DevOps teams to address the mess fast.

7. Greenlighting platforms in DevOps

The most highly evolved in DevOps models organizations are adopting a platform model that enables self-service for developers, provides a guided experience for the customers, and ensures delivery is smoother and faster. In addition, internal platforms improve their organizations’ performance. The strong relationship between internal platforms is illustrated by whooping growth from 8% to 48% of the DevOps evolution degree presented in this graph.

Source: 2021 State of DevOps report

Customized engineering platforms welcomed in DevOps to streamline the processes and improve teams’ collaboration are pretty effective, but only a few organizations, especially startups and SMOs, can build them, considering platforms require ever-increasing levels of investment. For example, a company may spend $10 million to make/assemble a platform and a quarter of a million to maintain it the following year. And if we add to this picture cloud bills constant swelling, pushing cloud costs optimization to head top cloud initiatives, the picture becomes pretty messy.

To streamline DevOps processes and cut cloud costs, bringing effective financial management to the cloud with the help of a unified cloud management platform, like Uniskai, is a solution. Uniskai by ProfiSea Labs has been designed to let users visualize, govern, analyze, and optimize their cloud infrastructure. Powered by AI, Uniskai scans system utilization, analyses data, and provides customized recommendations to handle cloud bill bloating. Uniskai is very helpful for young startups to reduce cloud costs at most. However, it’s no less beneficial for large enterprises where each project is a startup, and each team member takes responsibility for their cloud resource allocation and usage.

Final thoughts

DevOps empowers organizations to act smarter, collaborate faster, and deliver better results. So it’s no surprise that DevOps has become a top priority for the tech industry.

DevOps trends described above will help organizations move quickly from automation to continuous improvement, catalyzing creating a reliable release pipeline and improving communication between business teams while reducing cloud spending.

If you have any DevOps/FinOps-related questions or you’re looking for a solution to reduce your cloud costs, just send us a message.