This article is based on analysis originally published in April 2019 via BizTech Magazine. The trends discussed remain relevant context for understanding the trajectory of enterprise cloud strategy.
The Multicloud Inflection Point
Multicloud strategies had already achieved significant enterprise penetration heading into 2019. A survey cited by Virtustream at the time suggested that 86 percent of enterprises were adopting a multicloud approach — a figure that reflected a broader industry shift away from single-vendor cloud dependency.
The motivations were practical: avoiding vendor lock-in, improving resilience, and selecting best-of-breed services across providers. Technologies like Kubernetes accelerated this by making it possible to containerize workloads and move them between cloud environments with reduced friction. Against this backdrop, five major cloud computing shifts were anticipated for the year ahead.
1. The Shift from Automation to AI in the Cloud
The growing scale and complexity of cloud environments had already pushed IT teams toward automating monitoring, resource allocation, and troubleshooting. The logical next step — one anticipated to accelerate in 2019 — was moving from rule-based automation toward adaptive, AI-driven management.
The case for this is straightforward. Static automation rules work well in predictable environments, but multicloud infrastructure is anything but static. Workloads shift, traffic patterns evolve, and failure modes multiply. Concepts such as self-healing infrastructure — where systems detect and recover from failure without human intervention — become critically important at scale, but only if the underlying intelligence can adapt to novel conditions rather than just pattern-match against known ones.
For DevOps teams, this reinforced the importance of investing in observability tooling, AIOps platforms, and intelligent alerting rather than relying solely on threshold-based monitoring.
2. Major Public Cloud Providers Would Have to Differentiate
Infrastructure as a Service had become a commodity by 2019. Compute and storage pricing had converged across the major providers, meaning that AWS, Azure, and Google Cloud could no longer compete on raw infrastructure cost alone. In a multicloud world, enterprises could mix and match providers, which forced each to articulate a clearer value proposition.
The differentiation strategies that emerged aligned with each provider’s existing strengths:
- Google Cloud leaned into its AI and machine learning capabilities, positioning services like BigQuery ML and AutoML as differentiators for data-intensive workloads.
- Microsoft Azure focused on hybrid cloud and enterprise workload migration, leveraging its deep integration with Windows Server, Active Directory, and the broader Microsoft 365 ecosystem.
- AWS continued to expand its breadth of managed services, betting that ecosystem depth and maturity would retain customers even in a multicloud environment.
Competitive pressure from IBM’s acquisition of Red Hat — completed in July 2019 — also signaled that the enterprise open-source and hybrid cloud space was becoming a serious battleground. Regional cloud providers in Europe and Asia similarly continued to mature, motivated in part by data sovereignty requirements.
3. Container Orchestration at Scale Became the Real Challenge
By 2019, container adoption had moved well beyond the experimental phase. The question was no longer whether to containerize workloads, but how to manage hundreds or thousands of containers reliably across distributed infrastructure.
Kubernetes had effectively won the container orchestration wars by this point, but operational maturity — handling upgrades, secrets management, networking, and multi-tenancy at scale — remained genuinely hard. This created an acquisition opportunity: larger vendors began absorbing specialist companies whose expertise in container networking, security, or management tooling could be folded into broader platform offerings.
Serverless computing also emerged as a complementary pattern. Functions-as-a-Service offerings from all major providers allowed developers to run event-driven code without managing infrastructure at all. Open-source serverless frameworks — Knative being a notable example that gained traction during this period — began to challenge proprietary serverless platforms by offering portability across cloud providers.
For engineering teams, the practical takeaway was to invest in platform engineering capabilities: internal developer platforms that abstract away infrastructure complexity and let application teams focus on business logic rather than operational plumbing.
4. Deployment Complexity Required Standardization and CI/CD Discipline
As cloud architectures grew more sophisticated — mixing managed services, containers, serverless functions, and legacy workloads — the operational burden of managing deployments increased significantly. The response from the industry was a stronger emphasis on composable, standardized infrastructure building blocks.
The analogy used at the time — Lego-like building blocks — captures the intent well: modular infrastructure components that can be assembled, tested, and redeployed predictably. In practice, this translated into:
- Infrastructure as Code (IaC) using tools like Terraform or CloudFormation to make environments reproducible and auditable.
- CI/CD pipelines that automated testing and deployment, reducing the risk associated with each incremental change.
- Open-source tooling to avoid licensing costs and to benefit from community-driven quality improvements, while also making it easier to benchmark productivity changes tied to specific software decisions.
The continuous integration/continuous delivery model became increasingly non-negotiable for teams operating in fast-moving cloud environments — not just as a software development best practice, but as an operational requirement given the pace of cloud service evolution.
5. Edge Computing: Hype Meeting Early Reality
Edge computing — the practice of processing data closer to where it is generated rather than routing everything to a centralized cloud — had been discussed for years before 2019, but the combination of IoT device proliferation and the early rollout of 5G networks made the possibility of mature edge deployments more credible.
The technical case for edge is compelling in latency-sensitive contexts. Processing data at or near the source — rather than transmitting it to a regional data center for analysis — can reduce round-trip latency from hundreds of milliseconds to single digits. The anticipated 5G characteristics frequently cited at the time included multi-gigabit throughput and sub-millisecond latency under ideal conditions, which would enable real-time data processing for use cases like autonomous vehicles, industrial automation, and smart city infrastructure.
However, as with any emerging technology, proof of concept remained the critical gating factor. Organizations were rightly cautious about committing to edge infrastructure before vendors could demonstrate both operational reliability and a credible cost model. The ecosystem — including edge-optimized hardware, management software, and security frameworks — was still maturing.
The broader lesson for DevOps practitioners: edge computing introduces a genuinely new operational tier that requires rethinking deployment pipelines, device management, and observability. It is not simply “cloud, but closer” — it demands purpose-built tooling and architecture patterns.
Key Takeaways for Cloud and DevOps Teams
- Multicloud is a strategy, not a default. Adopting multiple cloud providers without a governance model adds complexity without necessarily adding resilience or cost efficiency.
- Automation must evolve toward intelligence. Rule-based automation has a ceiling; adaptive, AI-assisted operations become necessary as infrastructure scale and complexity grow.
- Container maturity is an organizational challenge as much as a technical one. The tooling exists; the harder work is building the platform engineering discipline to run it reliably.
- Standardization enables speed. Teams that invest in composable, IaC-driven infrastructure ship faster and recover from incidents more reliably.
- Edge is real, but requires deliberate architecture. Evaluate edge use cases based on genuine latency or bandwidth requirements, not trend-following.
Original analysis sourced from BizTech Magazine, April 2019.