What can we begin to expect in terms of types of cloud services, including the trends for today and tomorrow? To best understand the kinds of services available in the cloud, readers should have fundamental perceptions of how various services function and articulate performance as driven by the user or subscriber to those particular services.
To review from the many previous articles in this column and TV Tech, these are the key “ready-to-use” applications found in many cloud computing platforms:
- “Infrastructure as a Service (IaaS),” see Fig. 1, for fundamental resources like servers.
- “Platform as a Service (PaaS),” see Fig. 2, for cloud-development environments.
- “Software as a Service (SaaS),” for ready-to-use applications.
- Serverless computing or “Function as a Service (FaaS),” see Fig. 3, for event-
driven functions, which focuses primarily on pure code execution.
That said, most of the various XaaS applications are pretty much solid, running in fixed data centers or as cloud-compute services. So, where indeed does “cloud computing” head next?
Again, we move back to those topics covered over the past 18 months or so in my TV Tech columns, which detail the four main types of cloud computing deployment models: public, private, hybrid and multi- and/or community clouds. In the not-too-distant future, the next generation of cloud-compute platforms will need to offer different levels of control, security and cost, especially when looking at operations and utilization from shared public resources (i.e., “you” the end user).
Another division of overall “cloud” models, infrastructures or architecture must further address different types of cloud computing, including deployment and service models. Defining cloud computing models further describes which computing resources are appropriate for which applications, such as server vs. serverless, shared or distributed storage, databases, software and specialized applications, which are, generally, delivered over the internet. This implies companies can utilize these (and other emerging resources) without possessing or maintaining a huge or costly physical infrastructure.
What’s Coming Next?
Trends to expect in 2026 for cloud computing will likely involve the harmonization of techniques for deep AI/ML (artificial intelligence/machine learning) integration. We also anticipate significant shifts due in part to changes in data-center development and related infrastructures, i.e., the expansion of edge computing and the provision of sufficient bandwidth to deliver solutions “to the edge” needed to address expected demands from a variety of devices, mobile and otherwise.
As competition for services increases and the adaptation of existing cloud-centric data centers yields more choices for users, we can certainly expect widespread adoption of multicloud and hybrid cloud strategies.
New developments that will leverage a focus on cloud-native technologies, including serverless and containers, will change the level of I/O requirements. Internet egress (i.e., the on-ramps and off-ramps) will continue to expand as more players enter the cloud and/or AI marketplace.
Quantum Growth
Furthermore, we can anticipate a growing interest in quantum computing—i.e., quantum mechanics (superposition and entanglement)—with qubits to process information, allowing them to be 0, 1, or both simultaneously, unlike classical bits (0 or 1 only).
This level of structure aimed at supporting quantum computing through the cloud will surely require a strong emphasis on sustainability while managing costs via FinOps, or “finance” and “DevOps” defined as a collaborative, cultural practice. Financial management discipline must help organizations reach high business value from cloud spending by bringing engineering, finance and business teams together to make data-driven decisions for optimizing costs, improving efficiency and aligning cloud usage with business goals.
In addition to more depth on each of the cited trends, data litigation and protection are also expected to be important global trends that may be categorized as Data Sovereignty and Compliance. Each of these trends plays on new or expanded capabilities in data-systems design and engineering including—if not especially—enhanced security (including DevSecOps and/or Zero Trust). The data industry will soon need to embrace Intelligent Security (otherwise known as DevSecOps) beginning at the initial software-development process.
Driven in part by the emphasis on AI, expect that an increasing focus on meeting strict data regulations and ensuring data privacy could dramatically change the landscape, if it is not carefully orchestrated on an international basis that bypasses politics or attempts at “global dominance” by any governing body.
Developers and service providers will be expected to provide still “yet-to-be-fully-defined” levels of embedded security into the development pipeline (i.e., DevSecOps) and to adopt zero-trust models for automated, reliable cloud security.
An Intelligent Edge
Artificial intelligence is evolving deeply into embedded systems built on or in cloud platforms, optimizing every facet of cloud operations and security. Of importance is this integration of machine learning and AI at all levels of the computer chain. Among the features we can expect to see are real-time resource allocation, automated scaling (or the resizing of compute resources based upon the load), predictive maintenance and advanced security-threat detection. Promoters believe such changes are crucial to realizing the true needs and value of AI in any cloud computing environment.
Edge computing is a distributed IT approach that processes data nearer to its source (the “edge” of the network) instead of in distant, centralized cloud data centers. This is exemplified by some of the functionality of mobile devices that make choices or provide answers without necessarily being specifically connected (wired or wirelessly) to “the network”—akin to how IDP systems will cache certain sets of predicted replies to the local server rather than rely on every communication sourcing back through the network to a mainstream data center.
AI will be used to make edge devices more intelligent, improving speed, accessibility and endurance for select mobile devices.
By balancing source vs. edge computing capabilities, the provider extends the reach of the cloud to the edge of the network—enabling faster data processing from internet of things (IoT) devices, autonomous vehicles and other edge devices. AI in edge computing through on-device AI inference, on-the-edge AI model training and thin-edge AI was a key trend in 2024. Today and going forward, bringing compute and storage closer to devices, in turn, cuts latency and improves efficiency for time-sensitive tasks. In essence, the edge device needs only to send back certain essential data (information) back to the core “source” data center.
Moving to the Cloud?
Some 76% of businesses moving to the cloud use a hybrid or multicloud approach, according to a May 2025 blog post from managed services provider All Covered. In a broad sense, the primary trends in cloud computing include a rise in platform engineering that aims to manage multicloud complexity, as well as AI adoption as the main driver. Such improvements are properly coupled with FinOps (i.e., cloud-cost optimization trends and tools) and loud sustainability or “Green Cloud” computing trends.
