DevOps teams are leveraging AI copilots and agents to navigate the complexities of multicloud environments. Here are seven strategies showing how generative AI can enhance multicloud adoption, governance, observability, and more.
Adopting a single cloud infrastructure is considerably simpler than navigating a multicloud approach. Within a single-cloud environment, IT leaders can streamline skill development, centralize data with greater ease, secure infrastructure using fewer tools, and achieve numerous other operational advantages. Nevertheless, 89% of organizations are reportedly shifting towards multicloud adoption. The motivations for operating across multiple clouds include mitigating risks, minimizing service interruptions, and avoiding vendor dependency.
Cloud providers have responded to the intricacies of multicloud with “single pane of glass” solutions that function across various cloud platforms. For instance, AIOps platforms offer consolidated observability and data monitoring, while many data security posture management (DSPM) platforms are designed for multicloud use. Implementing platform engineering practices, integrating FinOps early in the architecture phase, and automating CI/CD deployments are three strategies DevOps teams can employ to lessen the operational burden of managing diverse cloud environments.
Generative AI tools, including AI copilots and AI agents, are also proving increasingly valuable. Leading IT departments are utilizing genAI to draft agile requirements, develop software, automate testing, and maintain essential documentation.
I consulted with IT leaders about their methods for employing generative AI to boost efficiency and simplify the management of complex multicloud architectures.
1. Assessing Cloud Service and Code Portability
Architects face the challenging task of weighing the advantages and disadvantages of proprietary cloud services against cross-cloud platforms. For instance, should developers opt for AWS Glue, Azure Data Factory, or Google Cloud Data Fusion to build data pipelines on their respective platforms, or should they choose a data integration platform that functions across various clouds?
Generative AI introduces a novel possibility for code generation and translation. Imagine a developer writing ETL (extract, transform, load) code for one cloud during development, then seamlessly adapting it for a different cloud provider if the architectural needs change.
“Managing multicloud environments is akin to mastering multiple languages from AWS, Azure, Oracle, and others, and it’s uncommon to find teams that can navigate these diverse environments with fluency and efficiency. Moreover, services and concepts are not easily transferable between clouds, particularly with cloud-native PaaS services that extend beyond IaaS,” explains Harshit Omar, co-founder and CTO at FluidCloud.
A potential solution to this challenge involves assigning an AI agent to assist the developer or architect in evaluating platform choices. This AI agent would analyze established standards, decision criteria, and requirements to propose solutions and outline associated trade-offs.
“Generative AI can serve as a DevOps copilot that grasps the user’s intentions and design preferences, whether the objective is to optimize for cost, performance, or security, and then automatically generates the appropriate infrastructure patterns,” Omar states. “Teams can dedicate less effort to finding multicloud specialists and more time to executing infrastructure updates and optimizing workloads for the most suitable environments for their business.”
Recommendation: Cross-cloud porting will become a more practical option for straightforward setups and deployments. Utilizing cloud architecture agents and AI tools for code translation can significantly aid multicloud portability.
Also see: Mastering multicloud: The updated essential guide.
2. Transitioning from Coding to Enhancing Resiliency
Developing APIs, applications, and data pipelines is becoming more accessible with generative AI tools. According to The 2025 State of AI Code Quality report, 82% of developers use AI coding tools either daily or weekly, with 70% reporting an improvement in code quality. While automation helped IT push “shift left” initiatives to prioritize customer experience and improve data quality, code generators could facilitate a “shift right” towards boosting operational resiliency.
“Generative AI is fostering a new expertise where knowledge workers learn to code using prompts and specifications, while AI handles the specific cloud mechanics,” remarks Ed Frederici, CTO of Appfire. “In the future, success will be measured not merely by cost savings, but by robust systems, enhanced governance, and empowered teams who view AI as their primary toolset rather than getting bogged down by the intricacies of each individual cloud, allowing them to work with increased assurance and impact.”
Recommendation: Generative AI can boost resiliency by translating governance policies into implementations specific to each cloud environment.
3. Generating Multicloud Configurations from Standard Requirements
Achieving consistent infrastructure and service configurations across various clouds demands specialized knowledge of diverse naming conventions, architectures, tools, APIs, and other paradigms. Generative AI tools are expected to serve as translators to streamline configurations, particularly for organizations capable of templating their requirements.
Jed Dougherty, head of AI architecture at Dataiku, notes that managing multiple clouds can be an exasperating experience due to each cloud’s unique approach to security, access, pricing, and services. Dougherty anticipates generative AI will simplify this translation process: “Picture genAI automatically converting a complex AWS IAM role into an Azure Role Definition, or an AWS CloudFormation template into a Google Deployment Manager Configuration.”
Recommendation: Seek out generative AI tools that can construct automations and cloud configurations from a unified set of requirements, translating them into cloud-specific implementations.
4. Streamlining Operations and Automation
CI/CD, infrastructure-as-code, and process automation are crucial tools for boosting efficiency, especially when tasks span across multiple cloud environments. Many of these tools rely on basic flows and rules to simplify tasks or orchestrate operations, which can sometimes lead to edge cases that cause process-halting errors. Incorporating generative AI into these automations can facilitate more robust processes and broaden their applicability.
“Managing multicloud environments has traditionally been intricate, demanding various tools for orchestration, compliance, and cost management,” states Mehdi Goodarzi, SVP and global head of AI at Hexaware. “Generative AI is now transforming this by introducing automation, contextual insights, and intelligent governance into the ecosystem. It simplifies visibility, proactively addresses performance and security concerns, and seamlessly orchestrates workloads across different providers. Collectively, this evolution transforms multicloud from a resource-intensive requirement into a powerful facilitator of agility, resilience, and business growth.”
Recommendation: Look for generative AI capabilities that offer recommended actions within their workflows and error handling. DevOps teams can then generate risk and accuracy scores to help determine which actions to automate and which necessitate human intervention.
5. Enhancing Problem Resolution with Generative AI Observability
Site Reliability Engineers (SREs) are expected to perform effectively during outages or performance issues, but they prefer to proactively review system performance and suggest improvements to developers before problems arise. While enhanced application observability has aided SREs, it has also introduced data management complexities.
“Multicloud complexity fundamentally stems from a data issue, and generative AI’s strength lies in constructing a unified semantic layer over configurations, logs, schemas, and lineage,” states Tobie Morgan Hitchcock, co-founder and CEO of SurrealDB. “Natural-language SRE copilots will deduce topology, data gravity, compliance, and cost factors to propose optimal placements, generate runbooks, and continuously rectify drift across cloud environments.”
Inconsistent and non-standardized observability data can lead to erroneous alerts and misdiagnoses. However, requiring development teams to adhere to strict data standards and naming conventions comes at a cost.
“Current multicloud operations overwhelm teams with a flood of noisy, disconnected alerts that obscure the actual problems,” comments Kyle Campos, CPTO of CloudBolt. “Generative AI transforms this by interpreting complex, cross-cloud telemetry and highlighting only high-value incidents and optimization opportunities with pertinent context. The outcomes include reduced alert fatigue, quicker resolutions, and a noticeable improvement in day-two operations—a crucial step for how enterprises build, manage, and continuously optimize across clouds.”
Recommendation: SREs should dedicate time to evaluating AI agents and other generative AI functionalities that streamline and accelerate the utilization of observability data to enhance application performance.
6. Narrowing the Gap Between Policy and Compliance
Every cloud provider offers its own set of tools for implementing policies and assessing compliance. When these policies are updated, security and operations teams must update their implementations individually, which is an inefficient process.
“Enterprises integrate three distinct technology stacks from AWS, Azure, and GCP, resulting in escalating operational complexity, fragile cross-cloud integrations, and increased costs driven by data gravity, egress fees, and ongoing talent shortages,” explains Pranay Ahlawat, chief technology and AI officer at Commvault. “Generative AI can automatically produce portable Infrastructure-as-Code (IaC)/policies to translate intent into native controls and correct drift across clouds, thereby improving compliance and cost efficiency.”
Recommendation: Regulated businesses will require compliance and security tools that enable policies to be implemented once, then deployed and reported across various cloud capabilities. Look for generative AI features within these tools to support reporting and configuration tasks.
7. Facilitating Continuous FinOps Monitoring
Cloud cost reports with recommendations are natively available in each cloud provider’s reporting tools and within specialized FinOps tools that consolidate relevant data. Organizations with substantial variable costs, particularly as they scale AI programs, need more than static reports for ongoing cost and workload optimization.
“Conventionally, organizations struggle to monitor, provision, and optimize workloads across multiple clouds, as they contend with diverse APIs, tools, and cost structures,” observes Kevin Cochrane, CMO of Vultr. “Generative AI simplifies this by offering intelligent recommendations, predictive scaling, and automated policy enforcement across environments. This reduces operational overhead, minimizes misconfigurations, and ensures workloads operate efficiently and cost-effectively, enabling teams to concentrate on AI innovation rather than managing complexity.”
Recommendation: Smaller organizations should delegate the task of reviewing cloud costs to native tools like Azure Advisor, Google Cloud Recommender, and AWS Cost Explorer. Larger organizations should investigate the generative AI capabilities offered in FinOps tools designed for both financial and engineering stakeholders.
Can Generative AI Fully Resolve Multicloud Complexities?
Generative AI is currently being integrated into various development and operational tools, but it is not a complete panacea. Regarding multicloud, Ahlawat from Commvault states that generative AI does not eliminate fundamental constraints such as data gravity, latency, commitments, or skill shortages. “Organizations still require robust guardrails and platform engineering to manage overall operational complexity,” he asserts.
We can also anticipate public clouds introducing further differentiating features, meaning that even as generative AI-powered tools simplify current challenges, new ones are likely to emerge.
