Standardizing on a single cloud infrastructure is much easier than pursuing a multicloud strategy. In a single-cloud environment, IT leaders can optimize skill sets, centralize data more easily, secure infrastructure with fewer tools, and gain many other operational benefits. Yet 89% of enterprises report they are pivoting to multicloud adoption. Reasons for choosing to operate across multiple clouds include mitigating risk, reducing service interruptions, and avoiding vendor lock-in.
Vendors have responded to multicloud complexities with “single pane of glass” tools that operate across cloud providers. For example, AIOps platforms can centralize observability and data monitoring, while many data security posture management (DSPM) platforms are multicloud. Establishing platform engineering practices, shifting finops left as a key architecture practice, and automating CI/CD deployments are three ways devops teams can reduce overhead in managing multiple clouds.
Generative AI tools, including AI copilots and AI agents, are also becoming invaluable. World-class IT departments are using genAI to write agile requirements, develop software, automate testing, and maintain documentation.
I asked IT leaders how they are using generative AI to increase efficiency and simplify complexity management in multicloud architectures.
1. Evaluate cloud service and code portability
Architects have the difficult job of understanding tradeoffs between proprietary cloud services and cross-cloud platforms. For example, should developers use AWS Glue, Azure Data Factory, or Google Cloud Data Fusion to develop data pipelines on the respective platforms, or should they adopt a data integration platform that works across clouds?
Generative AI opens the door to a third option for code creation and translation. Consider a developer writing code to extract, transform, and load (ETL) for one cloud during development, then transitioning to another cloud provider if the architecture changes.
“Managing multicloud is like learning multiple languages from AWS, Azure, Oracle, and others, and it’s rare to have teams that can traverse these environments fluidly and effectively. Plus, services and concepts are not portable among clouds, especially in cloud-native PaaS services that go beyond IaaS,” says Harshit Omar, co-founder and CTO at FluidCloud.
One way to work around this issue is to assign an AI agent to support the developer or architect in evaluating platform selections. This AI agent would review standards, decision criteria, and requirements to recommend solutions and articulate tradeoffs.
“GenAI can help by acting like a devops copilot that understands the user’s intent and design preferences, whether the goal is to optimize for cost, performance, or security, and automatically generates the right infrastructure patterns, says Omar. “Teams spend less time hunting around for multicloud linguists and more time executing on the infrastructure updates and optimizing workloads for the best environments for their business.”
Recommendation: Porting across clouds will be a more realistic option for straightforward configurations and implementations. Using cloud architecture agents and code translation AI tools can help with multicloud portability.
Also see: How to excel in multicloud: The new checklist.
2. Shift from coding to improving resiliency
Developing APIs, applications, and data pipelines is getting easier with genAI tools. According to The 2025 State of AI Code Quality report, 82% of developers use AI coding tools daily or weekly, and 70% report improved code quality. Whereas automation helped IT shift left efforts to focus on customer experience and improving data quality, code generators could help them shift right to improve operational resiliency.
“GenAI is creating a new skill set in which knowledge workers learn to code through prompts and specs, while AI handles cloud-specific mechanics,” says Ed Frederici, CTO of Appfire. “In the future, the true measure of success won’t just be cost savings, but also resilient systems, stronger governance, and empowered teams that understand AI as their toolset rather than the quirks of every cloud, helping them work with greater confidence and impact.”
Recommendation: GenAI can improve resiliency by translating governance policies into cloud-specific implementations.
3. Create multicloud configurations from standard requirements
Standardizing infrastructure and service configurations across different clouds requires expertise in different naming conventions, architecture, tools, APIs, and other paradigms. Look for genAI tools to act as a translator to streamline configurations, especially for organizations that can templatize their requirements.
Jed Dougherty, head of AI architecture at Dataiku, says managing multiple clouds can be an exercise in frustration because each cloud has its own approach to security, access, pricing, and services. Dougherty expects genAI will simplify the translation process: “Imagine genAI automatically translating a complicated AWS IAM role into an Azure Role Definition, or an AWS CloudFormation template into a Google Deployment Manager Configuration.”
Recommendation: Look for genAI tools to build automations and cloud configurations from a single set of requirements, translated into cloud-specific implementations.
4. Simplify operations and automation
CI/CD, infrastructure-as-code, and process automation are key tools for driving efficiency, especially when tasks span multiple cloud environments. Many of these tools use basic flows and rules to streamline tasks or orchestrate operations, which can create boundary cases that cause process-blocking errors. Adding genAI to these automations can enable more robust automations and expand their applicability.
“Managing multicloud environments has traditionally been complex, requiring multiple tools for orchestration, compliance, and cost control, says Mehdi Goodarzi, SVP and global head of AI at Hexaware. “GenAI is now changing this by introducing automation, contextual insights, and intelligent governance into the ecosystem. It simplifies visibility, proactively addresses performance and security issues, and seamlessly orchestrates workloads across providers. Together, this evolution transforms multicloud from a resource-intensive necessity into a powerful enabler of agility, resilience, and business growth.”
Recommendation: Look for genAI capabilities that provide recommended actions in their flows and error handling. Devops can then generate risk and accuracy scores to help decide which actions to automate and which require human intervention.
5. Improve problem resolution with genAI observability
Site reliability engineers (SREs) are expected to perform during outages or performance issues, but they prefer reviewing system performance and proactively recommending improvements to developers before issues arise. Improving application observability has helped SREs, but it’s also created data management challenges.
“Multicloud chaos is fundamentally a data problem, and genAI’s edge is building a unified semantic layer over configs, logs, schemas, and lineage,” says Tobie Morgan Hitchcock, co-founder and CEO of SurrealDB. “Natural-language SRE copilots will infer topology, data gravity, compliance, and cost to propose placements, generate runbooks, and continuously remediate drift across clouds.”
Inconsistent and unstandardized observability data can lead to false alarms and misdiagnosis. But going back to development teams to adhere to data standards and naming conventions has a cost.
“Today’s multicloud operations overwhelm teams with noisy, disconnected alerts that bury the real issues,” says Kyle Campos, CPTO of CloudBolt. “GenAI changes that by interpreting complex, cross-cloud telemetry and surfacing only high-value incidents and optimization opportunities with meaningful context. Results include less alert fatigue, faster resolution, and a measurable boost in day-two operations impact—a critical step in how enterprises build, manage, and continuously optimize across clouds.”
Recommendation: SREs should dedicate time to testing AI agents and other genAI capabilities that simplify and accelerate the use of observability data to improve application performance.
6. Reduce the gap between policy and compliance
Each cloud provider has its own tools for implementing policies and reviewing compliance. When these policies are updated, the security and operations teams must update their implementations one by one, which is inefficient.
“Enterprises stitch together three divergent stacks from AWS, Azure, and GCP, resulting in rising operational complexity, brittle cross-cloud integrations, and cost drag driven by data gravity, egress, and persistent skills shortages,” says Pranay Ahlawat, chief technology and AI officer at Commvault. “GenAI can auto-generate portable IaC/policy to translate intent into native controls and remediate drift across clouds, which can improve compliance and cost posture.”
Recommendation: Regulated businesses will need compliance and security tools that enable the implementation of policies once, then deploy and report across cloud capabilities. Look for genAI capabilities in these tools to support reporting and configuration.
7. Enable continuous finops monitoring
Cloud cost reports with recommendations are available natively in each cloud provider’s reporting tools and in dedicated finops tools that aggregate the relevant data. Organizations with significant variable costs, especially as they scale AI programs, need more than reports to continuously optimize costs and workloads.
“Traditionally, organizations struggle to monitor, provision, and optimize workloads across multiple clouds, as they juggle different APIs, tools, and costs,” says Kevin Cochrane, CMO of Vultr. “GenAI simplifies this by providing intelligent recommendations, predictive scaling, and automated policy enforcement across environments. This reduces operational overhead, minimizes misconfigurations, and ensures workloads run efficiently and cost-effectively, allowing teams to focus on AI innovation rather than managing complexity.”
Recommendation: Smaller organizations should assign the responsibility for reviewing cloud costs to tools such as Azure Advisor, Google Cloud Recommender, and AWS Cost Explorer. Larger organizations should review the genAI capabilities in finops tools designed for financial and engineering end-users.
Can genAI fully address multicloud complexities?
GenAI is currently being embedded across development and operational tools, but it’s not a silver bullet. Regarding multicloud, Ahlawat from Comvault says genAI doesn’t eliminate structural constraints such as data gravity, latency, commitments, or the talent gap. “Organizations still need strong guardrails and platform engineering to manage overall operational complexity,” he says.
We can also expect public clouds to release additional differentiating capabilities, so even as tools powered by genAI simplify today’s challenges, new ones will emerge.
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