GenAI & Multicloud: Solving Data Chaos for Better Observability | SurrealDB & CloudBolt

The increasing complexity of multicloud environments, coupled with the explosive growth of generative AI (GenAI), is creating a significant data observability challenge for enterprises. While GenAI promises to streamline operations and improve incident response, realizing those benefits hinges on the ability to effectively manage and interpret the vast amounts of data generated across disparate cloud platforms. The core issue isn’t a lack of data, but rather a lack of unified understanding – a semantic layer capable of making sense of configurations, logs, schemas, and data lineage.

According to Tobie Morgan Hitchcock, co-founder and CEO of SurrealDB, today’s multicloud chaos is fundamentally a data problem. Hitchcock posits that GenAI’s strength lies in building a unified semantic layer over this fragmented data landscape. “Natural-language SRE copilots will infer topology, data gravity, compliance, and cost to propose placements, generate runbooks, and continuously remediate drift across clouds,” he explains. This suggests a future where AI-powered tools proactively manage multicloud infrastructure, optimizing performance and reducing operational overhead.

The problem Hitchcock describes isn’t new. Enterprises have been grappling with multicloud complexity for years. However, the scale and velocity of data generated by modern applications, particularly those leveraging GenAI, are exacerbating existing challenges. Inconsistent and unstandardized observability data leads to a common issue: false alarms, and misdiagnosis. Addressing this requires a return to fundamental data governance principles – establishing standards and naming conventions for data across the organization. However, as the provided material notes, enforcing these standards comes at a cost, potentially impacting development velocity.

The promise of GenAI in this context isn’t simply about processing more data, but about processing it *smarter*. Kyle Campos, CPTO of CloudBolt, highlights this shift. Currently, multicloud operations often overwhelm teams with a deluge of alerts, many of which are irrelevant or misleading. “GenAI changes that by interpreting complex, cross-cloud telemetry and surfacing only high-value incidents and optimization opportunities with meaningful context,” Campos says. This ability to filter noise and prioritize critical issues is crucial for reducing alert fatigue and accelerating incident resolution.

The benefits extend beyond simply reacting to problems. GenAI-powered observability tools can also proactively identify optimization opportunities, leading to measurable improvements in day-two operations – the ongoing management and refinement of applications after initial deployment. This is a critical step for enterprises seeking to maximize the value of their cloud investments.

Recent developments in the observability space, as highlighted by Microsoft’s announcements at Build 2024, demonstrate the industry’s focus on addressing these challenges. Microsoft is extending its Azure Monitor pipeline capabilities to the edge, enabling high-scale data ingestion with centralized configuration management. This builds upon existing support for OpenTelemetry, an open-source observability framework, and introduces Azure Monitor pipeline, a native OpenTelemetry offering. Further extensions are planned through Azure Monitor Agent and direct cloud ingestion of OpenTelemetry Protocol (OTLP) signals. This move signifies a broader trend towards standardized data collection and analysis, which is essential for effective GenAI-powered observability.

Microsoft is enhancing the user experience of Azure Monitor logs, aiming to make log data more accessible to a wider range of users within an organization. Improvements to the integration with Microsoft Copilot in Azure allow users to interact with logs using natural language, simplifying the process of querying and analyzing data. This lowers the barrier to entry for non-technical users, empowering them to derive insights from observability data without requiring specialized skills.

The integration of GenAI with multicloud architectures isn’t without its complexities. A report from MIT, as referenced in August 2025, found that 95% of GenAI pilots fail, often because companies avoid addressing underlying friction points. This suggests that simply deploying GenAI tools isn’t enough; organizations must also be willing to address the organizational and technical challenges associated with data integration, standardization, and governance.

The New Stack’s reporting indicates that as enterprises adopt more AI, the underlying problems will only multiply. This underscores the need for a holistic approach to observability, one that combines robust data collection and analysis with intelligent automation and a user-friendly interface. The ability to trace data flows and understand dependencies is also becoming increasingly important, as highlighted in a recent YouTube discussion on data observability.

The convergence of multicloud architectures and GenAI represents a significant opportunity for enterprises to optimize their operations and unlock new levels of efficiency. However, realizing this potential requires a strategic investment in data observability, a commitment to data standardization, and a willingness to embrace the transformative power of AI-powered tools. The future of multicloud management will likely be defined by those who can effectively harness the power of GenAI to make sense of the ever-increasing complexity of their data environments.