Harnessing AI with Azure Cosmos DB: Insights from Cosmos Conf 2026

At Cosmos Conf 2026, the central theme was clear: artificial intelligence is not merely another workload—it's fundamentally transforming how applications and data platforms are built. This year's event, highlighted by a keynote from Azure Cosmos DB VP Kirill Gavrylyuk, showcased three major shifts driving this evolution. From the flexibility needed for semi-structured data to the speed of AI-driven development and the rise of semantic search as a core query operator, the conference offered a glimpse into the future of global-scale applications. In this Q&A, we explore the key trends and real-world examples, including how OpenAI uses Azure Cosmos DB to operate at planet scale.

What were the three key AI shifts highlighted at Cosmos Conf 2026?

The opening keynote by Kirill Gavrylyuk outlined three pivotal shifts reshaping application architecture with Azure Cosmos DB. First, AI demands flexible, semi-structured data as a foundation—databases must handle prompts, memory, and context that evolve over time, moving from systems of record to systems of reasoning. Second, AI dramatically accelerates development pace. Coding agents and iterative workflows require databases that offer serverless form factors, instant scalability, and agent-friendly interfaces, freeing developers from rigid schemas. Third, semantic search is now a first-class query operator. Modern AI applications need vector search, full-text search, hybrid search, and semantic ranking—not as add-ons but as core capabilities. These shifts were echoed across every customer story, emphasizing that retrieval, reasoning, and real-time context must be tightly integrated.

Why is semi-structured data foundational for AI applications?

AI applications don't operate on rigid, predefined schemas. Instead, they work with prompts, memories, and contextual data that are inherently semi-structured and constantly evolving. This reality forces a fundamental change in how databases behave. A data platform can no longer be just a system of record; it must become a system of reasoning that learns, adapts, and generates outcomes. Azure Cosmos DB's schema-agnostic nature, powerful indexing, and support for various data models make it ideal for such workloads. As Gavrylyuk emphasized, flexibility isn't a luxury—it's what enables teams to move at AI speed. Without it, developers would spend too much time wrestling with schema changes instead of building intelligent features. Semi-structured data models allow rapid iteration and seamless integration of new AI capabilities, which is why they've become foundational for next-gen applications.

How is AI accelerating the pace of development with Azure Cosmos DB?

AI, especially coding agents, is revolutionizing how software is built. Developers are iterating faster, shipping more frequently, and scaling from zero to massive usage instantly. This accelerated pace demands a database that can keep up without imposing constraints. As Kirill Gavrylyuk noted, strict schemas are no longer viable. Azure Cosmos DB meets this need with a serverless form factor, instant and limitless scalability, advanced integrated caching, and agent-friendly interfaces. Teams can prototype quickly in development and then seamlessly scale to production without re-architecting. The database's multi-model support and global distribution further enable rapid deployment across regions. At Cosmos Conf, this pattern was evident: teams using AI to build applications could iterate in hours rather than weeks, thanks to the flexibility and performance of Azure Cosmos DB. It transforms database management from a bottleneck into an accelerator for AI-driven innovation.

Why is semantic search becoming a first-class query operator?

Modern AI applications require more than traditional keyword matching. They need to understand context and meaning. Semantic search—encompassing vector search, full-text search, hybrid search, and semantic ranking—has evolved from a nice-to-have add-on to a core query operator. This shift reflects how applications now retrieve, reason, and provide real-time context. Azure Cosmos DB integrates these capabilities natively, allowing developers to combine precise full-text queries with meaning-based vector searches in a single request. The result is richer, more relevant results for users. At Cosmos Conf 2026, this pattern was prominent: teams building AI applications used hybrid search to merge structured data retrieval with AI-powered embedding similarity, enabling features like intelligent chatbots, recommendation engines, and knowledge bases. Semantic ranking further refines outputs by prioritizing the most contextually appropriate results. As AI becomes embedded in every application, semantic search is no longer optional—it's essential.

How does OpenAI leverage Azure Cosmos DB for massive scale?

Speaking at Cosmos Conf, Jon Lee of OpenAI shared how the company operates at an enormous scale, processing trillions of transactions and petabytes of data. He reinforced that the key isn't just scale but the ability to evolve quickly. OpenAI's systems must scale instantly from zero to millions of queries per second and from zero bytes to petabytes of data. They need schema-less design to rapidly onboard new use cases and enable thousands of developers to iterate simultaneously without schema conflicts. Azure Cosmos DB provides this foundation with its global distribution, multi-region writes, and flexible data model. Jon emphasized that being able to scale from zero to massive usage seamlessly is critical for AI applications that can go viral overnight. The database's SLA-backed performance and low latency also ensure that users get real-time responses. OpenAI's example shows how a modern data platform can support both unprecedented scale and rapid innovation, a combination that is essential for AI-driven services.

What does 'flexibility at planet scale' mean for modern app development?

'Flexibility at planet scale' describes the ability to adapt quickly to changing AI workloads while operating globally. Modern apps—especially those powered by AI—must handle unpredictable traffic spikes, evolving data formats, and diverse query patterns. Azure Cosmos DB delivers this through serverless capacity, instant scalability, and a multi-model approach that supports documents, graphs, key-value, and wide-column stores. Developers can start small, iterate with schema-less designs, and then scale to any size without downtime or reconfiguration. Global distribution ensures data is close to users anywhere, while integrated caching and high-performance indexing keep response times low. This flexibility is not just a technical feature; it enables business agility. Teams can experiment with new AI features, test them in production, and roll out changes rapidly. As seen at Cosmos Conf, organizations like OpenAI rely on this capability to innovate continuously while serving millions of users worldwide. In the age of AI, flexibility at planet scale is a competitive advantage.