When my co-authors Csilla Zsigri, Adrienn Lawson, and I set out to map the state of global data infrastructure on the fifth anniversary of OpenSearch, we sought to capture more than just market statistics. We wanted to document how a prominent open source software project is maturing alongside the rapid, pervasive shift toward enterprise artificial intelligence. Throughout this process, our research team worked closely with Bianca Lewis and the OpenSearch Software Foundation leadership team to ensure our empirical approach addressed the community's most critical strategic questions.
We’re very excited that this report, The 2026 Data Infrastructure Report: AI, Governance, and OpenSearch Adoption Trends, has now been published, coinciding with OpenSearchCon in San Jose, CA. Here are a few of the report’s featured takeaways:
Running or planning to run AI workloads is standard practice for at least 83% of organizations across every region, reaching over 90% among large enterprises. As artificial intelligence transitions from early-stage experimentation into core operational workloads, the underlying data platforms that search, index, observe, and retrieve information have become just as vital as model selection itself.
Our quantitative findings show that Generative AI and LLM-powered applications are the leading data infrastructure use case (82%), drawing on log and operational data (64%), transactional data (59%), and internal knowledge repositories (55%). Against this backdrop, 84% of organizations report that single-platform access to their enterprise data is critical for AI execution. This operational requirement sharpens the market's preference for consolidated platforms capable of unified retrieval, observability, and vector storage within secure enterprise boundaries.
Awareness of OpenSearch jumped from 68% in 2024 to 89% in 2026. This significant breakthrough in market visibility is particularly pronounced among technology decision-makers and mid-to-large enterprises, reflecting a growing recognition of the project's identity beyond its origins.
When practitioners describe OpenSearch in their own words, they identify it primarily as a unified search and retrieval platform (43 mentions) paired with analytics and observability capabilities (36 mentions). Open source identity, vendor independence, and community governance also feature prominently in practitioner descriptions (31 mentions). This widespread awareness indicates that OpenSearch has successfully established a clear brand identity as a mature, community-governed data infrastructure platform.
OpenSearch production adoption nearly doubled between 2024 and 2026, moving from 19% to 36%, with another 36% of organizations actively testing or planning adoption. This indicates that the active evaluation pipeline today is as large as the established production footprint itself.
While adoption numbers show strong upward momentum, deployment depth reveals that 60% of active setups remain experimental or production-but-replaceable, compared to 36% that are deeply embedded or mission-critical. Qualitative conversations with engineering leaders confirmed that non-adopters face practical, solvable barriers centered on operational expertise and education rather than fundamental architectural objections. The primary growth objective for the ecosystem is helping organizations convert active trials into deeply embedded, mission-critical infrastructure.
While the quantitative findings of our research established a strong, empirical baseline for the global data infrastructure landscape, understanding the true momentum of data infrastructure required moving beyond the numbers to engage directly with technical leaders, enterprise architects, and enterprise technologists and practitioners on the ground. Conducting the qualitative fieldwork provided an invaluable, direct perspective on how organizations are actively evaluating vendor independence, security guardrails, and architectural sustainability in practice. I am immensely grateful to the people who participated in this process.
Across our qualitative discussions, several overarching dynamics emerged regarding the practical deployment of modern data infrastructure. Technical decision-makers emphasized that open governance and neutral project stewardship serve as essential catalysts for enterprise confidence. Having professional project management and clear, vendor neutral stewardship provides organizations with the assurance needed to adopt and scale open source platforms alongside commercial software options. And this, the OpenSearch project has plenty of.
At the same time, practitioners highlighted the continuous balance between platform standardization and specialized flexibility. While organizations increasingly lean toward unified platforms to minimize operational overhead and control total cost of ownership, engineering teams facing unique, high-scale, or complex query challenges continue to assess specialized point tools. Ultimately, enterprise readiness depends on moving past legacy technical debt and embracing modular architectures that meet artificial intelligence demands.
Our combined findings show that the primary challenge facing OpenSearch is no longer initial discovery, but deepening deployment. Currently, 60% of active deployments remain experimental or production-but-replaceable, rather than deeply embedded. As open source communities remain the primary discovery channel for technical teams, the OpenSearch Software Foundation is uniquely positioned to help organizations convert experimental trials into mission-critical infrastructure. By addressing operational expertise gaps and advancing modular search architectures, OpenSearch can solidify its position as vendor neutral core infrastructure for the next generation of enterprise AI.