Companies that use Elastic

Source: Analysis of 100 job postings that mention Elastic

My analysis shows that Elastic purchasing decisions are heavily concentrated in technology leadership, with Director-level engineering and IT roles accounting for nearly 30% of the positions. Directors of Software Engineering (13%), Advanced Data Analytics and Insights (10%), and Information Technology (6%) are the primary buyers, alongside security leaders like Directors of Cybersecurity and Platform Engineering. These leaders are focused on building scalable cloud infrastructure, strengthening observability and monitoring capabilities, and modernizing legacy systems with distributed architectures.

The hands-on users of Elastic span multiple practitioner roles including Backend Engineers (10%), DevOps/SRE teams (7%), security analysts, and data engineers. These teams use Elastic for log aggregation and analysis, security monitoring through SOC operations, application performance monitoring, and search functionality across massive datasets. I noticed substantial emphasis on integration with complementary tools like Prometheus, Grafana, Splunk, and various cloud platforms (AWS, Azure, GCP), indicating Elastic serves as a central component in broader observability stacks.

The job postings reveal organizations struggling with complexity at scale and visibility gaps. Key phrases include "closing the cloud visibility gap," "detect threats early, respond faster, and understand network behavior at scale," and building "world-class observability stack" with "logging, metrics, tracing, alerting, and visualization." Companies are hiring to achieve "99.999%+ uptime" and handle "millions of daily transactions," showing Elastic addresses critical needs around system reliability, security threat detection, and operational excellence in high-stakes production environments.

Source: Analysis of tech stacks from 667 companies that use Elastic

I noticed that companies using Elastic tend to be design-forward, data-driven organizations that invest heavily in collaborative planning and user research. The presence of Figma Organization Plan, Miro, and Lucidchart shows these are teams that prioritize visual collaboration and sophisticated design workflows. Combined with Elastic's search and analytics capabilities, this suggests companies building complex products where understanding user behavior is critical to success.

The pairing of Elastic with PostHog Enterprise and UserTesting is particularly revealing. These companies aren't just collecting data, they're running comprehensive product analytics and user research programs. Elastic likely powers the backend infrastructure that handles massive amounts of log data and user events, while PostHog and UserTesting provide the product insights layer. Cursor's strong presence suggests these are engineering-heavy teams working with modern development tools, needing robust search and observability to manage increasingly complex systems. The Miro correlation tells me these companies run extensive planning and retrospective sessions, typical of organizations with mature product development processes.

The full stack reveals mid-to-late stage companies with sophisticated product-led growth motions. They're investing in enterprise collaboration tools and comprehensive user research, which indicates both the budget and organizational maturity to prioritize product excellence. These aren't early startups hacking together MVPs. They're scaling companies with dedicated product, design, and engineering teams that need to coordinate across multiple workstreams. The emphasis on visual collaboration and user testing suggests they're likely B2B SaaS companies or consumer tech firms where product quality directly drives retention and growth.

Source: Analysis of Linkedin bios of 667 companies that use Elastic

I noticed that Elastic's customers span an incredibly diverse range of industries, but they share a common thread: they're in the business of managing complexity at scale. These aren't simple operations. I see financial services companies processing millions of transactions, healthcare organizations synthesizing patient data across systems, logistics firms tracking goods globally, retailers managing inventory and customer experiences, and tech companies building platforms that serve thousands or millions of users. What strikes me is how many describe themselves as handling "comprehensive," "end-to-end," or "integrated" solutions for complex problems.

These are predominantly mature, established enterprises. I see Fortune 500 companies, organizations with thousands of employees, and firms that have been operating for decades. Even the younger companies in the mix, the Series B and C stage startups, are already serving substantial customer bases and processing significant data volumes. The employee counts tell the story: most have hundreds or thousands of people, with many exceeding 1,000 employees. These aren't experimental startups. They're companies with proven business models that need industrial-strength infrastructure.