Companies that use Kibana

Source: Analysis of 100 job postings that mention Kibana

I noticed that Kibana purchasing decisions come primarily from engineering leadership, with Directors of Software Engineering (17%), Directors of DevOps (10%), and Directors of IT (6%) making up the core buying committee. These leaders are focused on modernization initiatives, building observable systems, and ensuring platform reliability. They're hiring heavily for cloud migration, microservices architecture, and implementing modern monitoring stacks to support business-critical operations.

The day-to-day users are predominantly Backend Engineers (11%) and DevOps/SRE professionals (10%) who rely on Kibana for operational visibility. These practitioners use it alongside the broader ELK stack for log aggregation, real-time monitoring, troubleshooting production issues, and tracking system health across distributed environments. They're working with containerized applications, managing CI/CD pipelines, and maintaining high-availability services that demand constant observability.

The job postings reveal companies struggling with scale and complexity. I saw repeated emphasis on "monitoring and troubleshooting," "ensuring high availability," and "real-time data processing." One posting specifically called for "monitoring availability and taking a holistic view of system health," while another needed expertise in "observability and proactive prevention." A third highlighted "monitor and understand the development calendar" and manage blockers. These organizations need visibility into increasingly complex, distributed systems to prevent downtime and maintain service quality.

Source: Analysis of Linkedin bios of 1,111 companies that use Kibana

I noticed that Kibana users span an incredibly wide range of industries, but there's a common thread: they're organizations dealing with complex data operations at scale. These aren't just tech companies. I'm seeing government agencies managing national programs, logistics companies tracking shipments across continents, financial services firms processing transactions, healthcare platforms coordinating patient care, and e-commerce businesses managing inventory. What unites them is operational complexity. They're running systems where data visibility and real-time monitoring matter to their core business.

These companies cluster in the growth stage. I'm seeing lots of 11-50 and 51-200 employee counts, with funding rounds typically at seed or Series A/B levels. There are some larger enterprises mixed in, but the sweet spot appears to be scaling companies that have achieved product-market fit and are now expanding operations across regions or adding complexity to their tech stacks. They're past the MVP stage but still building out infrastructure.

Source: Analysis of tech stacks from 1,111 companies that use Kibana

I noticed that companies using Kibana are building serious engineering operations with modern DevOps practices at their core. The strong correlation with tools like Jenkins, Argo CD, and Rancher tells me these are technology companies or tech-forward enterprises that have moved beyond basic infrastructure into sophisticated, often containerized environments. They're not just shipping software occasionally. They're running continuous deployment pipelines and need deep observability into what's happening across their systems.

The pairing with Grafana is particularly telling. These companies aren't satisfied with a single monitoring solution. They're combining Kibana's log analysis strengths with Grafana's metrics visualization, which suggests they're dealing with complex distributed systems where different tools serve different observability needs. The Argo CD correlation reinforces this, since that's a Kubernetes-native deployment tool. Companies running Argo CD are practicing GitOps, and they need Kibana to debug when those automated deployments cause issues. SonarQube appearing frequently makes sense too. These teams care about code quality before deployment and system health after deployment.

My analysis shows these are likely growth stage or mature technology companies with dedicated platform engineering teams. They're product-led in the sense that their products are technical, but they're investing heavily in internal tooling and developer experience. The presence of Metabase alongside all these DevOps tools is interesting. It suggests data-driven decision making extends beyond engineering into the broader organization.