Companies that use Helm

Source: Analysis of job postings that mention Helm (using the Bloomberry Jobs API)

My analysis shows that Helm buyers are primarily engineering and infrastructure leaders making strategic technology decisions. Directors and VPs of Engineering represent key decision makers, alongside Platform Architecture leaders and DevOps/SRE managers who evaluate container orchestration tools. These buyers prioritize modernization, scalability, and automation. One posting seeks someone to "define and execute the roadmap for data engineering" while another needs leaders to "drive the strategic integration of AI across engineering practices." Their focus centers on building resilient, cloud-native platforms that enable rapid deployment.

The day-to-day users are DevOps Engineers, SRE teams, and Platform Engineers working hands-on with Kubernetes clusters. They manage "packages Helm and development of charts" and handle "deployment and maintenance of applications via ArgoCD for the GitOps approach." These practitioners automate CI/CD pipelines, manage containerized workloads across hybrid environments, and ensure high availability. Multiple postings require "Kubernetes, Docker, Helm basics" as core competencies for infrastructure roles.

The recurring pain points reveal companies struggling with legacy system modernization and operational complexity. Organizations seek to "balance data utility with strict privacy compliance" and build systems that are "observable, resilient and horizontally scalable." One posting captures the urgency perfectly, needing someone to "turn chaos into clarity" while another emphasizes "reducing risk, operational efficiencies, compliance, and competitive advantage." These companies want Helm to simplify complex deployments while maintaining security and governance standards.

Source: Analysis of Linkedin bios of 786 companies that use Helm

I noticed that Helm users are predominantly infrastructure and platform companies building technical products for other developers and enterprises. These aren't consumer apps or simple SaaS tools. They're companies creating databases (VictoriaMetrics, Dragonfly, NebulaGraph), observability platforms (Grafana Labs, Dynatrace, Edge Delta), cloud infrastructure (Voltage Park, VAST Data, Utho), developer tools (Docker, Domino Data Lab), and AI/ML platforms (Voxel51, Embedded LLM). Many are building what they call "platforms," "solutions," or "infrastructure" that other technical teams depend on.

These companies span the full maturity spectrum, but most are past the earliest startup phase. I saw Series A through Series E funding rounds, post-IPO companies like Dynatrace and Veritone, and established enterprises like EPAM with 60,000+ employees. However, a significant portion are in that 50-200 employee range, suggesting growth-stage companies building momentum. The presence of companies backed by top-tier VCs like Sequoia and Andreessen Horowitz, alongside bootstrapped open source projects, shows Helm appeals across funding models.

Source: Analysis of tech stacks from 786 companies that use Helm

I noticed that Helm users are infrastructure-focused engineering organizations with mature DevOps practices and a strong emphasis on security and automation. The extreme correlation with Terraform (4906x more likely) combined with GitHub Actions tells me these companies are deeply committed to infrastructure as code and treating their entire deployment pipeline as something reproducible and version-controlled. They're likely building cloud-native products or services where Kubernetes orchestration is central to their operations.

The pairing of Terraform with Helm makes perfect sense because these teams are managing both infrastructure provisioning and application deployment as complementary layers of their stack. When I see Dependabot and GitHub Advanced Security appearing so frequently alongside these tools, it reveals a proactive approach to supply chain security. These aren't teams that just deploy containers, they're systematically scanning for vulnerabilities and keeping dependencies current. The Claude Code correlation is particularly interesting because it suggests these engineering teams are early adopters willing to experiment with AI coding assistants to maintain their complex infrastructure codebases more efficiently.

My analysis shows these are product-led companies, likely in growth or scale-up stages. The emphasis on automation, security scanning, and verified GitHub organizations points to teams that have moved past scrappy startup mode into building robust, auditable systems. They're probably somewhere between Series A and mature growth stage, with engineering teams large enough to justify sophisticated tooling but still maintaining a culture of automation over manual processes. The verified organization status suggests they care about their public technical reputation.