Companies that use MinIO

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

My analysis shows MinIO purchasing decisions are split between engineering leadership and platform teams. Directors of Engineering and Data comprise roughly 7-10% of roles, focused on building scalable storage infrastructure for AI workloads and cloud modernization. These leaders are hiring for strategic initiatives around object storage, data platform architecture, and multi-petabyte systems. Their priorities center on cost optimization, cloud-native infrastructure, and supporting AI/ML pipelines at scale.

Day-to-day MinIO users are predominantly DevOps engineers (17%), backend engineers (19%), and data engineers (12%). These practitioners deploy MinIO alongside Kubernetes clusters, build data ingestion pipelines, manage S3-compatible storage for AI training datasets, and integrate it with streaming platforms like Kafka. I noticed frequent mentions of MinIO appearing in stacks with PostgreSQL, Redis, monitoring tools, and container orchestration platforms, suggesting it serves as the object storage layer in complex distributed systems.

The job postings reveal companies pursuing aggressive scale and cost reduction. One posting seeks someone to "design multi-petabyte AI/ML storage systems" and achieve "30-50% savings through intelligent tiering." Another emphasizes "cost-effective, searchable, and regulatory-compliant storage tiers" for enterprise archiving. A third describes building infrastructure that "powers AI at scale" with "high-performance GPU data centers." These phrases highlight MinIO's role in enabling AI workloads while controlling infrastructure costs and maintaining performance at massive scale.

Source: Analysis of Linkedin bios of 1,168 companies that use MinIO

I noticed MinIO's users span an incredibly diverse range of industries, but they share a common thread: they're building or operating technology-intensive systems that handle significant data. These aren't just "tech companies." I see food and beverage operations like AB InBev and Casa de Bolos running at scale, healthcare organizations managing patient data, financial services companies processing transactions, and municipalities digitizing public services. What unites them is that regardless of their primary business, they're all running data-heavy operations that require robust infrastructure.

These companies cluster in the growth and established mid-market segments. The employee counts tell the story: most fall between 50-500 employees, with a sweet spot around -200. I see very few early-stage startups and very few massive enterprises. The funding data is sparse, which suggests these are often bootstrapped or past their venture-dependent phase. They're at the stage where infrastructure decisions really matter because they're handling real volume but don't have unlimited engineering resources.

Source: Analysis of tech stacks from 1,168 companies that use MinIO

I noticed that MinIO users are predominantly infrastructure-focused companies building modern, self-hosted data platforms. The extremely high correlation with Portainer (694.4x) and Rancher (427.2x) tells me these are teams that prioritize container orchestration and want direct control over their infrastructure rather than relying purely on managed cloud services. They're likely running Kubernetes clusters and need scalable object storage that integrates seamlessly with their containerized environments.

The pairing of MinIO with N8N (106.3x more likely) is particularly revealing. N8N is a self-hosted workflow automation tool, which suggests these companies are building custom data pipelines and automation workflows that need reliable object storage. When I see this combined with Grafana for monitoring, it points to companies processing significant data volumes who need to observe their entire stack. The GitLab correlation reinforces this picture: these teams are practicing DevOps, running CI/CD pipelines that likely store artifacts and build outputs in MinIO. LiteLLM appearing in the stack, despite being a small sample, suggests some of these companies are experimenting with AI applications that require substantial storage for model artifacts and training data.

The full stack reveals engineering-led organizations, probably in growth or expansion stages. They're making build-versus-buy decisions that favor open source and self-hosted solutions, which means they have sophisticated technical teams but are likely cost-conscious. These aren't early-stage startups without infrastructure expertise, nor are they enterprise giants locked into AWS or Azure storage. They're technical product companies that value flexibility and cost control.