We built MLPipeX because we spent years watching brilliant data scientists wait weeks to see their models in production. The tooling was either too complex, too fragile, or locked behind enterprise contracts. We decided to fix that.
Alex Novak and Tomas Blaha, frustrated by broken ML deployment pipelines at their previous companies, begin building MLPipeX as a side project in Prague.
MLPipeX launches in private beta. Ten engineering teams across Europe use the platform to deploy over 200 models in the first six months.
MLPipeX opens to the public. The team grows to 12 people. Monthly active deployments cross 10,000. Drift monitoring and auto-scaling ship as core features.
MLPipeX processes over 2 billion inference requests per month across customer deployments. Enterprise plan and SOC 2 Type II certification launch.
We ship features our customers actually need, not features that look good in a pitch deck. The product roadmap is driven by real deployment pain points.
Your models serve real users. We take that responsibility seriously. 99.95% uptime SLA, transparent incident communication, no surprises.
No lock-in. MLPipeX integrates with the tools you already use and exports your data in open formats. Your models, your infrastructure, your choice.
"The best MLOps platform is the one your team doesn't have to think about. We're building toward that invisible reliability."— Alex Novak, CEO & Co-Founder, MLPipeX
ML