DevNops is a DevOps & MLOps consultancy. We give startups and scaling teams enterprise-grade infrastructure — production Kubernetes, LLM & GPU deployments, cloud migrations, and cost optimization that cuts cloud bills by 20–40%.
Fixed-scope engagements and ongoing retainers. Every project ends with documentation and a team that understands what was built.
Get your model to production — fast, reliable, cost-aware.
Find 20–40% savings — or the audit is free.
Production-grade clusters and self-service delivery.
Know your limits before your users find them.
Find the holes before attackers do.
On-prem ⇄ cloud, with zero drama.
See everything. Sleep at night.
Your infra team, on demand.
Transparent, startup-friendly pricing. Prices are starting points — every engagement gets a fixed quote after a free scoping call. Get a custom quote →
Start with a free call. No long contracts to get value.
A 30-minute call to understand your stack, pains, and goals. No pitch.
A concrete assessment with prioritized recommendations and a fixed-scope proposal.
I implement in your environment with IaC, tests, and zero-downtime rollouts.
Documentation, knowledge transfer, and optional ongoing retainer support.
Multi-cloud by default — AWS, GCP, Alibaba, DigitalOcean, and self-hosted on-prem.
Our engineers have run production systems for enterprise-scale companies — from fintech payments to global e-commerce to AI platforms serving millions of users.
For 8+ years, our engineers have designed and operated highly available, cloud-native platforms across AWS, GCP, Alibaba, DigitalOcean, and self-hosted on-prem environments — including systems serving over a million users. We bring that enterprise-scale experience to startups and growing teams.
We focus on the hardest, highest-leverage problems: getting AI/LLM workloads into production on GPUs, scaling Kubernetes without downtime, and cutting cloud bills that have quietly run away. Everything we build is documented, reproducible, and fully yours — we leave your team stronger than we found it.
Tell us what you're running and where it hurts — cost, reliability, scaling, or getting AI into production. You'll get concrete next steps, whether we work together or not. We usually reply within one business day.