Platform Engineering • AI Infrastructure • MLOps • SRE
Engineering the infrastructure behind reliable, scalable software.
OrbitronOps helps engineering teams solve complex cloud, Kubernetes, platform, reliability and AI infrastructure problems with senior, hands-on engineering expertise.
The high-value alternative to the bad hire.
The problem
Infrastructure gets complicated faster than teams expect.
Growth adds services, clusters, pipelines, models and cloud accounts. Without deliberate engineering, complexity compounds.
Rising Cloud Costs
Infrastructure grows faster than visibility into where money is going.
Kubernetes Complexity
Kubernetes provides flexibility but introduces operational complexity.
Slow Deployments
Manual processes and fragmented tooling slow engineering teams down.
Production Incidents
Teams become trapped in reactive firefighting.
ML Stuck in Experimentation
Models work in notebooks but struggle to become reliable production systems.
AI Infrastructure
AI workloads introduce new requirements for compute, scheduling, inference and observability.
Developer Friction
Engineers spend time waiting on infrastructure instead of shipping products.
Services
Engineering services for modern infrastructure.
From Kubernetes and Terraform to GitOps, observability and GPU scheduling — senior engineers who design it and build it.
AI Infrastructure
Build infrastructure your AI workloads can actually scale on.
GPU infrastructure, AI workloads on Kubernetes, model serving, inference, scheduling and optimization.
Explore AI InfrastructurePlatform Engineering
Build a platform developers actually want to use.
Internal developer platforms, Kubernetes platforms, self-service infrastructure, Terraform and GitOps.
Explore Platform EngineeringMLOps
Move machine learning from experimentation to production.
ML platforms, pipelines, deployment, model serving, monitoring and production ML infrastructure.
Explore MLOpsSRE & Reliability
Turn reliability into an engineering discipline.
SLOs, SLIs, error budgets, observability, incident reduction, automation and reliability transformation.
Explore SRE & ReliabilityAIOps
Put AI to work on your operations data.
AI-assisted operations: event correlation, anomaly detection, alert noise reduction and intelligent incident response.
Explore AIOpsDevOps & Cloud Modernization
Modernize the infrastructure behind your software.
CI/CD, infrastructure as code, Kubernetes, GitOps, cloud architecture and automation.
Explore DevOps & Cloud ModernizationCloud Architecture
Cloud architecture designed for scale, reliability and cost.
Architecture reviews, modernization, migrations, security posture and reliability on AWS, GCP and Azure.
Explore Cloud ArchitectureDifferentiation
Not another consulting layer. Engineering expertise.
Many companies don't need another team of consultants. They need someone who can understand the architecture, identify the bottleneck, build the solution and leave the engineering team stronger.
Don't add another layer of people. Solve the infrastructure problem.
Discuss Your InfrastructureTHINK
Architecture, assessment and technical strategy.
BUILD
Hands-on implementation by experienced engineers.
ENABLE
Automation, documentation and knowledge transfer.
Why OrbitronOps
Senior engineering. Practical delivery.
Senior Expertise
Direct access to experienced infrastructure engineering expertise.
Hands-On Delivery
Architecture is followed by implementation.
Technical Depth
Deep focus on cloud, Kubernetes, platforms, ML and reliability.
Vendor Neutral
Technology choices follow the engineering problem.
Outcome Focused
Focus on reliability, velocity, cost and engineering effectiveness.
Knowledge Transfer
Your team should understand and own what gets built.
Outcomes
What better infrastructure changes.
Lower Cloud Spend
Identify infrastructure waste and improve resource efficiency.
Faster Delivery
Automate deployment and infrastructure workflows.
Higher Reliability
Use measurable reliability engineering practices.
Less Toil
Automate repetitive operational work.
Better Developer Experience
Give developers self-service infrastructure and better workflows.
Production-Ready ML
Build repeatable systems for deploying and operating ML.
Scalable AI
Create infrastructure capable of supporting growing AI workloads.
How we engage
A transparent, four-step process.
- 01
Discover
Understand the architecture, constraints and engineering bottlenecks.
- 02
Architect
Define the target architecture and implementation roadmap.
- 03
Build
Implement infrastructure, automation, platforms and reliability improvements.
- 04
Enable
Transfer knowledge and create sustainable engineering practices.
Insights
Engineering notes from the field.
What Is Platform Engineering?
Platform engineering treats infrastructure as an internal product. Here is what that means in practice — and what it doesn't.
ReadInternal Developer Platforms: What Engineering Teams Actually Need
Most IDP initiatives start with a portal. The successful ones start with the paths behind it.
ReadKubernetes Cost Optimization
Requests, limits, bin-packing and autoscaling: where Kubernetes cost actually hides.
ReadLet's solve your infrastructure problem.
Tell us what you're building and where infrastructure is getting in the way. You'll talk directly to a senior engineer.