Solutions & Services · AI Infrastructure & MLOps
The platform underneath your models, agents and applications.
AI initiatives stall when every project reinvents its own infrastructure. Kimbodo designs, builds and operates the platform layer — Kubernetes, serverless or managed services on AWS, GCP and Azure — with infrastructure-as-code, CI/CD, model serving and full observability. One governed foundation that every AI workload after the first one ships faster on.
One governed platform: automated delivery, scalable serving and observability — so the second, third and tenth AI project ship in days, not quarters.
What Kimbodo Builds and Operates
Platform Foundations
- Kubernetes clusters (EKS, GKE, AKS) or managed platforms
- Terraform infrastructure-as-code and landing zones
- Networking, identity and secrets management
- GPU capacity planning and autoscaling
MLOps & Serving
- Model serving: Vertex AI, Bedrock, SageMaker, NVIDIA NIM, vLLM
- CI/CD for models, prompts and agents
- Feature and data pipelines with orchestration
- Retraining workflows and drift detection
Operations & Governance
- Observability: logging, tracing, evaluations, SLOs
- Cost governance and model-spend controls
- Security hardening and compliance controls
- Managed operations via Kimbodo Managed Services
Timeline & Engagement Model
Platform foundations typically take 6–8 weeks to stand up; full MLOps builds with serving, pipelines and observability run 8–14 weeks. Delivery is fixed-scope, followed by optional managed operations. Need Posit infrastructure specifically? Kimbodo Launch deploys complete data-science platforms on your cloud automatically.
Proof, Not Promises
- Google Cloud Kubernetes cluster with Vertex AI for high-volume advertising funnels — production AI on Kubernetes at scale
- Large-scale data pipeline for real-time product recommendations — streaming data infrastructure in production
- Cloud foundations — OpCos setup with Terraform — multi-entity infrastructure-as-code done properly
Buyer Questions
Kubernetes, serverless or managed platform — which do we need?
It depends on workload shape, team maturity and compliance needs — steady high-volume inference favors Kubernetes; bursty workloads favor serverless; small platform teams favor managed services. The discovery phase produces a concrete recommendation with cost projections for each option.
What does AI infrastructure cost to build?
Foundations typically start around $30,000; complete platforms with serving, pipelines and observability usually land between $50,000 and $160,000 depending on scope. The estimator gives you a range immediately, and running costs are modeled during discovery.
Can you run it after you build it?
Yes. Kimbodo Managed Services operates the platform — patching, scaling, monitoring, incident response — or we hand over to your team with documentation and training. Most clients choose a transition period.
Stop rebuilding infrastructure for every AI project
Describe your platform needs; get a preliminary range, timeline and reference architecture immediately.