AI infrastructure designed for production.
Most platforms weren't built for GPUs, for models, or for the auditors now asking about both. We engineer and operate the Kubernetes foundation that takes AI to production, and keeps everything else on it running.
GPU scheduling & sharing
Fractional GPUs with enforced limits, MIG where you need hard isolation, and DRA as the native API, so one card can serve several workloads.
Model serving
Serving engines tuned to your models and traffic, multi-node replicas where weights outgrow a node, and autoscaling on queue depth instead of CPU.
Inference routing
Gateways that route on KV-cache and queue metrics, serve LoRA adapters, and put many models behind one OpenAI-compatible endpoint.
AI observability
Time to first token, time per output token, and GPU telemetry side by side, with alerts on what your users actually feel.
GPU cost & capacity
Allocation versus real utilization by team and model, chargeback that holds up in a budget review, and a capacity plan grounded in measured demand.
Sovereign & air-gapped
Mirrored registries, offline model stores, signed artifacts, and admission policy, for AI that has to stay inside your boundary.
Why Todea
One engineering partner for how the stack works together.
Production problems rarely stay inside one component. Three commitments shape every engagement, for AI workloads and everything else you run.
Responsibility across the platform
When a problem spans the network, the mesh, the cluster, and your workloads, we lead the investigation and the fix, including escalations to vendors and upstream projects.
How we handle incidentsTechnology selected around your needs
We weigh your requirements, existing investments, team skills, and costs, explain the trade-offs, and keep the tools you already run when they fit.
How we make decisionsExperienced platform operations
Our engineers configure, maintain, upgrade, and improve the platform and respond to incidents, so you don't have to build an internal platform team.
What we operate
Where we start
Four situations we're built for.
If one of these sounds like your team, there is a defined engagement for it, and each one starts with a conversation with an engineer.
"Our GPUs are fully allocated and mostly idle."
Whole-card requests lock up accelerators that do a fraction of the work they could. We measure real utilization, then introduce sharing, queueing, and bin-packing, so you recover capacity before you buy more.
AI infrastructure"The model works in the demo. Production is another story."
Latency targets, autoscaling, rollouts, and routing that understands the KV cache: we build the serving platform under your models and hand it over documented, yours to run.
Implementation Project"When something breaks, we get passed between providers."
Each provider reports its own part as healthy while your application still fails. We take responsibility across the components: we lead the investigation, deal with vendors and upstream projects, and see the fix through.
Managed Platform"We built the platform, but we can't keep up with operating it."
Our engineers take over upgrades, patching, incident response, and everyday changes, so you don't need to hire a platform team to keep up. A 12-month partnership, scoped to your platform.
Managed PlatformServices
Three engagements. One platform lifecycle.
Assess, build, operate. Each engagement is fixed in scope before work starts and designed to stand on its own.
Field notes
We write about what we run.
Practitioner guides from the engineers who build these platforms: the mechanics, the trade-offs, and what breaks in production.
AI workloads, or the platform under everything else.
Tell us where your platform stands.
Opens a short form. An engineer replies within 1–2 business days.
Start with a Platform Readiness Review