Know what changes.
Compare output quality, p99 latency, reliability, and full operating cost against your current deployment.
Managed AI infrastructure
Your compute.The simplicity of a managed platform.
Run production inference on GPUs you procure. You choose the provider and control compute costs. Doku operates the platform.
The Doku platform
Inference, agent sandboxes, and batch jobs.
open-model-api
Dedicated vLLM endpoint on your Kubernetes cluster.
Team experience
- Meta FAIR
- Google Brain
- RunPod
- AWS
- FluidStack
COMPUTE INDEPENDENCE
Keep the managed experience.
Control the compute economics.
Choose your GPU provider and commercial terms without rebuilding the operational platform yourself. Compare the full cost before committing to a move.
01 / BUNDLED INFRASTRUCTUREOne provider.
One provider.
One combined bill.
Your infrastructure and serving experience are purchased together. Simple to begin, but compute pricing and placement are tied to that platform.
Compute accessInference softwareOperations
02 / DOKUCompute you choose.
Compute you choose.
Operations managed.
Procure the GPUs that fit your requirements. Doku provides the managed software and operating layer, independently of the compute contract.
Your compute contractDoku managed platformYour workload and policies
Economic outcomes depend on utilization, commitment terms, networking, engineering, and Doku fees. We compare all-in costs rather than assuming dedicated compute is always cheaper.
A CONTROLLED FIRST MOVE
Move with confidence.
Start with one production inference service. Know the trade-offs, move in stages, and keep a path back.
Prove it on your GPUs.
Replay representative traffic against the target environment, with agreed requirements and clear go/no-go checks.
Shift traffic in stages.
Review the plan, approve critical production changes, and retain recovery options at each step.
Keep the service running.
Track workload health, performance, utilization, and production changes through a managed operating layer.
THE OPERATING EXPERIENCE
Every change,
in context.
See the state of your workloads, understand the options, and review production changes with their expected impact and a clear way back.
Discuss your operating requirements open-model-api CHANGE REVIEW
Review before production.
01
ObserveTraffic, latency, errors, and GPU capacity
Signal02
InvestigateConfiguration, recent changes, and alternatives
Context03
ApproveProposed rollout, checks, and recovery plan
DecisionYour team controls critical production changes
Illustrative workflow, not live operational telemetryTHE WORK THAT MATTERS
A platform for the workloads.
Not just the infrastructure.
Start with reliable production inference. Extend the operating model into the performance and execution workflows that matter to your team, as each environment is validated.
Production inference
Run open-model APIs with performance visibility, deployment controls, and operational support.
GPU efficiency
Evaluate autoscaling, startup optimization, caching, and snapshot techniques for compatible environments.
Production operations
Monitor service health, review changes, and preserve safe recovery paths across deployments.
Beyond endpoints
Explore batch execution, evaluations, agent workloads, and sandboxes as the platform expands.
BUILD WITH DOKU
More ambition.
Same team.
Run more of the AI workloads your product depends on, without turning your engineering team into an infrastructure team.
Talk to an engineer