Use Case · Enterprise AI Operations
Ship AI to production without the chaos.
Multi-tenant model serving, GPU fleet orchestration, governed rollouts, and end-to-end cost attribution — on one platform. Move from notebooks to a hardened control plane with model registry, canary deploys, and per-tenant FinOps built in.
5×
More deploys per quarter
-30%
GPU cost via routing
99.95%
Inference SLO
< 4 min
Canary → 100%
Why teams struggle
The problems that block the work.
We hear the same pattern of failure modes across every engagement. These are the ones Yobitel exists to remove. Not generic platitudes, but the specific frictions that stall delivery.
Model sprawl across teams
Twelve teams shipping six fine-tunes each, each with its own Triton or vLLM image, scattered across notebooks, S3 buckets, and SageMaker endpoints. No single registry, no lineage, no rollback path.
GPU contention and idle burn
Static endpoints reserve H100s that sit at 12% utilisation overnight while training jobs queue. Per-team quotas leak. Nobody can answer which model is consuming which GPU-hour.
Opaque cost attribution
Finance asks why the GPU bill grew 4× and nobody can split the spend by product line, model, or customer. Showback dashboards lag by weeks. Pricing decisions are guesswork.
No deployment governance
Engineers push straight to prod, regulators ask who approved the change, and there's no canary, no eval gate, no audit trail. One bad weight ships to every tenant at once.
What Yobitel delivers
The capabilities we ship, end to end.
Each capability is a first-class product surface, not a slide. They compose into the platform behind every Yobitel customer in production.
Model registry with lineage
Every model version pinned to source dataset, training run, evaluation report, and signed container digest. Promote prod → staging → archive with a single API call.
Intelligent routing & batching
Mixture-of-experts router sends each request to the cheapest model that meets the quality bar. Continuous batching on vLLM, TensorRT-LLM, and SGLang backends.
Multi-tenant isolation
Namespace, quota, and KV-cache isolation per tenant. Soft and hard GPU quotas. Network policies and OPA gates on every model endpoint.
Canary, blue/green, A/B
Progressive delivery built-in. Shift 1% of traffic to a new weight, gate on latency and accuracy metrics, auto-rollback on regression. No bespoke Argo Rollouts work.
Autoscaling on real signals
KEDA-driven HPA on queue depth, p95 latency, and KV-cache pressure — not raw QPS. Scale-to-zero for spiky tenants; warm pools for latency-critical paths.
Per-token cost attribution
FinOps tags every prompt with tenant, product, and model. Showback dashboards refresh in minutes. Chargeback exports straight to your billing pipeline.
Governance & policy gates
Approval workflows, signed deploys, SBOM on every image. SOC 2, ISO 27001, and UK G-Cloud aligned controls. Audit log streamed to your SIEM.
Eval gates on every promotion
Plug InferenceBench into the registry — no version reaches prod without passing accuracy, latency, toxicity, and regression suites with signed reports.
How adoption unfolds
From pilot to production, step by step.
The typical adoption path. We compress it where you have momentum and we slow it down where compliance or change-control demand it.
Register & sign the model
Push weights and container to the Yobibyte registry. Lineage, dataset hash, and Cosign signature captured automatically.
Define routing & SLOs
Declare cost ceiling, latency SLO, accuracy bar, and tenant policy as YAML. The router enforces it on every request.
Canary deploy
Shift 1% → 10% → 50% → 100% gated on InferenceBench scores and Prometheus SLO burn. Auto-rollback under threshold.
Observe & attribute
Per-tenant dashboards: tokens, $/1K, p95, drift. Cost exports to your finance system. Alerts to PagerDuty.
Iterate or retire
Promote a winning variant, archive losers, retire end-of-life weights with a single command. Audit trail preserved.
The Yobitel stack behind this
Products & services that do this work.
No abstractions, no hand-waving. Each item below is a real Yobitel product or service with its own documentation, pricing, and SLA.
Yobibyte Platform
The control plane: model registry, routing, canary, quotas, FinOps, and observability in one self-serve console.
Omniscient Compute
The GPU substrate: bare-metal H100/H200/B200 clusters with InfiniBand, NVMe, and per-tenant slicing.
InferenceBench
The eval gate: pre-promotion accuracy, latency, and regression checks signed into the registry.
GPU Orchestration
Kubernetes-native scheduler with topology-aware placement, MIG slicing, and spot reclaim.
Observability Suite
OpenTelemetry-native traces, metrics, and logs for every inference call — with cost overlays.
Outcomes we measure
The numbers customers report back to us.
Aggregated medians across recent deployments. Specific outcomes depend on workload and starting baseline. We'll model yours during the first conversation.
5×
More production deploys per quarter
30%
Lower GPU spend via intelligent routing
4 min
Median canary → 100% rollout time
100%
Of deploys with eval + signed audit trail
Customer story
Global retail bank, 14-team AI org
Consolidated 38 ad-hoc model endpoints onto Yobibyte. GPU spend dropped 31% in one quarter while throughput rose 2.4×.
We finally know which model is costing us what — and which one is making us money. The conversation with finance changed overnight.
Where this lands
5×
More production deploys per quarter
30%
Lower GPU spend via intelligent routing
4 min
Median canary → 100% rollout time
Other use cases
Explore the rest of the solution suite.
Infrastructure Modernisation
Modernize Data Centres
Refit aging facilities into AI factories without ripping out what works. Yobitel engineers retrofit cooling, fabric, and orchestration around your existing footprint — then layer GitOps and platform tooling so the new estate runs itself.
ExploreApplied AI Engineering
Build AI Applications
Yobitel ships a complete app-building stack: typed SDKs, RAG primitives, agent orchestration, embeddable UI, and one-click deploy onto Yobibyte. Your product team focuses on the experience — we handle inference, observability, and the unglamorous middle.
ExploreAIOps & SRE Automation
Automate IT Operations
Anomaly detection, self-healing runbooks, GitOps drift control, and an AI SRE that triages incidents at machine speed. Yobibyte's automation surface plugs into your existing observability stack and learns from every postmortem.
ExploreReady to put this into production?
Talk to a Yobitel engineer. We'll map your environment, sketch the architecture, and propose a 60–90 day plan to first measurable outcome.