MLOps Cloud Hosting

Your GPU Bill Shouldn't Grow Faster Than Your Models.

Dedicated GPU infrastructure, Kubernetes-native MLOps pipelines, and flat-rate pricing — designed for AI and ML teams that need reliable training, inference, and data pipelines without the public cloud GPU lottery.

Request Free MLOps Sandbox Access Talk to a Cloud Engineer

No credit card required. Limited early-access spots available.

The Problem

Running ML on Public Cloud Is Getting Expensive to Defend

GPU Cost Chaos

On-demand GPU pricing makes training budgets difficult to defend.

  • Idle capacity still appears on the bill between training runs.
  • Spot interruptions turn planned six-hour jobs into delayed reruns.
  • Month-end cost rarely matches the estimate.

Shared Infrastructure Variability

Shared GPU pools make performance and delivery timing unpredictable.

  • Inference latency can vary 30–40% with co-tenancy.
  • Training jobs compete with other tenants for available GPU capacity.
  • Model releases depend on capacity you do not control.

MLOps Complexity Without Accountability

Infrastructure work keeps pulling data scientists away from models.

  • Experiment tracking, orchestration, registry, and serving need ownership.
  • Operational work falls to whoever has time.
  • Pipeline reliability declines when nobody owns the platform.

The Shift

MLOps Infrastructure That Works Like a Platform, Not a Side Project

Dedicated GPU compute, Kubernetes-native pipelines, flat-rate pricing, and accountable operations from the engineers who design the environment.

No capacity planning spreadsheets. No shared tenancy surprises. No vendor lock-in.

Dedicated GPU node pools

  • Allocate GPU capacity exclusively to your workloads.
  • Remove shared-tenancy interference and capacity contention.
  • Run training jobs to completion on reserved compute.

Kubernetes-native ML pipelines

  • Run batch training, distributed training, and inference on Kubernetes.
  • Use retry logic, resource guarantees, and job-level observability.
  • Surface pipeline failures quickly instead of missing silent failures.

Model serving at production scale

  • Deploy dedicated inference endpoints with monitoring.
  • Set autoscaling policies and observe latency.
  • Operate model serving to application reliability standards.

Experiment tracking and model registry — inside your environment

  • Keep model lineage, experiments, and artifacts in your isolated environment.
  • Avoid routing sensitive outputs through a third-party SaaS platform.
  • Maintain control over training data and model artifacts.

Flat-rate monthly pricing

  • Plan ML infrastructure spend month to month.
  • Avoid cost spikes when a training run takes longer than expected.
  • Give finance a stable budget input.

Managed operations, included

  • Cover GPU health, job alerts, model-serving uptime, and patching.
  • Handle incident response with the engineers who built the environment.
  • Keep platform accountability with one operations team.

Architecture Overview

A simplified view of a typical Mayan.Host MLOps Cloud environment:

Your Users / Applications
          |
API Gateway + Load Balancer
          |
Kubernetes Cluster (Dedicated)
|- Inference Services (GPU — dedicated node pool)
|- Training Job Scheduler (Kubeflow / Argo)
`- Data Pipeline Workers
          |
Model Registry + Artifact Storage (MinIO / S3-compatible)
          |
GPU Node Pool (Dedicated — no shared tenancy)
          |
Monitoring | GPU Metrics | Experiment Tracking | Alerting
Dedicated by default

Dedicated capacity for training, inference, artifacts, and GPU allocations.

Operated end to end

Engineers manage scheduling, GPU health, serving uptime, monitoring, and escalation.

Portable foundation

Standard Kubernetes and open MLOps tooling keep models and pipelines portable.

Audience

Built for AI and ML Teams Running Real Production Workloads

AI-First SaaS Platforms

Training models on shared cloud GPU and serving them through APIs your users depend on.

  • Control fast-growing GPU costs.
  • Keep inference latency consistent for users.
  • Own model artifacts and experiment history.
Explore AI SaaS infrastructure patterns

LLM and Generative AI Products

Running fine-tuning, RAG pipelines, and inference at scale.

  • Run fine-tuning without spot-interruption waste.
  • Keep context data and training corpora controlled.
  • Serve models on dedicated GPU capacity.
Review production LLM infrastructure options

Series A–B AI Companies

Past the prototype phase, building for production reliability.

  • Move production ML beyond manual operations.
  • Use a managed platform without a full ML platform team.
  • Run production-grade GPU infrastructure with accountable operations.
See why AI engineering teams choose Mayan.Host

Data Science Teams Spending Too Much Time on Ops

Scientists managing infrastructure they didn't sign up to own.

  • Return infrastructure time to experiments and models.
  • Give pipeline failures a clear owner.
  • Let an accountable team operate the platform.
Talk to us about taking ops off your team's plate

Regulated and Sensitive AI Workloads

Healthcare, fintech, and compliance-bound AI applications where data residency is non-negotiable.

  • Meet residency, isolation, and audit requirements.
  • Keep sensitive training data off shared GPU pools.
  • Document dedicated controls for compliance review.
Discuss regulated AI infrastructure requirements

ML Teams Migrating Off AWS SageMaker or GCP Vertex

Done with per-feature pricing and managed-service lock-in.

  • Replace per-feature managed-service pricing.
  • Own an open ML stack without assembling it alone.
  • Migrate with the platform managed end to end.
See how teams migrate off SageMaker

If your GPU line item is your fastest-growing infrastructure cost and your engineers spend more time managing pipelines than improving models, this is built for you.

Process

From Request to Running MLOps Infrastructure — In Days, Not Months

Step 1

Request MLOps Sandbox Access

Tell us what your ML environment needs to run reliably.

  • Share workloads, GPU requirements, and your current stack.
  • Two minutes. No sales pitch or commitment.

Step 2

Architecture Review

An engineer designs around your workload profile.

  • Review training pipelines, data constraints, GPU sizing, and latency targets.
  • Select tooling and isolation based on your requirements.

Step 3

Sandbox Provisioned

Test dedicated managed GPU infrastructure before committing.

  • Run a training job in a configured Kubernetes MLOps sandbox.
  • Deploy a model and validate serving behavior.

Early adopters get free sandbox access: no credit card, no commitment.

Request Free MLOps Sandbox Access

Comparison

Public Cloud GPU Is a Useful Starting Point. It Doesn't Have to Be Your Permanent Answer.

For production training, continuous inference, and sensitive data, compare usage-based GPU capacity with dedicated managed infrastructure.

Keep public cloud where it fits. Place predictable GPU workloads where cost and performance are controlled.

AreaAWS / GCP (Public Cloud GPU)Mayan.Host MLOps Cloud
Pricing modelPer-instance billing; spot rates vary with demandFlat-rate monthly pricing scoped to your GPU allocation
GPU availabilityShared capacity; regional availability subject to demandDedicated GPU nodes allocated exclusively to your workloads
Training interruptionsSpot instances can be reclaimed mid-jobNo interruptions; dedicated capacity runs to completion
Inference latencyVariable; dependent on host load and co-tenancyConsistent; isolated compute with no noisy-neighbor effect
MLOps toolingManaged services with per-feature billing (SageMaker, Vertex)Open stack (MLflow, Kubeflow, Argo) included and managed
Data residencyConfigurable; enforcement is your responsibilityDedicated infrastructure; you define where training data lives
OperationsDIY or expensive managed service add-onsFully managed by DevOps and SRE engineers

Always-on training pipelines and inference services are strong candidates for dedicated placement.

Talk to a Cloud Engineer About Your GPU Setup AWS Optimization GCP Optimization Hybrid Cloud

Trust

Trusted by Teams Where Model Reliability Is a Product Requirement

Trusted in Production

AI-Powered SaaSVisual CommerceProduction ML Workloads
"We moved our inference stack to Mayan.Host and GPU costs became the one predictable line on our infrastructure budget. Training jobs run to completion. The team catches GPU node issues before our engineers even notice them."

— CTO, AI-Powered Visual Commerce Platform

Built on production engineering fundamentals.

Dedicated GPU InfrastructureSRE-Grade ReliabilityKubernetes-Native MLOps24/7 Monitoring

Dedicated GPU capacity, observability, and operations are baseline controls, not optional add-ons.

Built and operated by production engineers

  • Experience with AI SaaS, fintech, and data-intensive production workloads.
  • Infrastructure built for dedicated GPU environments and regulated pipelines.
  • The engineers who design your environment operate it in production.
Review SRE services

Early Access

Get Early Access. GPU Sandbox Included.

We're onboarding a limited number of early-access partners for Mayan.Host MLOps Cloud.

Try It Before You Commit.

  • Run a real training job and serve a model on dedicated GPU infrastructure.
  • Test an environment configured for your ML stack and workloads.
  • Work directly with engineers responsible for the platform.

No credit card. No commitment. No 45-minute sales demo.

What you get in the sandbox

  • Dedicated GPU node pool (configured for your workload profile)
  • Kubernetes cluster with MLOps tooling deployed and ready
  • Experiment tracking and model registry (MLflow or equivalent)
  • Direct access to a Mayan.Host ML infrastructure engineer

Early-access spots are limited.

Request Free MLOps Sandbox Access

FAQ

Common Questions About Mayan.Host MLOps Cloud

What GPU hardware does Mayan.Host provision?

GPU nodes are sized to your workload profile.

  • Review training, inference latency, and GPU memory requirements.
  • Configurations can include NVIDIA A100, A10G, and RTX-class nodes.
  • Confirm hardware selection during architecture review.
Can I run LLM fine-tuning and inference serving on the same environment?

Yes. We can design separate pools when workload patterns require it.

  • Separate training from latency-sensitive inference.
  • Allocate capacity around your use case.
  • Operate both through one managed environment.
How does GPU pricing work?

Pricing is flat-rate and scoped to your allocation.

  • Base it on GPU capacity, compute, storage, and networking.
  • Avoid per-request charges and unplanned training-run spikes.
  • Know pricing before committing.
My training data is sensitive. Can it stay inside my environment?

Yes. Training data, artifacts, and logs can stay in your dedicated environment.

  • Avoid shared infrastructure for protected data.
  • Define residency and isolation controls in architecture.
  • Keep outputs within your controlled boundary.
What MLOps tooling is included and managed?

We support open MLOps tooling chosen for your requirements.

  • MLflow for experiment tracking and registry workflows.
  • Kubeflow Pipelines or Argo Workflows for orchestration.
  • MinIO or S3-compatible artifact storage, managed by our engineers.
Can I keep product infrastructure on AWS and move GPU workloads to Mayan.Host?

Yes. Hybrid GPU placement is a common pattern.

  • Keep application services and APIs on AWS where appropriate.
  • Run GPU-intensive training and inference on private cloud.
  • Design networking, access controls, and data flow as one system.
How long does it take to provision a GPU environment?

Sandbox environments are typically live within days of review.

  • Production timelines depend on GPU configuration and workload scope.
  • Custom production environments are typically delivered within two to four weeks.
  • This is not a six-month migration project.
Do I need an ML platform team to use this?

No. Mayan.Host is fully managed.

  • We handle GPU health, Kubernetes, MLOps tooling, monitoring, patching, and incident response.
  • Your team uses standard ML tooling.
  • An existing platform team helps you move faster, but is not required.

Request MLOps Sandbox Access