Turn underutilized GPU capacity into business value.
Metis is a Kubernetes-native AI operations platform that unifies the entire AI lifecycle — from model training and fine-tuning to production inference — under a single intelligent control plane across heterogeneous compute clusters.
Realize the full value of your infrastructure investment.
Most enterprises fail to fully utilize their GPU and compute resources due to inefficient scheduling. Metis automates the entire AI lifecycle — from model fine-tuning to large-scale agent inference — maximizing ROI on private infrastructure. Reduce the cost and complexity of building and operating an AI stack from the ground up. Metis abstracts the underlying hardware so your teams can focus on solving business-critical problems, not managing infrastructure.
The entire AI lifecycle on a single platform
ROI Engine
Every xPU works, all the time
Advanced Kueue/Kai-based scheduling dynamically allocates xPU resources. Strict multi-tenant controls and intelligent queue management guarantee 100% hardware utilization with zero idle resources.
Training Engine
Fine-tune with your data, inside your firewall
Run SFT and DPO pipelines directly on-premises. Full PyTorch and HuggingFace ecosystem support lets you train on sensitive internal data without ever exporting it.
Inference Engine
Agent responses faster than public cloud
vLLM and TensorRT-LLM optimized endpoints minimize TTFT. Dynamic traffic routing and Scale-to-Zero architecture automatically adapts to traffic fluctuations without public cloud dependency.
Operations Engine
From experimentation to production, without friction.
A Kubernetes-native single-pane-of-glass environment. Automates model experiments, lineage tracking, and production deployment in one workflow, fundamentally reducing MLOps operational burden.
Your hardware investment finally pays back in full
Consolidate fragmented AI stacks into a single Kubernetes-native platform to reduce operational complexity and maximize hardware ROI. Metis is a unified MLOps platform purpose-built for independent AI operations in private cloud environments.
xPU utilization — zero idle resources
External data exposure — on-prem fine-tuning
Unified platform — training, inference, operations
Scale-to-Zero inference — traffic-based scaling
Abstracting complexity, delivering all xPUs as a service
'AI Token Powerhouse'
Easy Deployment
Deploy AI/ML workloads with just a few clicks.
Smart Resource Optimization
Minimize idle resources with real-time monitoring and auto-scaling.
Maximize Developer Productivity
Eliminate repetitive setup with template-based workflows.
A Cloud-Native, Multi-Cluster Architecture
for Unified AI Acceleration
Centrally manage Kubernetes clusters across on-prem and public cloud environments with a single control plane that integrates multi-cluster GPU scheduling, distributed training, and scalable inference for enterprise AI workloads.
Global Scheduler
Monitoring/Billing
Policy/Quota
Cluster Connector A
Cluster Connector B
Cluster Connector C
Bring Your Own Cluster (BYOC)
Centrally manage all K8s clusters from on-prem to public cloud.
Centralized Observability & Policy: Unified monitoring, billing, quota, and SLA management in one place.
Control Plane
Scheduler
Cluster A
Baremetal
Cluster B
GPU VM
Cluster C
Public Cloud K8s
Unified K8s Control Plane
Single API and UI for all clusters.
Global Scheduler
Intelligently distribute workloads across clusters based on policies.
Maximize ROI from your AI infrastructure investment
Contact Us7-Layer Unified Architecture with 3 Pillars
This unified stack is designed to support every stage of AI workflows, from physical hardware to developer UI. Each layer is independent yet organically connected, ensuring stability and scalability.
Ecosystem Layer – Model · Agent · Data Hub
Thaki Cloud goes beyond GPU as a Service, providing an AI Cloud OS that includes Model Hub, Agent App Store, and Data Hub.
Model Hub
- Unified management of public and internal models
- Version and Release Channel-based deployment control
- KPI monitoring and TensorRT/vLLM optimized serving
Agent App Store
- Package model, prompt, and tool-calling logic into a single app
- Security verification and cost/usage dashboard
- Deploy and share revenue through marketplace
Data Hub
- Data cleaning, labeling, and validation pipeline management
- Governance and sovereignty metadata labeling
- Unified management of training and evaluation datasets
Key Features at a Glance
All-in-One Pipeline
Data cleaning, labeling, testing → SFT/DPO tuning → Evaluation → Serving (VLLM/TensorRT-LLM/Triton) all in one
Scheduler Strategy
Ready-to-use AI interfaces and applications for internal and external users
Serverless Interface
Scalable inference with fully managed service model
Dedicated Endpoints
Dedicated GPU/xPU nodes for high-priority or latency-sensitive services
Fine-tuning Studio
Platform for enterprise-specific AI model fine-tuning
Evaluations & Guardrails
Comprehensive toolset for measuring and ensuring model quality and regulatory compliance
Unified Workflow
End-to-End, All-in-One Pipeline
Policy-Based Safe Release
Supports release channels (Canary, Blue-Green) with policy approval and automatic rollback.
Version Control & Reproducibility
Manage dataset snapshots and version history for reproducible runs.
Resource Management
Scheduler Strategy
Kueue
Scalable serving workloads with multi-tenant support and resource quota management.
Kai
Optimized for model tuning, training workloads, and batch processing.
Slurm
High-performance computing (HPC) and large-scale parallel jobs.
Dynamically selects the optimal scheduler based on workload type from a single policy layer.
WebUI / Control Plane API
Scheduler Suite
(Selection Logic)
Kueue
Ideal for scalable serving workloads like vLLM, Jupyter.
Kai
Optimized for batch processing like PyTorch fine-tuning.
Slurm
Supports HPC workloads like MPI and scientific computing.
Fully Managed, Usage-Based Inference
Serverless Interface
OpenAI-Compatible API & Model Support
OpenAI-compatible API for easy migration from closed providers, with open-source and multimodal model support.
Auto Scaling
Infrastructure optimization with automatic scaling based on tokens-per-second throughput and request volume.
vLLM-Based Engine
Optimal performance with high throughput, low latency, and efficient KV cache utilization.
Reduced Management & Rapid Prototyping
No infrastructure management burden, rapid prototyping and production-grade serving in a unified stack.
Consistent Performance with Dedicated xPU Capacity
Dedicated Endpoints
Dedicated Nodes, VPC/Private Options
Isolated network environment and infrastructure for security-critical workloads.
SLA: Availability, Latency & Capacity Guarantee
Enterprise-grade SLA with uptime, latency, and capacity guarantees.
Fine-Grained Version/Scale/Rollout Control
Detailed configuration for model versions, scaling limits, and deployment strategies.
Predictable Performance & Cost
Consistent performance and clear cost structure in stable production environments.
Enterprise-Grade Model Customization
Fine-tuning Studio
SFT/DPO/GRPO, LoRA/QLoRA, Distributed Training
Support for various latest fine-tuning techniques and efficient distributed training across multiple GPUs.
PyTorch+HF, Task Templates
Verified task templates for chat, instruction-following, RAG, and domain-specific models.
Kueue/Kai Scheduling: Fair & Efficient Allocation
Fair and efficient GPU allocation through unified resource scheduling with integrated log-based operations.
One-Click Deployment: Serverless/Dedicated
Instantly deploy fine-tuned models to serverless inference or dedicated endpoints.
Quality Measurement & Compliance Enforcement
Evaluations & Guardrails
Model/Prompt A/B Testing
Automatic scoring based on latency, cost, quality metrics, and task-specific KPIs.
HITL Evaluation Workflow
Human expert-based evaluation system for subjective tasks.
Content Filters & Guardrails
Automated safeguards for safety checks, policy-based restrictions, and regulatory compliance.
Data-Driven Decision Making
Optimize model/prompt selection and reduce production deployment risks.