Serve AI models at enterprise scale — from endpoint to inference.
Production-grade model serving with GPU and NPU-aware scheduling, auto-scaling inference pipelines, and full observability across every request. The inference layer that connects your models to the agents, applications, and APIs that depend on them.
The gap between a trained model and a production inference service is wider than most teams expect. Metis closes it.
Serving an AI model in production is an entirely different problem from building one. Latency, throughput, hardware utilization, scaling policy, failover behavior, cost management — none of these are solved by the model itself. Most enterprises accumulate fragile deployment pipelines, undifferentiated infrastructure work, and custom serving scripts that consume the engineering resources meant for building better models. Metis treats inference as a first-class discipline. GPU and NPU-aware scheduling routes each request to the right hardware. Auto-scaling responds to real demand without manual tuning. Full observability surfaces performance, utilization, and cost at every layer. Your models reach the applications that need them — reliably, efficiently, and at any scale your enterprise requires.
GPU utilization achieved with xPU-aware inference scheduling
Manual scaling interventions with demand-responsive auto-scaling
Observability across every inference request, token, and endpoint
Run Metis wherever your enterprise operates.
The same inference platform — delivering identical serving capability, scheduling intelligence, and observability — regardless of where it runs.
AI Cloud
Deploy Metis on Thaki's managed GPU/NPU cloud infrastructure. Model endpoints provision on demand, with hardware scheduling and scaling managed entirely by the platform.
On-Prem Private Cloud
Run Metis entirely within your own data center, including fully air-gapped deployments. Model inference executes on your hardware, under your network policy, with no data leaving your perimeter.
Hybrid
Serve latency-sensitive inference workloads on private infrastructure while scaling burst capacity into the cloud. One platform, consistent serving behavior across both environments.
From model endpoint to high-throughput serving — a complete inference lifecycle.
Metis Serve
Production model endpoint management
Deploy models as versioned inference endpoints with traffic splitting, canary rollouts, and instant rollback. Route requests across single models or multi-model configurations from a unified serving layer.
Metis Hub
Centralized model and asset registry
A searchable, version-controlled hub for models, datasets, and API packages. Models trained in Maxis publish directly to Hub, making them immediately available for inference deployment without manual handoff.
GPU + NPU-Aware Scheduling
Right compute for every inference request
Inference requests are routed to the right accelerator — GPU or NPU — based on workload profile, hardware availability, and cost policy. No manual affinity rules. No hardware misconfiguration.
Auto-Scaling Inference Pipelines
Capacity that follows demand
Scale inference capacity automatically with real workload demand — up during peak periods, down during idle windows — without provisioned waste or manual capacity planning.
Full Inference Observability
Complete visibility across every endpoint
Real-time metrics on latency, throughput, token usage, hardware utilization, and error rates — surfaced per model, per endpoint, and per request. Identify bottlenecks before they become incidents.
Paxis Integration
Native agent inference without external API dependency
Metis Serve endpoints connect directly to Paxis agents, providing internal model inference for agent workflows without routing through external LLM APIs. Reduces token costs and eliminates external dependency.
How Metis fits into the stack.
Metis is the inference layer that Paxis agents call and Maxis models deploy into.
Metis
Production inference platform providing model serving, endpoint management, GPU/NPU-aware scheduling, auto-scaling, and full observability. The layer where trained models become production services.
Signum
Unified Control Plane — spans every layer
- IAM & access control
- Centralized logging
- Audit trail
- Alerts & anomaly detection
- Multi-channel notification
Built for enterprises deploying AI models at production scale.
Low-latency inference for regulated financial AI
Serve risk scoring, document analysis, and compliance Q&A models with full auditability — air-gapped within your own data center, with every inference request logged and traceable.
Air-gapped model serving for government AI environments
Deploy inference endpoints entirely within government-controlled infrastructure. Models serve applications and agents without any data or request routing outside your security perimeter.
Real-time inference for quality control and equipment monitoring
Serve inference models close to manufacturing operations on private infrastructure, delivering low-latency predictions for quality inspection and predictive maintenance without cloud dependency.
Centralized inference infrastructure across multiple business units
Replace fragmented, team-managed model deployments with a single governed inference platform — serving multiple internal teams and Paxis agent deployments from one consistent, auditable layer.