Dedicated model serving on your own infrastructure.
Open-weight and fine-tuned models served on GPU nodes you control — predictable latency, full capacity isolation, and registered directly into the governed gateway. No shared tenants, no compromises on data residency.
Private model serving with full gateway governance.
Dedicated Serving on Your Infrastructure
Open-weight and fine-tuned models run on GPU nodes you control — Kubernetes or dstack. No shared tenant pools, no noisy-neighbour latency spikes. Your model, your hardware, your SLA.
Predictable Latency
Capacity is reserved for your workload. Requests do not queue behind other tenants. P99 latency stays stable under sustained load — predictable enough to build user-facing features on top of.
Capacity Isolation
Model replicas are dedicated to your organisation. Scale replicas up or down via the ManyLayers API. Horizontal autoscaling available on Kubernetes with configurable scale-to-zero policies.
Curated Model Library
Browse and deploy from a library of pre-validated open-weight models — Llama, Mistral, Qwen, Gemma, DeepSeek, Phi, and more. Optimised serving configs included for each model family.
Gateway-Governed Endpoint
Dedicated deployments register as providers in the ManyLayers gateway. The same routing rules, budget controls, guardrails, and audit logging apply — no separate access path for on-prem models.
Fine-Tuned Model Serving
Deploy LoRA adapters trained by ManyLayers fine-tuning jobs directly to dedicated inference. From training artifact to governed endpoint without manual model packaging or serving config.
From model selection to governed endpoint in minutes.
Choose a model from the library
Browse the curated model library and select an open-weight model or upload your own fine-tuned checkpoint. Optimised serving configurations are pre-set.
Select hardware and replica count
Choose GPU type and starting replica count. ManyLayers provisions the deployment on your Kubernetes cluster and runs health checks before marking the endpoint live.
Route through the governed gateway
The deployment registers automatically as a provider. Apply routing rules, budget limits, and guardrails just like any other provider — no separate access configuration required.
Predictable latency for applications that demand it.
Shared inference endpoints are appropriate for batch and exploratory workloads, but latency-sensitive features — real-time copilots, voice pipelines, and user-facing chat — need dedicated capacity. ManyLayers lets you reserve GPU nodes for specific models while keeping them inside the governed gateway.
- Llama 3, Mistral, Qwen 2.5, Gemma 2, DeepSeek, Phi-3, and growing library of open-weight models
- LoRA fine-tuning artifacts deploy directly — no manual checkpoint conversion
- Kubernetes Horizontal Pod Autoscaler integration with configurable GPU metrics
- Air-gapped capable: model weights and serving stack run entirely on-premises
- Prometheus metrics per deployment: throughput, P50/P95/P99 latency, queue depth
“Our fine-tuned Mistral model is now serving production traffic from our own Kubernetes cluster. P99 latency dropped 40% compared to the shared API we were using before — and the model stays inside our security perimeter.”
Principal Infrastructure Engineer — Financial Technology Company