Terrane is a unified runtime for inference workloads, APIs and backend services. Region-aware routing, dynamic batching, elastic capacity and live observability, from a single deployment file.
Control plane
Global deployment topology
99.98%
Failover scenario replays every 14 s
Deploy high-performance inference, orchestrate workloads automatically, route traffic globally and run on the frameworks your team already uses. The control plane underneath is ours to manage.
Run LLMs, embeddings and multimodal workloads on compute tiers sized for sustained production traffic, not demos.
Scale from zero to multi-region capacity automatically, without tuning clusters, queues or hardware pools by hand.
$ terrane deploy llama-3 --regions sfo,tyo,par,sin Route every request to the healthiest nearby runtime so latency stays low and availability stays high, everywhere.
PyTorch, TensorFlow, TensorRT, vLLM or your own container. Bring the stack you have, keep the deployment workflow.
Terrane brings deployment, routing, batching and model orchestration into one unified runtime so technical teams can ship production AI systems without stitching together fragmented infra.
Deploy inference workloads close to demand with region-aware routing, resilient failover and low-latency execution across the distributed runtime.
Manage model versions, hardware allocation, image builds and rollout policies from a single deployment layer designed for production inference.
Increase throughput automatically with request batching, queue-aware scheduling and runtime-level optimisations that improve GPU utilisation under live traffic.
Ship APIs, inference endpoints and backend services through one deployment workflow with built-in routing, autoscaling, observability and infrastructure-aware execution.
runtime: edge-inference regions: [iad, sfo, fra, sin] autoscale: enabled batching: dynamic observability: full
From runtime setup to live observability, Terrane gives technical teams a clear operational path to deploy, route, scale and manage production AI workloads without stitching together fragmented infrastructure manually.
Bring models, APIs, queues and backend services into one deployment-ready control surface.
Configure compute, regions, scaling rules and rollout policies before traffic ever goes live.
Roll workloads out across regions through one workflow instead of managing isolated surfaces.
Send requests to the healthiest and closest runtime path using latency- and region-aware routing.
Expand active capacity under live inference demand without manually tuning queues or pools.
Track runtime health, request flow, regional status and system activity through one operational view.
Define deployment behaviour once and let Terrane manage execution across regions and hardware tiers.
Stay informed with real-time metrics for latency, health, throughput and regional runtime status.
Control compute classes, scaling thresholds, routing rules and rollout behaviour without rebuilding workflows.
Terrane is built for teams running live inference, distributed APIs and regional runtime infrastructure, with reliability, observability and operational control designed into the platform.
“Terrane removed the infrastructure sprawl from our inference stack. We can deploy globally, monitor runtime health and scale traffic without building a custom control plane around it.”
Monitor latency, throughput, queue health and regional runtime behaviour through one operational surface.
Deploy across regions, compute classes and model-serving stacks without rebuilding your delivery workflow.
Regional failover, health-aware routing and runtime safeguards keep production traffic stable under live demand.
Monitor latency, throughput, queue health and regional runtime behaviour through one operational surface.
Deploy across regions, compute classes and model-serving stacks without rebuilding your delivery workflow.
Regional failover, health-aware routing and runtime safeguards keep production traffic stable under live demand.
Runtime health across active regions under sustained production traffic.
Region-aware request routing keeps inference performance close to end users.
Distributed runtime presence for global AI services and backend workloads.
Elastic runtime expansion aligned to live load, queue pressure and inference demand.
Latency histograms, rollout post-mortems and the occasional opinion on how production AI should be run.
All articlesMove from prototype to production with a unified runtime for inference, APIs and backend services, built for global routing, elastic scale and real operational visibility.