SpinDynamics is an adaptive inference orchestration platform that uses reinforcement learning to dynamically route, scale, and optimize model serving across edge, on-prem, and multi-cloud infrastructure.
A single control plane that sits between your application layer and your heterogeneous infrastructure fleet. Every inference request is observed, routed, and optimized in real-time by a multi-objective RL agent.
Each layer of SpinDynamics is designed to operate independently and compose seamlessly. Here is how every piece works under the hood.
Our routing engine is not rule-based — it is learned. A multi-objective reinforcement learning agent observes every inference request and continuously optimizes placement decisions across your infrastructure fleet.
The agent balances latency, throughput, cost, and data residency constraints simultaneously — adapting to traffic patterns, hardware availability, and model performance in real-time. No manual tuning. No static rules. The policy improves with every request.
Models are compiled to optimized runtimes (TensorRT, ONNX, GGUF) and distributed to 200+ edge PoPs worldwide. Inference runs locally at the edge — no round-trip to origin.
Smart prefetching ensures models are warm at the edge before traffic arrives. Our edge compiler automatically quantizes and optimizes models for the target hardware at each PoP. Cold start latency: zero.
The full SpinDynamics platform — control plane, RL engine, observability, and all — deployed on your hardware with zero external dependencies. No phone-home. No telemetry exfiltration. Fully air-gapped.
Designed for environments where data cannot leave the building: defense, healthcare, financial services, and government. Our Field Deployment Engineers handle the installation, tuning, and ongoing optimization — so your team stays focused on the mission.
Learn about our FDE program →Predictive autoscaling powered by time-series forecasting and reinforcement learning. SpinDynamics provisions capacity before demand spikes — not after. No more over-provisioning. No more cold-start cascades.
Scale from zero to millions of inferences per second. GPU allocation, model replica management, and queue depth optimization — all automatic. Spot instance fallback, multi-cloud burst, and graceful degradation are built in.
Data residency, encryption, audit logging, and access controls — enforced at the routing layer. Not an afterthought. Not a checkbox. A core architectural primitive.
Compliance policies are defined once and applied globally. When a request hits SpinDynamics, the RL router already knows which infrastructure is eligible before it makes a placement decision. Every request is traced, every decision is auditable, and every byte is encrypted at rest and in transit.
Full-stack inference observability: request traces, latency histograms, cost attribution per model/team/customer, and real-time drift detection. See exactly where every token goes, what it costs, and how the model is performing.
Export to your existing stack — Datadog, Prometheus, Grafana, OpenTelemetry. Or use the built-in SpinDynamics dashboard. Every metric is available via API for custom alerting and automation.
Python, Go, TypeScript, and REST. First-class SDKs with full type safety, streaming support, and async-native design.
The SpinDynamics SDK wraps the entire platform into an ergonomic, well-documented interface. Deploy models, configure routing policies, manage autoscaling, query telemetry, and run inference — all from the same client.
Streaming-first by default. Async-native with sync wrappers. Full type annotations for Python and TypeScript. Generated from our OpenAPI spec — always in sync with the platform.
Request a personalized demo with our engineering team.