Cache locality affects serving work
A route can send work away from useful retained context.
SpinRoute evaluates the supported inference-routing choices with cache locality in view.

Expert-routing and load-balancing software for mixture-of-experts AI serving.
See what it solvesTHE PRODUCT, IN PRACTICE
SpinRoute makes inference routing an optimization problem. Its supplied product description covers cache-aware routing; a separate retained MoE experiment compares token-to-expert allocation with a greedy baseline. Cache hit rate, dropped tokens, solve time and model quality are separate measures, not interchangeable claims.
A route can send work away from useful retained context.
SpinRoute evaluates the supported inference-routing choices with cache locality in view.
A routing change is not useful simply because the loads look even; token handling and model behavior still matter.
The product addresses dropped-token behavior alongside load balance. The deployment is assessed with the model-quality and serving requirements that your team accepts.
An isolated routing experiment does not show what will happen inside the real model-serving environment.
SpinRoute’s integration focuses on the expert-routing decision in that environment, so you can compare its impact on the full serving workload.
QUBO-based routing in the supported cache-locality or MoE serving integration.
Balancing functionality focused on the work sent to model experts.
A route to evaluate dropped-token behavior together with the distribution of serving work.
Defined model, expert and runtime compatibility for the deployment being purchased.
SpinRoute is fully available with CLI, MCP and headless access. Use the interface that fits your terminal, your agents or your automated workflow.
Run the product from a terminal or script, alongside the tools your team already uses.
Get CLI accessConnect MCP-compatible clients and agents to the product’s exposed capabilities.
Get MCP accessOperate without a graphical interface in unattended, scheduled or pipeline-driven work.
Set up headless accessProduct-specific inputs, hardware requirements and result guarantees still apply. An access mode does not change your license or deployment scope.
Explore all product interfacesA serving team wants to address an uneven expert workload without assuming that a different routing pattern preserves every quality target.
Illustrative customer use—not a new benchmark or completed customer deployment.Supply the supported model and a representative serving workload.
Integrate SpinRoute at the expert-assignment point.
Compare expert load, token handling, latency and quality under the same workload.
Explore the product workflow, from your input to a result you can use.
Token work arrives unevenly across a set of model experts.
Interactive explanation. This illustration does not execute customer workloads or display measured performance.
The owner reports a 99.2% KV-cache hit rate and sub-millisecond solves on NVIDIA GPUs; the associated workload and timing scope were not supplied here. These are attributed product claims, not universal promises. The MoE experiment is separate. QUBO formulation does not by itself establish a global-optimality proof.
$75,000/year is the retained standalone price proposal for its named scope. The platform plans buy qualified workflows and capacity, not a second charge every time a compatible method is selected. Existing bundle inclusions do not expand automatically.
Fully available with CLI, MCP and headless access. Runtime requirements, supported operations and license scope remain product-specific.
One named production deployment. Shared engine rights for this application included.
Scope & packagesBring a representative workload and the result your team needs. We’ll define the product, deployment and engineering scope around that task.
Activate this capability inside a qualified ai serving workflow. Compatible methods and required shared checking are included once within that workflow; the product remains independently purchasable.
Capacity, release, environment, result contract and operating responsibilities are specified before activation. Choosing an additional method is not permission to add an unrelated workload or unqualified deployment.
Explore AI Serving Choose workflow capacityCommission the model, integration and acceptance work needed for your task. Existing products stay independently available.
Use the software directly or ask us to scope the implementation.
One capability. A connected workflow.
A team to put it into practice.