The entire system is open and available to use

Tandemn’s complete planning algorithm and execution engine are open source. Configuration search, causal evidence, scoring, and cluster-wide planning logic are available alongside the execution engine’s deployment logic. Your team can inspect the software, run it in your own environment, and adapt how it connects to your infrastructure.

Bring the workloads and hardware you already have, whether they run inside your VPC, on premises, or across a mix of environments. Tandemn applies a global planning layer to that existing fleet, continuously finding better configurations and placements that turn stranded capacity into useful compute. You can run more work, meet demanding performance targets, and improve token economics while keeping your infrastructure and serving stack under your control.

Our enterprise offering builds on the same open software with detailed telemetry and observability into the algorithm’s behavior. We help your team understand why configurations and placements are selected, how predictions compare with actual serving performance, and how confidence, uncertainty, and workload priorities influence each plan. This connects a deployment decision to its operational consequences across the fleet.

The focus extends from placing workloads to continuously optimizing how they run against your standards. Latency targets, throughput requirements, batch deadlines, capacity quotas, and operational policies guide planning, while risk assessments account for prediction uncertainty and the consequences of reconfiguration. Enterprise visibility helps your team track whether workloads are meeting those requirements and operating in configurations that make the best use of available hardware as conditions change.

Under an enterprise contract, Tandemn can provide supplementary performance data and inference relationships relevant to your models, hardware, runtimes, and workload profiles. This gives the planning algorithm a richer starting point for evaluating deployment candidates and understanding how configuration choices affect serving behavior in your environment.

With more relevant evidence available from the outset, the planner can spend less time learning those relationships through live deployments and reach effective configurations sooner. The aim is faster convergence toward a high-performing operating point for your specific cluster, followed by continuous calibration as workloads and conditions evolve. The software remains open; our enterprise business provides the performance context and operational visibility that help your team realize its capacity gains more quickly.