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 provides support and implementation assistance along with observability, governance, security, and trust capabilities.

The extended observability features provide detailed telemetry and observability into the algorithm’s behavior along with the insights necessary to tune the behavior to your specific needs. 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 Enterprise Edition also provides supplementary performance data and inference relationships relevant to your models, hardware, runtimes, and workload profiles. This complementary profiling gives the planning algorithm a richer starting point for evaluating deployment candidates and understanding how configuration choices affect serving behavior in your environment. With relevant evidence available from the outset, the planner can spend less time learning those relationships through live deployments and reach effective configurations sooner. It continues to calibrate its plans against observed performance as workloads and conditions change, helping your team extract more capacity from its GPU fleet.