Declare what, the platform handles how.
Your team describes the workload. Amalgamy translates intent into hardware selection, data routing, and compliance enforcement.
Researchers spend a significant portion of their time on infrastructure tasks: choosing CUDA versions, requesting node types, staging data, and tuning configurations. Amalgamy eliminates that. The user declares what they want to run and the outcome they want (fastest, cheapest, lowest carbon footprint). The platform handles everything else: which hardware, which data path, which zone of the facility, and which compliance boundaries apply.
The outcome
Infrastructure disappears, research accelerates.
The "deployment tax" is the time researchers lose to infrastructure plumbing. On a hyperscaler, the cost is refactoring to their runtime. On bare metal, the cost is standing up a scheduling and ops team. With Amalgamy, neither applies.
The user inputs a workload description and an intent signal. Amalgamy translates that into a concrete execution plan: which nodes, which data path (move data to compute or compute to data), which facility zone has the power and cooling headroom, and which compliance boundaries constrain the placement. The user never writes a hardware script.
How it works
Intent in, execution out.
Hardware requests are hints, not mandates.
If a researcher requests a specific GPU partition, Amalgamy treats it as a preference. The system finds the best match across the full fleet, including capable older silicon.
Task-by-task recruitment.
Complex workflows are disaggregated into granular steps. Each step recruits exactly the hardware it needs for that moment, then releases it.
Late binding.
Amalgamy defers the final mapping of a task to a physical resource until the data is local and the compute is ready. No GPUs held hostage waiting for dependencies.
Fastest, cheapest, or greenest.
The user picks the optimization target. Amalgamy translates that intent into hardware selection, data routing, and facility zone placement.
No rewriting scripts.
Python, Jupyter, Bash, Airflow, MLFlow, and standard IDEs. Moving from a local experiment to a partner cluster is a data-input change, not a code rewrite.
Automatic compliance.
Policies are constraints, not guidelines. Non-compliant placements are blocked before a compute cycle is spent.
Stop managing infrastructure, start running workloads.
Technical docs and API reference at amalgamy.ai.