ATLANTA – Adviser Labs, an emerging deep-tech startup at the intersection of cloud HPC, AI, and quantitative computing, is building Adviser, a platform designed to radically simplify the orchestration and optimization of compute-intensive workloads across multi-cloud environments. With Adviser, engineers, researchers, and quantitative teams can access the power of large-scale clusters without the usual complexity of DevOps, manual resource tuning, or cloud cost management.

At the core of the platform is its breakthrough “adviser run” command: a unified CLI and GUI interface that transforms how HPC users interact with the cloud. Instead of writing job-scheduler scripts or configuring infrastructure manually, users can replace commands like: ./my_simulation.py with adviser run python ./my_simulation.py

The command works with any executable, not just Python — users can run precompiled binaries, C, Go, R, Perl, or FORTRAN applications seamlessly. Adviser automatically provisions optimized GPU/CPU clusters, selects the most cost-effective cloud resources, and manages job execution, giving users instant access to high-performance compute power without requiring deep cloud expertise.

Adviser Labs is already supporting critical workloads across quantitative finance, AI training, biotech, and energy, where compute bottlenecks can stall innovation and result in significant costs.

Unlike traditional HPC schedulers or rigid cloud orchestration tools, Adviser is architected natively for a multi-cloud, containerized world. The platform integrates directly with AWS, Azure, and GCP, and supports BYOC (bring-your-own-cloud) and hybrid/on-prem clusters via Kubernetes.

Key Differentiators:

Adviser Labs has raised approximately $1M in pre-seed funding from Drive Capital, Simplex Ventures, and Unusual Ventures, alongside early-stage funds and angel investors from leading tech companies including DoorDash.