Outerbounds

Founded by Hamza Tahir in 2021

Build and deploy machine learning models and AI agents on scalable cloud infrastructure

Pricing model

Paid

Rating

★ People love it

Time to value

Quick Setup (< 1 hour)

You can use Outerbounds to design, run, and scale machine learning and artificial intelligence systems using standard software engineering practices. Built on top of open source Metaflow, it connects your existing cloud data stores directly to autoscaling compute clusters and GPUs. Teams can run model training experiments, build autonomous agents, manage production pipelines, and track every version with complete auditability. It enforces enterprise security policies, supports HIPAA and SOC 2 compliance, and provides unified monitoring across both offline batch jobs and online real-time prediction services.

What Outerbounds does

Configure autoscaling GPU clusters for deep learning workloadsDeploy machine learning pipelines using Python APIsTrack model artifacts and training experiment versionsMonitor execution logs and compute costs across cloud environmentsEnforce security and compliance policies on data infrastructureTransition local Metaflow code to distributed cloud production runsDirect integration with open source MetaflowAutoscaling compute layer with on-demand GPU accessEnd-to-end lineage and experiment tracking for modelsHIPAA and SOC 2 compliant environment controlsUnified execution runtime across batch and real-time workloadsDeveloper-friendly Python APIs for model orchestrationMulti-cloud deployment across private and public cloud regions