metaPlay is a cloud software platform that enables data scientists to deploy ML/AI directly to business stakeholders in minutes, not months - streamlining the process for unlocking real ROI. With composable workflows, first-class validation, and zero vendor lock-in, metaPlay delivers the velocity and cost efficiency traditional platforms can't match.
First model deployed in minutes vs months compared to legacy approaches. Solve the "Last Mile" problem between development and business impact.
Annual pricing with minimal infrastructure overhead—unlike legacy platforms with significant markups and usage-based fees.
Multi-cloud by design. Switch providers without rearchitecting. User workloads are highly portable.
Every deployed model includes full lifecycle tracking. Align with regulatory frameworks and standards from day one.
metaPlay orchestrates; it doesn't replace. API-first architecture means if it's Python-compatible, it works:
Data sources: Pre-built integrations data warehouses, RDBMS, NoSQL, vector DBs and object stores + pluggable integration API
ML frameworks: PyTorch, TensorFlow, scikit-learn, XGBoost – any Python ML/AI library
Compute: AWS, GCP, Azure, on-premises—multi-cloud by design
Version control: GitHub, GitLab, Bitbucket
Compose data integrations, pipelines, and models into reusable objects that are immediately available for downstream modeling, testing and endpoint provisioning. Define once in Python, reuse everywhere. Work with data where it lives across multiple clouds and without centralization.
Models deployed in minutes. Every building block in a workflow is fully auditable. metaPlay’s composable framework means velocity and awareness compound through reuse.
metaPlay provides built-in model validation capabilities across LLM, timeseries and traditional ML models, along with the flexibility to customize as needed. Complete provenance from training to deployment ensures models meet quality standards throughout their lifecycle.
Scale ML/AI confidently while aligning with regulatory frameworks and standards (EU AI Act, NIST AI RMF). Prevent production ML/AI failures before they happen.
Every deployment is automatically shipped under Zero-Trust security. All user defined code is automatically committed to Git for safe versioning. Robust access controls enable clear administration and least privilege operations while empowering self-service velocity.
Move fast without breaking things. Enterprise security built-in from day one.
Connect to existing data sources across multiple clouds—SQL databases, data warehouses, object storage—with no required replications or migrations.
Cross-platform APIs, Git integration, and proprietary access-control framework enable seamless collaboration across teams.
Automatically handles deployment management, version control, and security enforcement. The orchestration layer that makes safe self-service possible.
Deploy models as ML inference APIs, data APIs, or interactive dashboards that business stakeholders consume immediately.