Data science & ML platforms · McKinsey & Company (QuantumBlack)

Iguazio

MLOps-focused AI platform for deploying and managing ML and generative AI applications in production.

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Iguazio is an operational AI/MLOps platform for building, deploying, and monitoring machine learning and generative AI applications in live business environments, with features covering pipeline orchestration, model monitoring, GPU resource management, and governance guardrails. The company was acquired by McKinsey & Company in 2023 and now operates as part of McKinsey's QuantumBlack AI arm, continuing to develop and support the platform alongside its open-source MLRun and Nuclio projects for pipeline orchestration and serverless functions. It deploys on any Kubernetes cluster, whether on-premises or across major public clouds, making it a fit for organizations needing flexible, production-grade MLOps rather than a full end-to-end modeling suite. No public pricing is available; the product is positioned as part of McKinsey/QuantumBlack's enterprise AI engagements.

At a glance

Vendor McKinsey & Company (QuantumBlack)
Pricing model Quote only
Free tier No
Deployment Cloud, Self-hosted
Open source No
Best for Enterprises needing production-grade MLOps deployment flexibility across cloud and on-premises Kubernetes.

Pricing

No public pricing page; the platform is sold as part of McKinsey/QuantumBlack enterprise AI engagements.

Pricing has not been verified yet — see the vendor's site.

Features

  • ML and generative AI pipeline orchestration
  • Production model monitoring
  • GPU and compute resource management
  • Governance and guardrails for deployed models
  • Multi-cloud and on-premises Kubernetes deployment
  • Open-source MLRun and Nuclio components

Integrations

Profile last reviewed September 21, 2026

Alternatives

Domino Data Lab Enterprise MLOps and data science platform for running, governing, and scaling model workflows across teams. DataRobot Enterprise AutoML and generative AI platform for building, deploying, and governing predictive models. Dataiku Collaborative platform spanning data preparation and machine learning, used by both analysts and data scientists.

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Terms to know

Machine learning Building models that learn patterns from data to make predictions or decisions, rather than following hand-coded rules. Data pipeline The automated sequence of steps that moves data from source systems to a destination, transforming it along the way. MLOps (machine learning operations) The set of practices for deploying, monitoring and maintaining machine learning models reliably in production, extending DevOps to ML systems. Model monitoring Ongoing tracking of a deployed model's inputs, predictions and outcomes to catch accuracy loss, drift or failures after launch. Workflow orchestration Scheduling, sequencing and monitoring interdependent data tasks so each step runs only after the ones it depends on succeed.

Related guides

How to choose a data science & ML platform The decisions that matter are who builds the models, how automated the modeling should be, and whether you want a cloud-native platform or a portable one.