MLOps companies, products & suppliers

Operations for classical ML models: training, registry, deployment and monitoring. Jobs: model registry; deployment pipelines; feature/model lineage.

What is MLOps?

Operations for classical ML models: training, registry, deployment and monitoring. Jobs: model registry; deployment pipelines; feature/model lineage.

What problems does it solve?

Predictive models still ship from notebooks without a registry.

Typical business use cases

  • Model registry
  • Deployment pipelines
  • Feature/model lineage

Important capabilities

  • Registry
  • CI for models
  • Rollbacks

What buyers should evaluate

  • Reproducibility
  • Who can promote
  • Monitoring hooks

Risks and governance considerations

An undocumented model in production with no rollback.

Procurement checklist

  • Registry entry
  • Promotion rights
  • Rollback

Relevant AI Trustmark assurance

AI Trustmark independent findings appear only when an assessment or certificate exists. Category membership does not imply verification.

Methodology · How verification works

Companies and providers

Claimed suppliers appear first so buyers can start with listings the company has taken ownership of. Payment does not buy this order.

  • BentoML

    Deploy any model anywhere, with tailored inference optimization, efficient scaling, and streamlined operations. BentoML is used for MLOps work. BentoML publishes product informatio

  • Dagster

    Dagster is a modern data orchestrator to build, schedule, and monitor reliable data pipelines for teams. Dagster is used for MLOps work. Dagster is recorded in United States. Dagst

  • Databricks

    Mosaic AI on Databricks unifies model building, evaluation, serving, and governance on the lakehouse. Public marketing also emphasises lakehouse-native GenAI/ML. It is presented fo

  • dstack

    dstack is a unified control plane for GPU provisioning and orchestration that works with any GPU cloud, Kubernetes, or on-prem clusters. dstack is used for MLOps work. dstack publi

  • Get AI Ready

    Get AI Ready is a UK Databricks-partner AI consulting arm delivering lakehouse, MLOps, agentic automation, and governance for enterprises. Get AI Ready (Rhino Partners).uk, MLOps.

  • Iguazio

    Iguazio publishes MLRun and Iguazio AI Platform as named AI products. Iguazio is used for Model governance and MLOps work. Iguazio is recorded in Israel. Iguazio publishes product

  • Infinite Lambda

    Infinite Lambda is a data and AI consultancy specialising in modern data platforms (often Snowflake/dbt ecosystems) for UK/EU clients. Infinite Lambda publishes Infinite Lambda dat

  • Jozu

    Jozu is the on-prem Kubernetes AI platform for secure model packaging, registry, and deployment. Jozu publishes KitOps as named AI products. Jozu is used for MLOps work. Jozu publi

  • Linux Foundation

    Helping open technology projects build world class open source software, communities and companies. Linux Foundation publishes Monocle, vLLM, and Kubeflow as named AI products. Lin

  • MindsDB

    MindsHub gives everyone the freedom to choose among leading AI models and providers—whether they use AI to get work done or build it into products. MindsDB is used for MLOps work.

  • Outerbounds

    Outerbounds commercialises Metaflow infrastructure for building and operating real-life ML and AI systems. Outerbounds publishes Outerbounds / Metaflow as named AI products. Outerb

  • Prefect

    Prefect is a workflow orchestration platform for data and ML pipelines with a modern Pythonic developer experience. Public marketing also emphasises pythonic workflow orchestration

  • Seldon

    Real-time machine learning deployment with enhanced observability for any AI application or system, managed your way. Seldon publishes Seldon Core 2 as named AI products. Seldon is

  • TrueFoundry

    TrueFoundry markets an AI gateway and MLOps platform for deploying and governing models and LLM apps in enterprise environments. Public marketing also emphasises aI gateway + MLOps

  • Union.ai

    One platform to orchestrate durable AI workflows, real-time apps, and compute optimization. Your data never leaves your infrastructure. Union.ai publishes Union as named AI product

  • Weights & Biases

    Learn why thousands of companies rely on W&B as their system of record for training AI models and developing AI applications with confidence. Weights & Biases publishes W&B Serverl

  • ZenML

    ZenML Labs is the unified infrastructure layer for AI in production: ZenML orchestrates your pipelines and agents on the infra you choose, and Kitaru replays them on production dat

Products

Claimed products appear first. Ranking packs and payment do not change this list.

Also used in this category

These products have a different primary category so they do not compete for the same ranking queries. They are listed here because buyers still encounter them in this job.

Related categories

Relevant procurement and assurance guides

Frequently asked questions

What is MLOps?

Operations for classical ML models: training, registry, deployment and monitoring. Jobs: model registry; deployment pipelines; feature/model lineage.

What should not be listed as MLOps?

Products whose buyer job is LLMOps, experiment trackers alone, or AutoML products. Those belong on their own category page so search queries are not split.

Has AI Trustmark independently assessed every MLOps supplier?

No. A category listing is descriptive. Independent assessment is shown only on company or product pages that carry Trustmark evidence.

What logging should an AI agent provide?

Buyers should be able to see who the agent acted as, which tool was called, what data was sent, what changed, and when. Logs that omit write actions or store raw customer prompts without access control are incomplete evidence.

What model or provider changes should a buyer insist on being told about?

Material change usually includes a new model family, new region, new subprocessor, new write-capable tool, or a change that affects logging, privacy or human oversight. Those changes should trigger evidence refresh rather than a silent release.

How should buyers verify where AI customer data is processed?

Ask for the named processing locations, cloud regions and any subprocessors that see prompts, files or outputs. A directory listing is not evidence of residency. Independent assessment records the locations that were in scope on the assessment date.

Does a TrustMark on one product cover the rest of the company?

No. Independent assessment is scoped to the named organisation and, where relevant, the named product. Category pages list suppliers as a topic label. They do not imply that every listed company has been assessed.

What incident-handling evidence is useful for AI suppliers?

Buyers should see how AI-specific failures are detected, contained and notified — including unsafe outputs, data leakage and unauthorised agent actions. An incident policy that never mentions models, prompts or tools is incomplete for this class of product.