MLOps & Experiment Tracking brand directory

MLflow

Rank #1 · 37.6% visibility

MLflow is an open-source platform spanning the complete AI and ML lifecycle—experiment tracking, model registry, deployment, LLM/agent tracing, evaluation, prompt optimization, and an AI Gateway—used by thousands of organizations and available free under Apache 2.0 or as a managed enterprise service via Databricks.

Weights & Biases

Rank #2 · 18.4% visibility

Weights & Biases is an AI developer platform comprising W&B Models (experiment tracking, hyperparameter sweeps, artifact versioning, model registry), W&B Weave (LLM tracing, evaluation, agentic observability, guardrails, online monitoring), W&B Inference (hosted open-source foundation model API), and W&B Training (serverless RL and SFT fine-tuning). A unified SDK enables one-line integration with all major ML frameworks. The platform serves as a system of record across the full AI development lifecycle for both model builders and LLM application developers.

ClearML

Rank #3 · 7.2% visibility

ClearML is a full-stack, open-source AI infrastructure and MLOps platform that enables data science, ML engineering, DevOps, and IT teams to manage the complete AI lifecycle—from experiment tracking and data versioning through GPU orchestration, pipeline automation, and GenAI deployment—on any infrastructure.

Comet ML

Rank #4 · 4.8% visibility

Comet is an end-to-end AI developer platform with two core product families: (1) a MLOps platform covering experiment tracking, model registry, dataset versioning, and production monitoring for teams building and training ML models; and (2) Opik, a truly open-source LLM observability and evaluation platform for tracing, testing, optimizing, and monitoring LLM applications and agentic workflows—available via self-hosted OSS, managed cloud, or enterprise deployment.

ZenML

Rank #5 · 3.2% visibility

ZenML is an open-source MLOps framework and AI control plane that enables teams to build, orchestrate, version, and govern machine learning and AI agent pipelines across any infrastructure. Using Python decorators, practitioners wrap existing ML code into pipeline steps that run identically from local development to cloud-scale Kubernetes production. ZenML's stack abstraction decouples pipeline logic from infrastructure choices, providing 60+ integrations with orchestrators, experiment trackers, model registries, cloud providers, and GenAI frameworks. ZenML Pro, the managed SaaS offering, adds enterprise governance features including a Model Control Plane, Artifact Control Plane, RBAC, SSO, audit logs, and environment snapshot versioning.

Anyscale

Rank #6 · 0.0% visibility

Anyscale Platform is a managed AI compute platform powered by Ray (and its enterprise-optimized variant RayTurbo), built by the creators of Ray. It provides developers and platform engineering teams with the infrastructure to run distributed AI workloads—data processing, model training, batch inference, model serving, and agentic pipelines—at scale across CPUs and GPUs on any cloud or on-premises environment, without requiring deep distributed systems expertise.