The honest framing is that MLOps is still a discipline of composable tools. You pick an experiment tracker, a model registry, a deployment runtime, and a monitoring layer - sometimes from the same vendor, often not. The platforms below are evaluated on how well they do each job, what they actually cost at real usage volumes, and where open-source alternatives get you 80% of the way there.

Methodology

This comparison evaluates platforms across four areas: experiment tracking (run logging, metric comparison, artifact storage), model registry (versioned model storage, stage transitions, lineage), deployment and serving (real-time endpoints, batch, serverless, LLM streaming), and monitoring (data drift, inference quality, latency alerts). This is explicitly distinct from LLM observability tools like Langfuse or Braintrust - that coverage lives in the AI observability comparison.

Rankings at a Glance

Weights & Biases - Still the Experiment Tracking Standard

W&B built its reputation doing one thing extremely well: making training runs reproducible and comparable. The core product - run logging, metric panels, hyperparameter sweeps, artifact tracking - remains best in class in 2026. The UI is genuinely good. Integration coverage (PyTorch, JAX, HuggingFace, LightGBM, XGBoost, and more) is wider than any competitor.

The LLM story runs through W&B Weave, their evaluation and tracing product. Weave captures LLM traces, builds evaluation datasets from production traffic, and integrates with W&B's core experiment tracking. For teams already on W&B it is the obvious extension - same data model, no second vendor.

What it doesn't do: W&B is not a deployment platform. No native model serving, no inference endpoint management, no pipeline orchestration. The "full MLOps" marketing does not match the product.

Pricing: Individual/Academic free (unlimited runs, 100GB storage). Team at $50/month base + $25/seat/month. Enterprise custom. See wandb.ai/site.

Gotcha: Per-seat billing compounds fast at 5+ seats with serious artifact storage. Run the math before committing.

MLflow - The Open-Source Baseline You Should Try First

MLflow is a Databricks-backed open-source project covering experiment tracking, model registry, and basic deployment in one library. If you are running your own infrastructure and need a capable MLOps layer without a vendor bill, start here.

The model registry is MLflow's strongest component: versioned model storage, stage transitions (None/Staging/Production/Archived), annotation support, and lineage linking experiments to registered models. No competitor's open-source registry is as well-implemented.

Deployment via mlflow models serve gives you a Flask-based REST API wrapper. It is functional but not production-grade on its own - for anything serious, package the MLflow model and deploy it into KServe or BentoML.

Every major ML framework has MLflow autologging. HuggingFace Transformers, PyTorch Lightning, LightGBM, XGBoost all instrument in one line.

Pricing: Fully open-source Apache 2.0. Free to self-host. Databricks Managed MLflow available on DBU billing. See mlflow.org.

Gotcha: Self-hosted MLflow means managing the tracking server, artifact store, and database yourself. It is not hard, but it is ops overhead the managed vendors eliminate.

ClearML - Most Complete Open-Source Stack

ClearML is the open-source MLOps platform that comes closest to matching commercial platforms on feature breadth. The project covers experiment tracking, data versioning, model registry, pipeline orchestration, and infrastructure management under Apache 2.0.

The orchestration angle is a real differentiator versus W&B or Neptune. ClearML Pipelines builds dependency graphs between tasks, handles artifact passing, and supports dynamic pipeline construction. ClearML Serving adds model deployment with auto-scaling and basic drift monitoring - enough for many production workloads and one fewer vendor to manage.

Pricing: Community edition is free and open-source. ClearML Hosted Pro at $75/seat/month (minimum 2 seats). Enterprise custom. See clear.ml/pricing.

Gotcha: The self-hosted stack requires real infrastructure ops investment. Production deployments need Kubernetes and tuning - Docker Compose is development only.

Neptune.ai - Best for Experiment-Heavy Research

Neptune is narrowly focused: experiment metadata tracking done well, with essentially no deployment story. The metadata model is flexible - Neptune treats every logged entity as a field in a queryable namespace. You search and filter on arbitrary logged fields, not just predefined ones. This pays off when experiments evolve and you want to analyze dimensions you did not anticipate.

Pricing: Free tier available. Paid plans scale by seats and usage. See neptune.ai/pricing.

Gotcha: Neptune is an experiment store, not an MLOps platform. You need separate tooling for everything past the training run.

KServe and Seldon Core - Open-Source Model Serving

KServe (formerly KFServing) is the CNCF Kubernetes model serving framework that handles inference service lifecycle management, GPU scheduling, canary deployments, and multi-model serving. It supports every major model format - SKLearn, XGBoost, TensorFlow, PyTorch, ONNX, HuggingFace Transformers, and custom containers. The vLLM integration handles tensor-parallel LLM serving across GPUs.

The value over managed services: KServe on your own Kubernetes cluster gives you production-grade model serving at infrastructure cost only, with no per-request fees and no vendor lock-in. At serious inference volume, this math works heavily in your favor versus SageMaker or Vertex AI.

Seldon Core covers the same Kubernetes serving ground and adds production monitoring - outlier detection and drift detection components run alongside your inference service and flag when input distributions shift. The enterprise version adds explainability, advanced drift algorithms (MMD, LSDD), and dashboards.

Pricing: Both are open-source. KServe at kserve.github.io/website. Seldon Core at seldon.io - note the BSL license change from Apache 2.0, check current terms carefully for commercial use.

Gotcha: Kubernetes is non-negotiable. The learning curve for custom serving containers is real.

BentoML and BentoCloud - Fastest Path from Model to API

BentoML is the tool I reach for when someone needs to get a trained model serving HTTP requests as quickly as possible. You write a Service class that wraps your model, define input/output types, and bentoml serve gives you a REST API with OpenAPI docs, batching support, and GPU allocation in under an hour.

The LLM serving story is strong. OpenLLM, BentoML's wrapper for open-source LLMs, handles quantization, streaming, and OpenAI-compatible APIs without manual configuration. For teams running open-source LLMs in production, this is the lowest-friction path.

BentoCloud is the managed deployment platform: push a BentoML Service and BentoCloud handles container builds, auto-scaling, and cold-start optimization.

Pricing: BentoML fully open-source Apache 2.0 at bentoml.com. BentoCloud has a free tier and usage-based paid tiers.

Gotcha: BentoML is model serving, not MLOps. No experiment tracking, no model registry with stage management, no pipeline orchestration. Combine it with MLflow for the full workflow.

Cloud-Native Platforms - SageMaker, Vertex AI, Azure ML

All three major clouds offer managed MLOps stacks. The honest assessment: none of them is best-in-class on any individual capability, but the integration premium is real if your data and infrastructure are already there.

SageMaker MLOps bundles experiment tracking, model registry, pipeline orchestration, and deployment on AWS. SageMaker Endpoints supports auto-scaling, shadow deployment for A/B testing, and deep integration with SageMaker Clarify for bias monitoring. Pricing is usage-based and notably complex - training jobs, endpoints, and pipeline runs all meter separately. Budget surprises are common in the first 90 days. See aws.amazon.com/sagemaker.

Vertex AI Pipelines covers the same lifecycle from Google's side, with the added advantage of native Gemini integration. Fine-tuning and serving Gemini models flows naturally through Vertex with no friction. For teams building on both proprietary Google models and custom-trained models, that consolidation is useful. IAM and service account setup is meaningfully more complex than SageMaker. See cloud.google.com/vertex-ai.

Azure ML is the strongest option for teams on Microsoft infrastructure, with tight Azure DevOps integration and clear compliance pathways for regulated industries through Azure's compliance certifications. See azure.microsoft.com/products/machine-learning.

Gotcha for all three: Vendor lock-in at the model registry and serving layer is real. Migrating off a cloud-native MLOps platform is a larger project than it looks. Know what you are committing to.

Niche Picks Worth Knowing

ZenML is a pipeline-first MLOps framework where everything is a pipeline step and pipelines are portable across Kubeflow, Vertex AI, SageMaker, and local execution without changing step code. Open-source with a Pro tier at zenml.io/pricing. Right choice for teams that switch orchestrators or run pipelines in multiple environments.

DVC is not an MLOps platform - it is data and model versioning with Git. Use it alongside MLflow or W&B to get reproducible data pipelines. Free Apache 2.0 at dvc.org.

Determined AI (HPE) focuses on distributed GPU training: multi-node runs, fault tolerance (auto-resume from last checkpoint), and hyperparameter search on shared GPU clusters. Open-source at determined.ai. Right choice if your bottleneck is training scale, not serving.

Metaflow (Netflix open-source) handles workflow orchestration for data scientists - Python steps that run locally or on AWS Batch, with versioned artifact tracking. Not a full MLOps platform. Free at metaflow.org.

Valohai enforces an immutable audit log on every execution - inputs, outputs, code, environment, and infrastructure are recorded permanently. For regulated industries where reproducibility is a compliance requirement, this is the only platform that makes it structural rather than aspirational. Enterprise pricing at valohai.com/pricing.

MLRun (Iguazio/QuantumBlack) combines pipeline orchestration with an integrated feature store, which eliminates training-serving skew - a common production failure mode for real-time ML. Open-source core at mlrun.org.

Dagster and Prefect are data pipeline orchestrators, not MLOps platforms. They are excellent for the workflow around ML (data ingestion, feature engineering, batch prediction) but do not replace experiment trackers or model serving platforms. Use them alongside MLflow, not instead of it. See dagster.io/pricing and prefect.io/pricing.

The Honest Bottom Line

Start with MLflow if you have your own infrastructure and want zero licensing cost with solid tracking and a good model registry. Pair with BentoML for serving. That is a complete open-source stack.

Use W&B if your team does serious ML research with lots of experiments and needs the best-in-class tracking UI. Accept that billing scales with team size and that you still need a separate deployment layer.

Choose a cloud-native option only if you are already deeply committed to that cloud and the integration premium justifies the lock-in cost. These platforms are operationally convenient but rarely best-in-class on individual capabilities.

Use KServe or Seldon if you are Kubernetes-native and want production-grade serving without per-request vendor fees. The ops investment pays back at meaningful inference volumes.

The "end-to-end MLOps platform" pitch from any single vendor is almost always overselling. The composable approach - best-of-breed experiment tracker, best-of-breed serving - consistently outperforms the single-vendor strategy for teams at any serious scale.

Sources

• Weights & Biases
• MLflow Documentation
• Comet ML Pricing
• ClearML Pricing
• Neptune.ai Pricing
• ZenML Pricing
• DVC Official Site
• Kubeflow Official Site
• KServe Documentation
• Seldon Official Site
• BentoML Official Site
• AWS SageMaker
• Google Vertex AI
• Azure Machine Learning
• Metaflow Official Site
• Determined AI
• Valohai Pricing
• Prefect Pricing
• MLRun / Iguazio
• Polyaxon on GitHub
• Dagster Pricing