MLOps tooling has consolidated in 2026. The category split into two camps: end-to-end platforms that handle everything from notebook to production, and best-of-breed tools that do one thing well and integrate with everything else. Most teams need a hybrid.
We ran every platform here against three real workloads: a fine-tuning pipeline for a 7B language model, a tabular fraud detection system retrained nightly, and a computer vision model serving 10k requests per second. What follows is what actually worked.
1. Weights & Biases — Score: 9.2/10
[[weights-biases]] remains the gold standard for experiment tracking and has matured into a credible end-to-end platform. The new Weave LLM observability layer is the best in class for tracing agent workflows, and Launch handles distributed training across clusters without the Kubeflow tax. The UI is still the most thoughtful in the category — sweeps, reports, and artifacts all just work.
Best for: Research teams, LLM fine-tuning, anyone who runs more than 50 experiments a week.
Pricing: Free for personal and academic use. Teams start at $50/user/month. Enterprise pricing scales with compute hours tracked.
2. MLflow — Score: 8.8/10
MLflow is the open-source backbone of most ML stacks for a reason. MLflow 3.0 added native GenAI support, prompt versioning, and a meaningfully better model registry. If you're running on Databricks it's the default; self-hosted it requires more glue but costs nothing. The tracking API has barely changed in five years, which is a feature not a bug.
Best for: Teams who want vendor independence, regulated industries, anyone already on Databricks.
Pricing: Free and open source. Managed hosting via Databricks bundled with their platform pricing.
3. Databricks — Score: 8.7/10
[[databricks]] keeps pulling more of the MLOps stack into its lakehouse. The Mosaic AI integration means you can fine-tune, evaluate, serve, and monitor models without leaving the platform, and Unity Catalog finally makes governance feel like it was designed rather than retrofitted. The cost is real — Databricks is not cheap — but for teams already standardized on it the ROI is hard to argue with.
Best for: Enterprises with significant data warehouse spend, teams unifying analytics and ML.
Pricing: Consumption-based DBUs. Realistic ML workload budgets start around $5k/month and scale up.
4. Vertex AI — Score: 8.5/10
[[vertex-ai]] is Google Cloud's bet on managed MLOps, and in 2026 it's finally cohesive. Model Garden, Pipelines, Feature Store, and Model Monitoring all share consistent abstractions, and the Gemini integration is tighter than what AWS offers with Bedrock. The downside is the usual GCP tax — if your data lives elsewhere, the egress math gets ugly fast.
Best for: Teams already on GCP, anyone fine-tuning Gemini or PaLM derivatives.
Pricing: Pay-per-use across pipelines, endpoints, and feature serving. Budget $2-10k/month for production workloads.
5. SageMaker — Score: 8.2/10
[[sagemaker]] is sprawling, occasionally confusing, but undeniably comprehensive. The HyperPod offering for distributed training is now production-ready, and SageMaker Studio finally feels like one product instead of seven stitched together. If you're on AWS, this is the default answer — just budget time for the learning curve and engineers who know IAM.
Best for: AWS-native teams, regulated workloads needing VPC isolation.
Pricing: Pay-per-use with notorious complexity. Expect $3-15k/month for serious production use.
6. Kubeflow — Score: 7.8/10
[[kubeflow]] is what you reach for when you've outgrown managed platforms or have compliance reasons to run everything in your own Kubernetes cluster. The 1.9 release improved Pipelines significantly and KServe is now the reference for model serving on K8s. The operational burden is still real — plan on a dedicated platform engineer.
Best for: Teams with Kubernetes expertise, on-prem deployments, GPU clusters at scale.
Pricing: Open source. Real cost is infrastructure and platform engineering headcount.
7. ClearML — Score: 7.9/10
[[clearml]] is the underrated dark horse here. It does experiment tracking as well as W&B, adds orchestration that's lighter than Kubeflow, and the self-hosted option is genuinely free with no asterisks. The UI is less polished than W&B and the community is smaller, but for teams who need an open-source full stack without the Kubeflow operational burden, it's the right answer.
Best for: Self-hosted full-stack MLOps, teams avoiding vendor lock-in.
Pricing: Free self-hosted. Pro tier starts at $15/user/month for managed hosting.
8. DVC — Score: 7.5/10
[[dvc]] isn't trying to be an MLOps platform — it's data and model versioning that plugs into git. That focus is its strength. Studio (the hosted UI) added meaningful collaboration features this year, and the new VS Code extension makes it actually pleasant to use. Pair it with MLflow or W&B for tracking and you have a credible stack for under $100/month.
Best for: Teams who already live in git, reproducible research workflows.
Pricing: Free open source. Studio is free for up to 3 users, $30/user/month above that.
Comparison Table
| Platform | Score | Best For | Starting Price | Self-Host? |
|---|---|---|---|---|
| Weights & Biases | 9.2 | Research, LLMs | $50/user/mo | Enterprise only |
| MLflow | 8.8 | Vendor independence | Free | Yes |
| Databricks | 8.7 | Lakehouse teams | ~$5k/mo | No |
| Vertex AI | 8.5 | GCP-native | Pay-as-you-go | No |
| SageMaker | 8.2 | AWS-native | Pay-as-you-go | No |
| ClearML | 7.9 | Open-source stack | Free | Yes |
| Kubeflow | 7.8 | K8s at scale | Free | Yes |
| DVC | 7.5 | Git-native versioning | Free | Yes |
Final Picks
If you have a budget and want the best experience: [[weights-biases]]. The tracking is unmatched and the platform has grown into the surrounding workflow without losing focus.
If you want vendor independence: MLflow for tracking, [[dvc]] for data versioning, [[clearml]] if you want a fuller stack. All three give you portable artifacts and no lock-in.
If you're already on a hyperscaler: Use the native option — [[sagemaker]] on AWS, [[vertex-ai]] on GCP, [[databricks]] if you're a heavy lakehouse user. The integration tax of going elsewhere usually isn't worth it.
If you have a Kubernetes platform team: [[kubeflow]] is still the most flexible, and you'll appreciate it at scale. Don't choose it if you don't have the operational headcount.
The honest meta-take: MLOps is no longer about picking the perfect platform. Pick the one that matches your existing infrastructure and team skills, then spend your energy on the work that actually moves the model — data quality, evaluation, and monitoring.