Most teams shopping for an MLOps platform are solving the wrong problem. They go looking for the one tool that does everything, sign a per-seat contract, and six months later they’re using maybe a third of it while gluing the rest together with scripts. The useful question isn’t which platform is best. It’s which of the jobs an MLOps stack does you actually need a product for right now, and which you can get from a free library and an afternoon.
There are six of those jobs: tracking experiments, versioning and registering models, orchestrating training pipelines, serving models in production, watching for drift once they’re live, and sharing features across teams. No single product does all six well. The all-in-one platforms check every box but are mediocre at a few. The focused tools are excellent at one or two and silent on the rest. Knowing which boxes you care about is the whole decision.
Experiment tracking is where most teams start, and overspend
If you train models and can’t answer “which run produced the checkpoint in prod and what data did it see,” tracking is the first thing worth buying. It’s also the layer with the most options and the widest price spread, which is exactly why teams waste money here.
MLflow is the default, and for good reason. It’s open source, free, runs on your own infrastructure, and the tracking plus model registry covers what most teams need. You log params, metrics, and artifacts, promote a model through stages in the registry, and nothing is tied to a vendor. The catch is that you operate it: someone stands up the tracking server and the backing store, and the UI is plain next to the paid tools. MLflow 3 also leaned hard into GenAI tracing, so if you’re evaluating LLM apps it now covers more than loss curves.
Weights & Biases is the upgrade people pay for once the team is large enough that visualization and collaboration matter more than the bill. The dashboards are genuinely better, hyperparameter sweeps are smooth, and a researcher can share a report without anyone touching infrastructure. It’s also among the priciest, somewhere in the range of $50 to $60 per user per month on team plans, which adds up fast across a 20-person org. Pull their current pricing page before you budget, because these numbers move.
ClearML sits in the middle and is the one teams overlook. Self-hosted it’s free with no feature gates, and the paid Pro tier runs around $15 per user per month while bundling orchestration and autoscaling that W&B either charges separately for or doesn’t do. Comet is in similar price territory and strong on tracking, but it stops at experiment management and won’t serve models or schedule GPUs for you.
A real cautionary tale from this year: Neptune wound down its standalone tracking SaaS, and teams that built on it had to migrate. That’s the risk with a single-purpose vendor, and it’s a fair argument for keeping your tracking layer either open source or on a platform big enough that it won’t quietly exit the market.
The cloud all-in-ones: SageMaker, Vertex AI, Databricks
If your data already lives in AWS or Google Cloud, the gravity is real. SageMaker and Vertex AI cover every box on the list, integrate with the storage and compute you’re already paying for, and bring the governance and audit trails that compliance teams ask for. For a regulated enterprise, that last part alone can settle the question.
The trade is depth against friction. SageMaker has the deeper feature set and the steeper learning curve; you will meet a great deal of configuration. Vertex AI is easier to move around in and tighter with BigQuery and the rest of Google’s stack, at the cost of some flexibility. Databricks is the third option, strongest if your people already live in notebooks and Spark, and it ships managed MLflow so you get the open-source tracking API without running the server yourself. Pricing for all three is usage-based and hard to predict up front, so model your real workload before committing rather than trusting a sticker number.
The failure mode with these platforms is buying the whole thing for one feature. Plenty of teams adopt SageMaker for managed endpoints, then resent paying its tax on training they could run more cheaply on raw instances. You can use one piece without surrendering the entire workflow, and you usually should.
Orchestration and serving, where the all-in-ones leak
Past tracking, the question becomes how a model goes from a training run to a versioned artifact to a live endpoint without a human copy-pasting. This is the orchestration layer, and it’s where the cloud platforms feel heaviest and the focused tools earn their keep.
Kubeflow is the answer if you already run Kubernetes and have someone who knows it well. It’s powerful and it’s a lot of operational weight; teams without a platform engineer tend to drown in it. ZenML takes a different line, sitting on top as a framework that lets you write a pipeline once and run it across backends, which keeps you from marrying any single orchestrator. It’s open source and self-hostable, with a paid SaaS around the same per-seat range as W&B. Metaflow, out of Netflix, is the pick when you want data scientists shipping pipelines without becoming infrastructure engineers.
For serving specifically, BentoML and Ray Serve are the names worth knowing. BentoML packages a model and its dependencies into something you deploy as a container; Ray Serve shines when you need to scale inference across a cluster or compose several models behind one endpoint. Neither tracks experiments, and that’s the point. They do one job and get out of the way.
Watching models after they ship
A model that was accurate at launch quietly rots as the world drifts away from its training data. Monitoring catches that, and it’s the box teams skip until an incident forces the issue. Evidently is the common open-source choice for drift and data-quality checks, and it slots in next to whatever else you run. The cloud platforms have monitoring built in, which is one more reason they appeal to teams that would rather not assemble this part themselves.
What the GenAI shift changed
A lot of what gets called MLOps in 2026 is really LLM application work, and the tooling followed. Tracking a prompt pipeline and scoring its outputs looks nothing like logging a loss curve. W&B built Weave for this, MLflow added tracing, and a wave of LLM-native tools like Langfuse and LangSmith showed up aimed squarely at evaluation, prompt versioning, and token-cost tracking. If your roadmap is mostly retrieval and agents rather than training classifiers, weigh those as seriously as the classic platforms, because the classic ones are still catching up here.
A quick map by where you actually are
| Tool | Best at | Rough cost | The catch |
|---|---|---|---|
| MLflow | Tracking and registry, vendor-neutral | Free, self-hosted | You run and maintain it |
| Weights & Biases | Visualization, collaboration, sweeps | ~$50–60/user/mo (verify current) | Cost scales with headcount |
| ClearML | Tracking plus orchestration | Free self-hosted; ~$15/user/mo Pro | Smaller community than MLflow |
| Comet | Experiment tracking | ~$19/user/mo | No serving or GPU scheduling |
| SageMaker / Vertex AI | Full lifecycle, governance | Usage-based, hard to predict | Lock-in; you pay for boxes you skip |
| ZenML / Kubeflow | Pipeline orchestration | Open source (ZenML SaaS ~$50/user/mo) | Kubeflow ops weight; ZenML is newer |
For a one-to-five person team or an early project, run MLflow for tracking and registry, add a serving framework like BentoML when you genuinely have something to deploy, and add nothing else until a real pain shows up. Resist the per-seat SaaS. You don’t have the scale to justify it and the open-source tools cover you fine.
For a 10-to-30 person ML org, pick one orchestrator (Kubeflow if you’re Kubernetes-native, ZenML or Metaflow if you’d rather not be), MLflow or W&B for tracking depending on whether visualization is worth the cost, a feature store like Feast only once multiple models share features, and Evidently for monitoring. This is the configuration most growing teams settle into, and it’s mostly open source with one or two paid pieces where they earn their place.
For an enterprise carrying compliance and audit requirements, the realistic choices are SageMaker, Vertex AI, or Databricks, picked mostly by which cloud already holds your data. The governance and the single vendor relationship are worth more than best-in-class point tools once auditors are in the room.
The mistake that costs the most isn’t picking the wrong tool. It’s picking too many too early, or signing a platform contract sized for a team you don’t have yet. Find the one box that’s actually on fire, buy the cheapest thing that puts it out, and add layers only when a specific pain tells you which one. Almost nobody looks back and wishes they’d started bigger.
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