Best Experiment Tracking Tools for Small ML Teams (2026)

If your ML team has fewer than ten people and your experiments still live in spreadsheets, notebook filenames and Slack messages, pick one tracker this week and stop arguing about it. Our short answer: start with MLflow if you want open source and zero vendor risk, choose Weights & Biases if you want the best dashboards and will pay for them, take ClearML if you also want pipelines and remote execution in the same tool, use Comet if your real work is LLM apps, and add DVC when the thing you cannot reproduce is your data rather than your metrics.

The more useful story this year is not features. It is ownership. In roughly twelve months, one popular tracker was shut down, one was bought by a GPU cloud, and one changed stewards. That should change how you buy. We read each vendor's own pricing and product pages, plus the announcements behind the deals, and added our own opinion. Where a number comes from a vendor, we say so. For the wider category map, see our AI & ML guide, the AI & ML topic page and the DevTools guide.

The quick comparison

Prices are list prices from the vendors' own pages in October 2026. They change, so check before you budget.

What changed for experiment tracking in 2026?

Three ownership changes landed within about a year, and each one affects a small team differently.

Neptune is gone as a hosted product. InfoWorld reported on December 4, 2025 that OpenAI had agreed to acquire Neptune, and that Neptune was withdrawing its products from the market. The hosted app and API were scheduled to be turned off on March 4, 2026, with remaining hosted data deleted, and Neptune published guides for moving to MLflow or Weights & Biases. If you are reading an old "best tracker" list that still recommends Neptune, close the tab. We keep the Neptune profile for reference, but treat it as a cautionary tale: a tool you rent can be withdrawn on a few months' notice.

Weights & Biases now belongs to CoreWeave. CoreWeave announced completion of the acquisition on May 5, 2025 and said it remains committed to interoperability of the W&B platform. The practical sign is on the pricing page, which now sits under a "CoreWeave Forge" brand with W&B's experiment tracking shown as "Weights & Biases Models". Nothing about that makes the product worse today. But a GPU cloud will steer the roadmap toward training and inference on its own infrastructure, so keep your logging code portable.

DVC changed hands. lakeFS announced on November 18, 2025 that it had acquired the DVC open-source project from Iterative.ai, saying DVC continues as an independent open-source tool for data scientists working with smaller datasets while lakeFS focuses on enterprise-scale data versioning. The announcement did not spell out governance details, so watch the repository activity rather than the press release.

Our read: the era of "pick the trendiest tracker" is over. The question for a small team is now how cheaply you can leave. Open formats, a self-hostable server and a thin logging wrapper matter more than a dashboard feature you will use twice a year.

Which experiment tracker should a small ML team pick?

MLflow: the safe default

MLflow is open source under the Apache 2.0 licence, and its site claims more than 30 million downloads a month and describes it as backed by the Linux Foundation. That scale is the real feature. Almost every training framework, cloud platform and orchestrator speaks MLflow, which is why it is the migration target Neptune itself recommended. Our listing notes integrations with Databricks, AWS SageMaker, Azure ML and Google Vertex AI.

It has also moved well beyond classic tracking. The current site covers experiment tracking, a model registry, and for LLM work it adds tracing built on OpenTelemetry, evaluation with 50+ built-in metrics and LLM judges, a prompt registry and an AI Gateway. For a five-person team that means one open tool can follow you from a scikit-learn baseline to an agent in production.

The trade-off is polish. You run the tracking server, manage the artifact store and live with a UI that is functional rather than delightful. If nobody on the team wants to own a small piece of infrastructure, that cost is real. Pick it when portability and zero licence cost outrank pretty charts.

Weights & Biases: the best dashboards, with a landlord

Weights & Biases is still the tool researchers reach for when they want to compare runs fast. The free plan is positioned for personal development and includes evaluations, tracing, experiment tracking and an asset registry. The Pro plan starts at $60 per month, is aimed at professionals and early-stage teams with fewer than 50 employees, adds team-based access controls, service accounts, CI/CD automations and alerts, and comes with a 30-day free trial. Extra storage is listed at $0.03 per GB per month. Enterprise is by quote and adds options such as single sign-on, audit logs and customer-managed encryption keys. Students and academic researchers can get a free Pro licence.

Our opinion: this is the strongest product here for teams that debug training runs all day, and the free tier is generous enough to start on. The risk is not capability but dependence. Wrap your logging calls in a small internal function so a future move to anything else is a one-day job, not a sprint. See how it stacks up in MLflow vs Weights & Biases and ClearML vs Weights & Biases.

ClearML: tracking plus the plumbing

ClearML is the pick when you want more than a scoreboard. Its Community plan is free for teams of up to three and lists dataset versioning, experiment management, a model repository, pipelines, agent orchestration and CI/CD automation, with 100 GB of artifact storage, 1 GB of metric events and 1 million API calls a month included. The Pro plan is $15 per user per month plus usage, for teams of up to ten, and adds cloud autoscaling on AWS, GCP and Azure, hyperparameter optimisation and pipeline triggers. Extra usage is priced at $0.10 per GB of artifact storage and $1 per 100,000 API calls. You can also self-host the open-source version from GitHub.

For a small team without a dedicated platform engineer, ClearML's remote execution and autoscaling can replace a stack of glue scripts. The cost is a bigger surface area to learn and a pricing model with a usage component you should model before committing. It sits well alongside compute providers such as Modal or Anyscale if you outgrow a single GPU box.

Comet: choose it for LLM apps, not for nostalgia

Comet built its name on experiment tracking, and its navigation still lists experiment management, artifacts and a model registry. But the current pricing page leads with Opik, its LLM observability and evaluation product. Opik is available as a free open-source download, a Free Cloud plan with up to 10 team members, 25,000 spans a month and 60-day data retention, and a Pro Cloud plan at $19 per month with up to 50 members and 100,000 spans a month. Enterprise lists SOC 2, ISO 27001, HIPAA and GDPR compliance.

We could not confirm separate pricing for the classic experiment-management product from that page, so ask Comet directly if tracking classical models is your main need. If you are building agents and need traces, test suites and annotation queues, Comet is a credible specialist. See Comet vs Weights & Biases.

DVC: the answer when data is the problem

DVC solves a different problem. It is a Git extension that versions datasets, models and pipelines alongside code, so you can reproduce a result from three months ago with the exact data it used. There is also a free VS Code extension. It does not give you a shared dashboard, and that is fine: pair it with MLflow or any tracker above. The ownership change means you should pin versions and watch release cadence for a few quarters, but the Git-first model is simple enough that leaving is easy. Compare it with the dashboard-first approach in DVC vs Weights & Biases.

Quick picks by situation

• Solo researcher or student: Weights & Biases free or academic licence, or MLflow running locally. Both cost nothing.
• Two to five engineers, no platform person: ClearML Community or Pro, or Weights & Biases Pro. Choose ClearML if you want pipelines and autoscaling included; choose W&B if the dashboards are what you will actually open.
• Regulated or air-gapped environment: self-hosted MLflow or open-source ClearML. Keep data inside your own network.
• LLM and agent products: MLflow tracing or Comet Opik, both with open-source options.
• Reproducibility of data, not just metrics: add DVC to whatever you chose.

How to avoid the next Neptune

Three habits protect you whatever you pick.

1. Log through one wrapper. A 30-line module that calls log_metric, log_param and log_artifact means swapping vendors touches one file.
2. Export on a schedule. Dump run metadata and artifacts to object storage monthly. It is dull and it is the only insurance that works.
3. Prefer open formats. MLflow-compatible output is a safe lingua franca, which is why Neptune pointed customers there when it closed.

If you are also deciding what to train, read Fine-Tuning vs. RAG and Open-Source vs. Proprietary AI Models first, because that choice decides how much tracking you need. For retrieval projects, our top vector databases guide and search APIs for agents cover the data side, and The Best AI Tools for 2026 maps the wider stack. Model hubs such as Hugging Face integrate with most of these trackers.

What is missing from this list

Several well-known options are not yet listed on ProductListo, so we have not reviewed them: TensorBoard, Aim, Kubeflow, Databricks managed MLflow, Amazon SageMaker Experiments, Google Vertex AI Experiments and Azure Machine Learning. If you are already inside one of those clouds, its built-in tracker deserves a look before you add another vendor.

Make an ML tool? Get listed

If you build experiment tracking, MLOps or data versioning software, submit your tool for review, or ask about guest posts and sponsored placements on our write for us page. We label sponsored content clearly and never sell reviews.

Sources and further reading

• OpenAI to acquire AI training tracker Neptune (InfoWorld)
• lakeFS acquires DVC (lakeFS blog)
• CoreWeave completes acquisition of Weights & Biases (CoreWeave)
• MLflow official site
• MLOps (Wikipedia)
• Data Version Control (Wikipedia)