ML experiment tracking in Python is the practice of logging parameters, metrics, code versions, and artifacts for every model training run so you can reproduce, compare, and audit results. In 2026, the three tools most teams shortlist are MLflow 3.0, Weights & Biases, and Comet. They all do the basics well (params, metrics, artifacts, a UI), but the differences in model registry, deployment, hosting model, and pricing are large enough to push the wrong choice into rewrite territory six months in. So, this guide benchmarks all three with real code, a feature matrix, and a decision framework so you pick correctly the first time.
MLflow 3.0 (released 2025) is the only fully open-source option with a built-in model registry, and now ships native GenAI tracing, deployment jobs, and prompt evaluation.
Weights & Biases wins on UI polish, hyperparameter sweeps, and team collaboration, though the free tier caps you at 100 GB and the paid plan starts around $50/user/month.
Comet is the most generous on the free tier (unlimited public projects, 100 GB storage) and uniquely strong at code/data diffing between runs.
For self-hosted, regulated, or air-gapped environments, MLflow is the default. Both W&B and Comet self-hosted require paid enterprise contracts.
For Hugging Face / LLM workflows, W&B Weave and Comet Opik both ship purpose-built LLM tracing; MLflow 3.0's mlflow.genai module closes the gap.
Switching tools later is painful: model registry data and run history rarely export cleanly, so test the deployment story before logging your first metric.
What is ML experiment tracking and why does it matter?
An ML experiment tracker is a service (local or hosted) that records, for every model training run, the inputs (dataset hash, code commit, hyperparameters), the outputs (metrics, charts, predictions), and the artifacts (the model file, plots, sample data). Without one, a few weeks of iteration produces a directory full of model_v3_final_actually.pkl files and a Slack thread of screenshotted accuracy numbers. With one, every run is a row you can sort, filter, and diff.
The reason this matters in 2026, more than it did in 2021, is that the model lifecycle has gotten longer. Foundation-model fine-tunes run for hours and cost real money; regulators in the EU AI Act and similar frameworks now require an auditable lineage from dataset to deployed model. The tracker is where that lineage lives.
Honestly, this is the part I've seen teams underestimate the most. In my last project, the team skipped a tracker until "later" and ended up rebuilding the last six weeks of experiments from git logs and Slack screenshots when a customer asked why a prediction had changed. Pair a tracker with disciplined hyperparameter tuning with Optuna and Bayesian optimization and the loop tightens fast.
The three tools we benchmark here cover roughly 90% of Python ML teams. Other options exist (Neptune.ai, ClearML, Aim, DVC Studio, Sacred), but in 2026 their combined mindshare is small, and the support stories outside MLflow/W&B/Comet are noticeably thinner. We'll touch on when to consider them in the decision section.
MLflow vs Weights & Biases vs Comet: feature comparison
Before the code, here's the cheat sheet. Every cell below is the 2026 state, verified against the official docs in May 2026.
Feature
MLflow 3.0
Weights & Biases
Comet
License
Apache 2.0 (fully open)
Proprietary (client SDK MIT)
Proprietary (client SDK MIT)
Free tier (individual)
Unlimited (self-host)
100 GB storage, unlimited personal projects
100 GB storage, unlimited public projects
Hosted SaaS
Databricks-managed only
Yes (wandb.ai)
Yes (comet.com)
Self-host (free)
Yes (Docker, K8s, single binary)
No, enterprise contract only
No, enterprise contract only
Model registry
Built-in, open spec
Built-in (Artifacts + Registry)
Built-in (Model Registry)
Hyperparameter sweeps
Via Optuna/Ray Tune integration
Native Sweeps (Bayesian, grid, random, Hyperband)
Native Optimizer (Bayesian, grid, random)
LLM / GenAI tracing
mlflow.genai + tracing (3.0)
W&B Weave
Comet Opik
Deployment / serving
mlflow deployments (SageMaker, K8s, Databricks)
Via Launch (paid)
Comet MPM (monitoring), no direct serving
Paid plan entry price (2026)
Free / Databricks usage
~$50/user/month
~$39/user/month
Two things are easy to miss from the table. First, MLflow is the only one whose self-hosted edition has zero license cost; you pay only for the box it runs on. Second, W&B and Comet both bill per seat, not per run, so a team of ten on the paid plan is roughly $500 to $600 per month before storage overages.
Tracking experiments with MLflow 3.0
MLflow 3.0 (June 2025) reorganized the SDK around a unified mlflow namespace and added first-class support for GenAI, deep-learning checkpointing, and deployment jobs. The basic logging API is unchanged from 2.x, so existing code keeps working. Install and start a local tracking server:
Then log a run. The pattern is straightforward: set a tracking URI, name an experiment, open a run context, log params and metrics, log the model.
import mlflow
from sklearn.datasets import load_breast_cancer
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import cross_val_score
mlflow.set_tracking_uri("http://127.0.0.1:5000")
mlflow.set_experiment("breast-cancer-gb")
X, y = load_breast_cancer(return_X_y=True)
with mlflow.start_run(run_name="gb-baseline") as run:
params = {"n_estimators": 200, "max_depth": 3, "learning_rate": 0.05}
mlflow.log_params(params)
clf = GradientBoostingClassifier(**params, random_state=42)
scores = cross_val_score(clf, X, y, cv=5, scoring="roc_auc")
mlflow.log_metric("roc_auc_mean", scores.mean())
mlflow.log_metric("roc_auc_std", scores.std())
clf.fit(X, y)
mlflow.sklearn.log_model(clf, name="model", input_example=X[:2])
print(f"Run logged: {run.info.run_id}")
Open http://127.0.0.1:5000 and the run appears immediately, with the params, metrics, and a registered model artifact you can later promote through stages (Staging to Production). The new mlflow.autolog() call still works and now covers PyTorch Lightning 2.x, Keras 3, XGBoost 2.x, LightGBM 4.x, and Transformers 5 with no per-framework imports.
Tracking experiments with Weights & Biases
W&B is hosted-first. You sign up at wandb.ai, copy an API key, and the client SDK posts everything to their cloud. The UX bet they made (and it's aged well) is that a great browser experience for comparing runs is more valuable than infinite flexibility on the backend. For most teams, they're right.
pip install wandb scikit-learn
wandb login # paste the key from wandb.ai/authorize
import wandb
from sklearn.datasets import load_breast_cancer
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import cross_val_score
run = wandb.init(
project="breast-cancer-gb",
name="gb-baseline",
config={"n_estimators": 200, "max_depth": 3, "learning_rate": 0.05},
)
X, y = load_breast_cancer(return_X_y=True)
clf = GradientBoostingClassifier(**run.config, random_state=42)
scores = cross_val_score(clf, X, y, cv=5, scoring="roc_auc")
run.log({"roc_auc_mean": float(scores.mean()), "roc_auc_std": float(scores.std())})
clf.fit(X, y)
# Save the model as a versioned artifact
import joblib, tempfile, pathlib
artifact = wandb.Artifact("gb-model", type="model")
tmp = pathlib.Path(tempfile.mkdtemp()) / "model.joblib"
joblib.dump(clf, tmp)
artifact.add_file(str(tmp))
run.log_artifact(artifact)
run.finish()
The W&B UI's strongest features are run grouping (you can split a project by config columns with one click) and the parallel-coordinates plot for sweeps. Both are noticeably nicer than MLflow's equivalents. The cost is the hosted-only constraint: for regulated workloads or anything air-gapped, W&B is off the table unless you sign the enterprise contract. See the official W&B documentation for current quotas and SSO options.
Tracking experiments with Comet
Comet sits between the other two on most axes: hosted SaaS like W&B, but with a more generous free tier and a strong focus on code/data diffing. The SDK is similar in shape but uses a single Experiment object.
from comet_ml import Experiment
from sklearn.datasets import load_breast_cancer
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import cross_val_score
experiment = Experiment(project_name="breast-cancer-gb")
experiment.set_name("gb-baseline")
params = {"n_estimators": 200, "max_depth": 3, "learning_rate": 0.05}
experiment.log_parameters(params)
X, y = load_breast_cancer(return_X_y=True)
clf = GradientBoostingClassifier(**params, random_state=42)
scores = cross_val_score(clf, X, y, cv=5, scoring="roc_auc")
experiment.log_metric("roc_auc_mean", float(scores.mean()))
experiment.log_metric("roc_auc_std", float(scores.std()))
clf.fit(X, y)
experiment.log_model("gb-model", "model.joblib") # path or sklearn object
experiment.end()
The Comet feature I miss most when I'm using W&B is the diff view. Pick two runs and Comet shows a side-by-side of the code, dependencies, and dataset hash. That makes "which change broke ROC AUC" a 30-second question instead of a 30-minute git bisect. Comet Opik (their LLM observability layer) is also free for individual use and worth a look if your roadmap includes RAG or agentic systems.
Hyperparameter sweeps across all three
This is where the three tools diverge most. W&B Sweeps is native: write a YAML config describing the search space and method (grid, random, Bayesian, Hyperband), launch agents from any machine, and the W&B server orchestrates which trial each agent runs next.
Comet Optimizer uses a similar config-driven model, also supporting Bayesian search. MLflow deliberately does not ship a sweep engine; the recommended pattern is to drive sweeps with Optuna or Ray Tune and have each trial log to MLflow. That's more setup, but it gives you full control over the search algorithm. If you already use Optuna for the techniques covered in our cross-validation strategies in scikit-learn piece, MLflow's BYO approach is barely extra work.
Model registry and deployment
A model registry is a versioned catalog of trained models with lifecycle stages (None, Staging, Production, Archived). All three tools have one. The differences:
MLflow Model Registry ships in every install, has a stable REST API, and is what tools like SageMaker, Vertex AI, and Databricks Mosaic AI integrate against. The open spec means a model logged today is portable to a different MLflow server tomorrow.
W&B Registry (rebuilt on top of Artifacts in 2024) is excellent inside the W&B ecosystem and integrates tightly with W&B Launch for one-click deployment to K8s or SageMaker, but Launch is a paid feature.
Comet Model Registry covers the basics (versions, stages, lineage back to the experiment), but Comet's positioning is "track, don't deploy." They pair with serving stacks rather than offering one.
Once your model is registered, the next concern is what happens after it's deployed. Our walkthrough on data drift detection with Evidently, NannyML, and Alibi Detect covers the monitoring layer that lives downstream of whichever registry you pick. All three trackers can log drift reports as artifacts, which keeps the production story tied back to the original experiment.
Self-hosting, pricing, and team plans
This is where many decisions actually get made. In 2026:
MLflow self-hosted: free in perpetuity. Run the server in Docker, behind nginx with OIDC, using PostgreSQL and S3 as backends. Operations cost is real (about one engineer-week to harden it for a team), but you keep your data in your VPC.
W&B: free for individuals up to 100 GB. Team plan is around $50/user/month (billed annually) with a 1 TB storage allowance. Enterprise (self-hosted, SAML, audit logs) is a custom contract, typically with five-figure annual minimums.
Comet: free for individuals on public projects with 100 GB storage. Team plans start around $39/user/month for private projects with shared storage. Self-hosted ("Comet On-Prem") is also a custom contract.
The trap I've watched teams fall into is starting on the SaaS free tier of W&B or Comet, growing past it without noticing, and discovering the upgrade cost three quarters later when finance reviews the invoice. If you suspect you'll exceed the free quota within twelve months, model the paid cost up front. For regulated industries (healthcare, finance, defence), MLflow self-hosted is often the only option that passes legal review.
Which experiment tracker should you use?
A short decision tree that holds up well in 2026:
Pick MLflow if you need self-hosting, are deploying to SageMaker, Vertex, or Databricks, or work in a regulated environment. Also pick MLflow if you want one tool that covers tracking, registry, and deployment end-to-end without per-seat fees.
Pick Weights & Biases if hyperparameter sweeps and team-collaboration UX are top priority, your data can live in their cloud, and per-seat billing fits your budget. W&B is the default for most computer-vision and LLM research teams in 2026.
Pick Comet if you want a generous free tier for a small team, value code/data diffing between runs, and don't need an opinionated deployment story.
Pick something else (Neptune, ClearML, Aim) only if you have a specific reason: Aim for pure-OSS minimal overhead, Neptune for the cleanest comparison UI, ClearML for a tighter MLOps stack with built-in orchestration.
Whatever you choose, instrument before the first serious training run. Retrofitting tracking onto two months of completed experiments is a chore nobody enjoys, and the runs you wish you had logged are always the ones you didn't. For more on the surrounding ML lifecycle, our guide to SHAP values for explaining ML models pairs naturally with whichever tracker you adopt; every SHAP figure becomes a logged artifact you can compare across runs. The official MLflow 3.0 documentation is the best starting point if you go that direction.
Frequently Asked Questions
Is MLflow really free to use?
Yes. MLflow is Apache 2.0 licensed and the entire stack (tracking server, model registry, deployment plugins) is free to self-host. The only paid version is Databricks-managed MLflow, which charges for compute and storage on their platform.
Can I use Weights & Biases offline or in an air-gapped environment?
Only with a W&B Enterprise (self-hosted) contract. The open-source SDK can queue runs locally with WANDB_MODE=offline and sync later, but the server itself is not freely self-hostable. For air-gapped environments, MLflow is the standard choice.
Which experiment tracker is best for LLM and GenAI workflows?
In 2026 the three purpose-built LLM layers are MLflow's mlflow.genai tracing module, W&B Weave, and Comet Opik. Weave and Opik are more mature for prompt evaluation and agent traces; MLflow 3.0 closes the gap and wins if you already use MLflow for classical ML.
How does MLflow compare to DVC for experiment tracking?
DVC focuses on data and pipeline versioning with Git as the source of truth; MLflow focuses on run-level params, metrics, and model artifacts in a dedicated server. Many teams use both (DVC for dataset versioning, MLflow for run tracking) rather than choosing one.
Can I migrate runs from Weights & Biases to MLflow?
Partially. The W&B SDK exposes wandb.Api() to export run params, metrics, and artifacts, and you can replay them into MLflow with mlflow.log_run(). Sweep history, panel layouts, and registry stage transitions do not migrate, so plan for losing the UI state.
Compare GPTQ, AWQ, bitsandbytes, and GGUF for LLM quantization in Python. Real H100 benchmarks, kernel choices, and a production-ready decision tree for 2026.
A practical 2026 walkthrough of GeoPandas 1.0 for Python geospatial analysis: installing the stack, handling CRS gotchas, running spatial joins, plotting interactive maps, and scaling beyond memory with DuckDB Spatial and GeoParquet.
Compare vLLM, TGI, and SGLang for serving LLMs on your own GPUs in 2026. Throughput numbers, prefix caching, quantization tradeoffs, and a production Docker deployment with monitoring.
