I’ve lost count of how many times I’ve seen brilliant ML engineers frantically searching through Git commits, trying to figure out which hyperparameters produced those amazing results from last Tuesday. One guy on my team literally kept an Excel spreadsheet with 300+ rows of experiment configs. It was chaos. Then we tried every experiment tracking tool on the market, and let me tell you — some are absolute game-changers while others are just expensive noise.
Experiment tracking tools are supposed to solve one problem: keeping track of what you tried, what worked, and why. But the market is flooded with options, each claiming to be the best. Some are free and powerful, others cost a fortune and deliver mediocre features. After burning through three years and countless corporate budgets testing these tools, I know which ones are worth your time.
Let me save you the trial-and-error and show you what actually works.
Before we dive into specific tools, let’s establish what actually matters. Not marketing fluff — real features you’ll use daily.
Core requirements:
Nice-to-haves:
Now let’s see which tools deliver on these promises.
I’ll be honest — Weights & Biases is what everyone uses, and for good reason. It’s polished, powerful, and the free tier is genuinely generous.
The free tier limits you to personal/academic projects and has some storage caps, but for most individual users, you’ll never hit them.
Starting at $50/month per user, the paid tier adds:
Setup is ridiculously simple:
python
import wandb# Initialize
wandb.init(project="my-project", config={
"learning_rate": 0.001,
"batch_size": 64,
"epochs": 10
})
# Log metrics during training
for epoch in range(epochs):
loss = train_epoch()
wandb.log({"loss": loss, "epoch": epoch})
# Save model
wandb.save("model.h5")
That’s it. Your experiments now appear in a gorgeous web dashboard with interactive plots, system metrics, and full reproducibility.
Pros:
Cons:
Best for: Teams that value polish and collaboration, companies willing to pay for quality, researchers who want public portfolios
My take: W&B is the gold standard. If you can afford it and don’t mind cloud hosting, this is what you should use. The free tier is perfect for students and hobbyists.
MLflow is what you use when you want complete control and zero vendor lock-in. It’s open source, self-hosted, and totally free. Forever.
Everything. MLflow is 100% open source:
python
import mlflow# Start tracking
mlflow.set_experiment("my-experiment")
with mlflow.start_run():
# Log parameters
mlflow.log_param("learning_rate", 0.001)
mlflow.log_param("batch_size", 64)
# Train and log metrics
for epoch in range(epochs):
loss = train_epoch()
mlflow.log_metric("loss", loss, step=epoch)
# Log model
mlflow.sklearn.log_model(model, "model")
MLflow runs locally or on your own servers. The UI is functional but basic — think “engineering tool” not “product showcase.”
Pros:
Cons:
Best for: Companies that need full control, teams with DevOps resources, projects with strict data privacy requirements, anyone allergic to subscription fees
My take: MLflow is the sensible choice for production ML systems. It’s not flashy, but it’s rock-solid and you’ll never get a surprise bill. Just budget time for infrastructure management.
TensorBoard came with TensorFlow and it’s still going strong. It’s free, it’s simple, and if you’re already using TensorFlow or PyTorch, it’s already installed.
python
from torch.utils.tensorboard import SummaryWriterwriter = SummaryWriter('runs/experiment_1')for epoch in range(epochs):
loss = train_epoch()
writer.add_scalar('Loss/train', loss, epoch)
writer.add_scalar('Accuracy/train', acc, epoch)
writer.close()
# Launch TensorBoard
# tensorboard --logdir=runs
TensorBoard runs locally in your browser. It’s simple, fast, and does one thing well.
Pros:
Cons:
Best for: Solo developers, quick prototyping, people who just want to watch loss curves, anyone doing serious TensorFlow/PyTorch work
My take: TensorBoard is perfect for what it is — a training monitor. Don’t expect it to manage hundreds of experiments or facilitate team collaboration. Use it for development, graduate to something else for production.
Neptune is like W&B’s more organized, slightly less flashy cousin. It focuses on experiment organization and metadata management.
The free tier is generous for individuals but restrictive for teams.
Starting at $59/month per user:
python
import neptune# Initialize
run = neptune.init_run(
project="workspace/project",
api_token="YOUR_TOKEN"
)
# Log everything
run["parameters"] = {"lr": 0.001, "batch_size": 64}
for epoch in range(epochs):
run["train/loss"].append(loss)
run["train/accuracy"].append(acc)
# Stop tracking
run.stop()
Neptune’s UI emphasizes organization — tags, filtering, and comparison tools are first-class citizens.
Pros:
Cons:
Best for: Teams that prioritize organization over flash, companies wanting W&B features at lower cost, people who love metadata :)
My take: Neptune is solid but stuck in the middle. It’s better than MLflow’s UI but not as good as W&B’s. It costs less than W&B but isn’t free like MLflow. It’s a perfectly fine tool that I never feel excited to recommend.
Comet has been around forever and keeps adding features. Sometimes too many features, IMO.
The free tier is usable but storage limits are annoying.
Starting at $20/month per user:
python
from comet_ml import Experimentexperiment = Experiment(
api_key="YOUR_KEY",
project_name="my-project"
)
experiment.log_parameters({"lr": 0.001, "batch_size": 64})for epoch in range(epochs):
experiment.log_metric("loss", loss, step=epoch)
experiment.end()
Comet tries to do everything — experiment tracking, model monitoring, dataset versioning, AutoML, you name it.
Pros:
Cons:
Best for: Teams that want an all-in-one platform, companies that need production monitoring alongside experiment tracking, people who like feature-rich tools
My take: Comet is like that Swiss Army knife with 50 attachments — theoretically useful, practically awkward. The core tracking works fine, but I find myself fighting the UI instead of enjoying it.
Sacred is different — it’s not a platform, it’s a Python library. You run it locally and store data wherever you want (MongoDB, files, whatever).
Everything, because it’s just a library:
No cloud, no subscription, no vendor. Just code.
python
from sacred import Experimentex = Experiment('my_experiment')@ex.config
def my_config():
learning_rate = 0.001
batch_size = 64
@ex.automain
def train(learning_rate, batch_size, _run):
for epoch in range(epochs):
loss = train_epoch()
_run.log_scalar("loss", loss, epoch)
Sacred captures everything automatically. Pair it with Omniboard for a web UI or query MongoDB directly.
Pros:
Cons:
Best for: Researchers who value reproducibility above all, teams with strict data privacy requirements, anyone running experiments on air-gapped systems, minimalists who hate unnecessary complexity
My take: Sacred is brilliant for what it does — perfect experiment capture with zero bloat. But you’ll need additional tools for visualization and collaboration. I use it for academic projects where reproducibility is critical.
DVC (Data Version Control) isn’t primarily an experiment tracker — it’s Git for data. But DVC Studio adds experiment tracking on top.
DVC itself is 100% free and open source:
The cloud UI is free for public repos, paid for private:
bash
# Initialize DVC
dvc init# Track data
dvc add data/dataset.csv
git add data/dataset.csv.dvc
# Track experiments
dvc exp run
dvc exp show
# Compare experiments
dvc plots diff
DVC treats experiments as Git branches — it’s Git-native experiment tracking.
Pros:
Cons:
Best for: Data scientists who love Git workflows, teams already using DVC for data versioning, projects where data versioning matters as much as experiment tracking
My take: DVC is incredible for data versioning. The experiment tracking feels like an add-on (because it is). If you need both data versioning and experiment tracking, DVC is perfect. Otherwise, dedicated experiment trackers work better.
ClearML (formerly Allegro Trains) is open source with enterprise features. Think MLflow but with better UX and more features.
Everything in the open source version:
They offer hosted service and enterprise features:
Pricing isn’t public — you have to contact sales (always a red flag, FYI).
python
from clearml import Task# Initialize
task = Task.init(project_name='my_project', task_name='experiment_1')
# Get config
params = {'learning_rate': 0.001, 'batch_size': 64}
task.connect(params)
# Log metrics
for epoch in range(epochs):
task.logger.report_scalar("loss", "train", iteration=epoch, value=loss)
ClearML auto-captures a lot — imports, uncommitted changes, environment variables. Sometimes too much.
Pros:
Cons:
Best for: Teams wanting self-hosted solution with better UX than MLflow, companies needing orchestration + tracking, people who want open source but polished
My take: ClearML is impressive but tries to do too much. The experiment tracking is solid, but you’re also getting orchestration, data management, and deployment tools whether you want them or not. Great if you need all that, overkill if you just want tracking.
Let me break this down in a way that’s actually useful:
Best choice: Weights & Biases (free tier) or TensorBoard Runner-up: Sacred + Omniboard Why: W&B’s free tier is generous and the UI is fantastic. TensorBoard is already installed and works great for watching training.
Best choice: MLflow (self-hosted) or Neptune Runner-up: Weights & Biases (if budget allows) Why: MLflow is free and you control everything. Neptune is affordable and designed for teams. W&B is better but $250+/month might sting.
Best choice: Weights & Biases or MLflow Runner-up: ClearML or Neptune Why: W&B shines with team features. MLflow saves money if you have DevOps capacity. ClearML splits the difference.
Best choice: MLflow (self-hosted) or W&B Enterprise Runner-up: ClearML Enterprise Why: At scale, vendor costs explode. MLflow’s open source model wins financially. W&B Enterprise is worth it if collaboration is critical.
Best choice: MLflow or Sacred Runner-up: ClearML Why: Self-hosted, no data leaves your network, complete control. End of discussion.
Best choice: Weights & Biases (free tier) or Sacred Runner-up: TensorBoard Why: W&B free tier is perfect for papers and public portfolios. Sacred gives perfect reproducibility for rigorous research.
Different projects, different tools. Here’s my actual setup:
Personal projects: Weights & Biases free tier. The UI is too good to pass up, and I like having a public portfolio.
Client work: MLflow. Clients don’t want their data in the cloud, and I don’t want to explain subscription fees. MLflow just works.
Research papers: Sacred. Perfect reproducibility matters more than pretty dashboards when reviewers are involved.
Quick prototyping: TensorBoard. It’s already there, why complicate things?
Team projects at work: We use W&B because management approved the budget and the team loves it. Would I use MLflow if I had to pay myself? Absolutely.
Still not sure? Answer these questions:
After three years and way too much money spent on subscriptions, here’s what I know: there’s no “best” experiment tracking tool. There’s the best tool for your situation.
Weights & Biases is gorgeous and powerful — if you can afford it. MLflow is bulletproof and free — if you can manage it. Sacred is perfect for reproducibility — if you don’t need collaboration. TensorBoard works great — if your needs are simple.
The Excel spreadsheet my teammate was using? We moved him to W&B and his productivity skyrocketed. But another client with strict compliance requirements? MLflow was the only option, and it worked perfectly.
Pick based on your constraints, not hype. Every tool on this list will track your experiments. The question is which one fits your budget, your team, and your workflow. Answer that honestly, and you’ll be fine.
Now stop researching tools and go train some models. Your experiments aren’t tracking themselves (yet).
Comments
Post a Comment