MLOps Tools · Honest Comparison

MLflow vs Weights & Biases: what nobody tells you

Both tools work. The difference is what breaks when your team scales. Here’s what six months of using both actually taught me.

The tools

Open source — MLflow

Built by Databricks. Tracks experiments, packages code, serves models. Does everything adequately.

• Free — you pay only for your own infra
• Self-hostable — full data control
• Setup friction is real — the tracking server config will burn a morning
• UI is functional, not fast — filtering 500 runs feels like work

SaaS — Weights & Biases

Purpose-built for ML teams. Logging, sweeps, reports, collaboration — all polished.

• First run logged in under 5 minutes — no server to configure
• Free tier caps hit faster than you expect — plan for it
• Sweep UI is exceptional — hyperparameter search becomes visual
• Pricing stings at scale — $50/user/month adds up

The real difference in code

Both tools log your training runs. The gap is what you get for the same four lines of logging code:

MLflow

import mlflow

mlflow.start_run()
mlflow.log_metric("loss", loss)
mlflow.log_param("lr", 0.001)
mlflow.end_run()

# result: a row in a table

Weights & Biases

import wandb

wandb.init(project="my-model")
wandb.log({"loss": loss})
# lr, system metrics,
# GPU usage auto-logged

# result: interactive dashboard

W&B auto-captures system metrics (GPU, CPU, memory), generates a shareable link, and renders loss curves live. MLflow gives you a number in a table. Neither is wrong — they reflect different philosophies.

Head-to-head

Dimension	MLflow	W&B
Time to first logged run	30–60 min (server setup)	~5 min
Cost at 5 users	$0 + infra	$250/month
Filtering 500+ runs	Slow, limited	Fast, visual
Hyperparameter sweeps	Manual setup	Built-in, visual UI
Team collaboration	Not built-in	Reports, sharing, comments
Data stays on-prem	Yes (self-hosted)	Enterprise only
Databricks integration	Native	Available, not native
Model registry	Mature, battle-tested	Good but newer

Pricing — the honest version

MLflow — $0 always

Open source. Infrastructure costs vary: a small EC2 instance for a team of 5 runs ~$30–80/month. You own the ops burden.

W&B — $50 per user / month (Team plan)

Free tier exists but caps on storage and usage hit faster than most teams expect. Enterprise pricing is custom and opaque.

The real pricing question isn’t MLflow vs W&B — it’s whether $250/month for 5 ML engineers saves more than 5 hours/month of time. If your team is spending hours per week asking “which run had those numbers again?” — W&B usually wins on ROI.

Who should pick what

Choose MLflow when…

• You’re already on Databricks — it’s native
• Compliance requires data on-prem
• Budget is a hard constraint
• You have a DevOps person who can manage infra
• You’re a solo researcher with no collaboration needs

Choose W&B when…

• Your team is 3+ people sharing results regularly
• You run hyperparameter sweeps often
• Onboarding speed matters more than cost
• Stakeholders need polished reports
• You don’t want to manage an MLOps server

One honest test: Count how many times your team said “which run was that?” last week. If it’s more than twice — that’s W&B’s ROI, right there.

Bottom line

The verdict

MLflow is the right default if you want control, zero cost, and don’t mind the setup. W&B is the right call if your team loses time finding and sharing results. The tool that costs more is often the one that’s free — because someone’s paying with hours instead of dollars.

Try both in one afternoon. Run a 5-minute experiment in each. The right answer will be obvious to your team.