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Modal vs Weights & Biases

Modal logo

Modal

AI

Cloud functions for AI and ML

From
Free
Free plan
Yes
Weights & Biases logo

Weights & Biases

Machine Learning

Developer tools for machine learning

From
Free
Free plan
Yes

The short version

  • Each has a real cost: Modal the Team plan carries a $250 monthly base fee and returns only $100 of that as free credits, so $150 is a flat charge before any compute; Weights & Biases pricing can be prohibitive for large teams without enterprise discounts
  • They diverge on capability: Modal covers Serverless GPUs, Weights & Biases covers Experiment tracking.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Modal and Weights & Biases actually diverge.

Attributes where Modal and Weights & Biases differ
AttributeModalWeights & Biases
Pricing modelusage-basedUnknown
PlatformsCloud, ApiWeb, Python SDK, REST API
CategoryAIMachine Learning
Founded20212017

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated).

What each one covers

Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.

Only in Modal

  • Serverless GPUs
  • Python functions
  • Auto-scaling
  • Fast cold starts
  • Python SDK
  • GitHub Actions
  • Cloud storage
  • Cloud support

Only in Weights & Biases

  • Experiment tracking
  • Dataset versioning
  • Model registry
  • Hyperparameter sweeps
  • Collaborative dashboards
  • PyTorch
  • TensorFlow
  • Keras

What people use each for

The jobs each tool is most often brought in to do.

Modal

  • Running serverless GPU workloads for model inference and trainingnot Weights & Biases
  • Executing Python functions on cloud compute without managing serversnot Weights & Biases

Weights & Biases

  • Machine learningnot Modal
  • Data analysisnot Modal
  • Model trainingnot Modal
  • Predictive analyticsnot Modal

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

Modal

  • The Team plan carries a $250 monthly base fee and returns only $100 of that as free credits, so $150 is a flat charge before any compute
  • Compute is billed per second across separate GPU and CPU meters, so total cost depends on execution time rather than any fixed rate
  • The Starter plan's $30 monthly free credit is the only allowance below the paid base fee
  • Enterprise volume discounts are custom and unpublished

Weights & Biases

  • Pricing can be prohibitive for large teams without enterprise discounts
  • Limited integrations compared to some competitors
  • Dashboard customization options limited on lower plans
  • Requires some setup and configuration knowledge

Pricing, plan by plan

Modal

Free
  • StarterFree
    • 3 seats
    • 100 containers
    • 10 GPU concurrency
  • Team$250/month
    • Unlimited seats
    • 5,000 containers
    • 50 GPU concurrency
  • Enterprise$null/custom
    • Custom seats, containers, and GPU concurrency

Weights & Biases

Free
  • FreeFree
    • 5 model seats
    • 5 GB storage
    • 1 GB/month Weave ingestion
  • Pro$60/month
    • 10 seats
    • 100 GB storage
    • Private projects
  • Teams$179/month
    • Team collaboration
    • Advanced analytics
    • Dedicated support

Which should you pick?

Choose Modal if

  • You need serverless gpus.
  • You want to start without paying.
  • You work on Cloud, Api.
  • You also want python functions.

Choose Weights & Biases if

  • You need experiment tracking.
  • You want to start without paying.
  • You work on Web, Python SDK, REST API.
  • You also want dataset versioning.

Questions people ask

Is Modal or Weights & Biases better?
Neither clearly leads. Modal starts at Free and Weights & Biases at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Modal or Weights & Biases?
Modal starts at Free and Weights & Biases at Free.
Does Modal or Weights & Biases run on more platforms?
Modal runs on Cloud, Api. Weights & Biases runs on Web, Python SDK, REST API.
Can I use Modal for free?
Both have a free tier, so you can try either at no cost before committing.
What is Modal best used for?
Modal is most often used for running serverless gpu workloads for model inference and training, executing python functions on cloud compute without managing servers. Of those, running serverless gpu workloads for model inference and training and executing python functions on cloud compute without managing servers are not what Weights & Biases is typically brought in for.
What can Modal do that Weights & Biases cannot?
Modal covers Serverless GPUs, Python functions, Auto-scaling, Fast cold starts. Weights & Biases covers Experiment tracking, Dataset versioning, Model registry, Hyperparameter sweeps.

Answered from the vendors’ own pages

Modal: How much does Modal cost?

Modal uses pay-as-you-go pricing with Team plan at 250 USD/month base. Starter includes 30 USD/month free credits; Team includes 100 USD/month free credits. Compute charges per second for CPU cores, memory, and GPU instances.

Source
Weights & Biases: Does Weights & Biases have a free plan?

Yes. The Free tier includes 5 model seats, 5 GB storage, and 1 GB/month Weave ingestion. Academic users get unlimited tracked hours, 200 GB storage, and 100 seats at no cost.

Source
Modal: Is there a free tier?

Yes, Starter plan is free plus 30 USD/month in compute credits included monthly for new users.

Source
Weights & Biases: What are the paid plans for Weights & Biases?

Pro starts at $60/month with 10 seats and 100 GB storage. Team plans start at $179/month. Enterprise pricing is custom.

Source
Modal: What are the seat limits?

Starter plan includes 3 seats; Team plan provides unlimited seats; Enterprise tier has custom seat allocations.

Source
Weights & Biases: What machine learning features does W&B provide?

Weights & Biases captures hyperparameters, metrics, and model outputs automatically. Features include experiment tracking, interactive Reports for sharing findings, Artifacts for managing datasets and models, advanced hyperparameter sweeps, and model deployment tools.

Source
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