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AI · head to head

Modal vs OpenAI API

Modal logo

Modal

AI

Cloud functions for AI and ML

From
Free
Rated
-
Full reviewWebsite
OpenAI API logo

OpenAI API

Machine Learning

Hosted API for OpenAI's language, embedding, image and audio models, billed per token

From
$0.15/per-million-tokens
Rated
-
Full reviewWebsite

The short version

  • Only Modal has a free tier, so it costs nothing to try first.
  • 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; OpenAI API cost scales with tokens rather than with seats, so a successful feature's bill grows with its adoption, and an interface that lets users paste long documents has no natural ceiling on spend unless you build one yourself.
  • They diverge on capability: Modal covers Serverless GPUs, OpenAI API covers Text and reasoning models.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Modal and OpenAI API actually diverge.

Attributes where Modal and OpenAI API differ
AttributeModalOpenAI API
Starting priceFree$0.15/per-million-tokens
Free tierYesNo
PlatformsCloud, ApiApi
CategoryAIMachine Learning
Founded20212015

Identical on both: pricing model (usage-based), 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 OpenAI API

  • Text and reasoning models
  • Embeddings
  • Speech and audio
  • Image generation
  • Function calling
  • Structured outputs
  • Batch processing
  • Prompt caching

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 OpenAI API
  • Executing Python functions on cloud compute without managing serversnot OpenAI API

OpenAI API

  • Adding summarisation, drafting or classification to an existing product where building a model would take longer than the product's whole roadmapnot Modal
  • Retrieval-augmented question answering over internal documents, using the embedding and generation models togethernot Modal
  • Extracting structured records from unstructured text, where schema-constrained output removes most of the parsing problemnot Modal
  • Prototyping a language feature quickly to find out whether it is worth the cost of a self-hosted alternative laternot 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

OpenAI API

  • Cost scales with tokens rather than with seats, so a successful feature's bill grows with its adoption, and an interface that lets users paste long documents has no natural ceiling on spend unless you build one yourself.
  • Models are deprecated on the vendor's timetable, and a fine-tuned model built on a retired base goes with it, so the tuning work and the data curation behind it must be redone rather than migrated.
  • Behaviour shifts between model versions in ways no test catches unless you wrote one, so prompts tuned over months against a particular snapshot can regress quietly on migration, which makes an evaluation suite a prerequisite rather than an improvement.
  • It cannot run inside your own network, so data residency requirements, air-gapped environments and contracts forbidding third-party processing rule it out regardless of the provider's own security posture.
  • You inherit its availability and its rate limits, so a provider incident is an outage in your product and a traffic spike can be throttled at precisely the moment the feature is proving itself.

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

OpenAI API

$0.15/per-million-tokens
  • GPT-4o mini$0.15/per-million-input-tokens
    • Fast
    • Affordable
  • GPT-4o$5/per-million-input-tokens
    • Multimodal
    • 128K context

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.
Modal review

Choose OpenAI API if

  • You need text and reasoning models.
  • You work on Api.
  • You also want embeddings.
OpenAI API review

Questions people ask

Is Modal or OpenAI API better?
Neither clearly leads. Modal starts at Free and OpenAI API at $0.15/per-million-tokens, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Modal or OpenAI API?
Modal has a free tier; the other does not. Paid plans start at Free for Modal and $0.15/per-million-tokens for OpenAI API.
Does Modal or OpenAI API run on more platforms?
Modal runs on Cloud, Api. OpenAI API runs on Api.
Can I use Modal for free?
Yes. Modal has a free tier, so you can try it without paying. OpenAI API starts at $0.15/per-million-tokens.
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 OpenAI API is typically brought in for.
What can Modal do that OpenAI API cannot?
Modal covers Serverless GPUs, Python functions, Auto-scaling, Fast cold starts. OpenAI API covers Text and reasoning models, Embeddings, Speech and audio, Image generation.

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
OpenAI API: Is my data used to train the models?

API inputs and outputs are not used for training by default, which differs from the consumer product. Retention periods and enterprise terms change, so read the current data usage policy rather than trusting a summary.

Modal: Is there a free tier?

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

Source
OpenAI API: Can I run these models on my own hardware?

No. The weights are not distributed. If self-hosting is a requirement, you are looking at open-weight models instead, with the operational and quality trade-offs that implies.

Modal: What are the seat limits?

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

Source
OpenAI API: How is it priced?

Per token, with input and output priced differently and each model priced differently. Batch processing and cached input prefixes reduce it. The practical consequence is that your bill is a function of prompt design, not just of request count.

OpenAI API: What is the difference from Azure OpenAI Service?

The same model family delivered by Microsoft under an Azure contract, with Azure identity, networking and regional controls, and a different release cadence for new models. Enterprises with an Azure agreement often choose it for procurement and data residency reasons rather than technical ones.

OpenAI API: How do I keep the cost under control?

Cap input length, cache repeated prefixes, route easy requests to smaller models, use the batch path where latency does not matter, and set per-user limits before launch rather than after the first surprising invoice.

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