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Machine Learning · head to head

OpenAI API vs Quadratic

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
Quadratic logo

Quadratic

Spreadsheets

Infinite-canvas spreadsheet that runs Python, SQL and formulas in the same grid

From
Free
Rated
-
Full reviewWebsite

The short version

  • Only Quadratic has a free tier, so it costs nothing to try first.
  • Each has a real cost: 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.; Quadratic aI usage is metered as a dollar allowance rather than a flat entitlement, so a team that adopts the agent enthusiastically will exhaust 20 USD per user per month quickly and the true cost per seat becomes unpredictable.
  • They diverge on capability: OpenAI API covers Text and reasoning models, Quadratic covers Multi-language cells.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

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

Attributes where OpenAI API and Quadratic differ
AttributeOpenAI APIQuadratic
Starting price$0.15/per-million-tokensFree
Pricing modelusage-basedPer user per month
Free tierNoYes
PlatformsApiWeb, macOS, Windows, Linux
CategoryMachine LearningSpreadsheets
Founded2015Unknown

Identical on both: 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 OpenAI API

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

Only in Quadratic

  • Multi-language cells
  • Infinite canvas
  • Direct database connections
  • AI agent in the sheet
  • WebAssembly engine
  • Python package support

What people use each for

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

OpenAI API

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

Quadratic

  • An analyst who wants a SQL result, a Python transformation and a set of manual assumptions visible in one file instead of three toolsnot OpenAI API
  • Ad hoc modelling where part of the logic is genuinely code and part is a judgement call typed into a cellnot OpenAI API
  • Sharing a reproducible analysis with a colleague who will only ever open a spreadsheet, not a notebooknot OpenAI API
  • Prototyping a data pull against a warehouse before committing it to a scheduled pipelinenot OpenAI API

Where each one falls short

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

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.

Quadratic

  • AI usage is metered as a dollar allowance rather than a flat entitlement, so a team that adopts the agent enthusiastically will exhaust 20 USD per user per month quickly and the true cost per seat becomes unpredictable.
  • Self-hosting is available only on the Enterprise plan, so an organisation that cannot send data to the vendor cloud has to enter a sales negotiation rather than run a container.
  • Excel and Google Sheets compatibility is partial, so files with heavy conditional formatting, pivot tables or macros do not survive a round trip and have to be rebuilt.
  • The product is young and the connector list is short, covering the main relational databases and two warehouses, so anything else has to be reached through Python code you write and maintain yourself.
  • There is no real mobile editing experience, which matters more than it sounds for a spreadsheet because approvals and quick checks routinely happen on a phone.

Pricing, plan by plan

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

Quadratic

Free
  • PersonalFree
    • Limited AI usage
    • Limited files and connections
    • Limited sharing
  • Pro$18/month
    • Billed annually
    • 20 USD of AI credits per month
    • Unlimited files
  • Business$36/month
    • Billed annually
    • 40 USD of AI credits per month
    • Advanced permissions
  • Enterprise$undefined/year
    • Quoted
    • Self-hosting option
    • Custom AI usage

Which should you pick?

Choose OpenAI API if

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

Choose Quadratic if

  • You need multi-language cells.
  • You want to start without paying.
  • You work on Web, macOS, Windows, Linux.
  • You also want infinite canvas.
Quadratic review

Questions people ask

Is OpenAI API or Quadratic better?
Neither clearly leads. OpenAI API starts at $0.15/per-million-tokens and Quadratic at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, OpenAI API or Quadratic?
Quadratic has a free tier; the other does not. Paid plans start at $0.15/per-million-tokens for OpenAI API and Free for Quadratic.
Does OpenAI API or Quadratic run on more platforms?
OpenAI API runs on Api. Quadratic runs on Web, macOS, Windows, Linux.
Can I use Quadratic for free?
Yes. Quadratic has a free tier, so you can try it without paying. OpenAI API starts at $0.15/per-million-tokens.
What is OpenAI API best used for?
OpenAI API is most often used for adding summarisation, drafting or classification to an existing product where building a model would take longer than the product's whole roadmap, retrieval-augmented question answering over internal documents, using the embedding and generation models together, extracting structured records from unstructured text, where schema-constrained output removes most of the parsing problem, prototyping a language feature quickly to find out whether it is worth the cost of a self-hosted alternative later. Of those, adding summarisation, drafting or classification to an existing product where building a model would take longer than the product's whole roadmap and retrieval-augmented question answering over internal documents, using the embedding and generation models together are not what Quadratic is typically brought in for.
What can OpenAI API do that Quadratic cannot?
OpenAI API covers Text and reasoning models, Embeddings, Speech and audio, Image generation. Quadratic covers Multi-language cells, Infinite canvas, Direct database connections, AI agent in the sheet.

Answered from the vendors’ own pages

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.

Quadratic: Do I need to know Python to use it?

No, formulas work on their own, but the reason to choose Quadratic over Google Sheets is the code cells, so a team with no Python will not get the value.

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.

Quadratic: What happens when the AI credits run out?

Prompting stops until the next monthly reset or until you move up a plan; the spreadsheet itself keeps working normally.

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.

Quadratic: Can it replace a BI tool?

Not for scheduled distribution or governed metrics. It is for exploratory work by one person or a small team, not for dashboards a hundred people read.

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.

Quadratic: Is my database query sent to the vendor?

Connections run through the vendor service on all plans except Enterprise self-hosting, which is the option to take if that is unacceptable.

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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