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Spreadsheets · head to head

Quadratic vs Semantic Kernel

Quadratic logo

Quadratic

Spreadsheets

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

From
Free
Rated
-
Full reviewWebsite
Semantic Kernel logo

Semantic Kernel

Machine Learning

Model-agnostic SDK for AI orchestration

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: 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.; Semantic Kernel steep learning curve for advanced features
  • They diverge on capability: Quadratic covers Multi-language cells, Semantic Kernel covers Multi-model support.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Quadratic and Semantic Kernel actually diverge.

Attributes where Quadratic and Semantic Kernel differ
AttributeQuadraticSemantic Kernel
Pricing modelPer user per monthOpen source, no pricing
PlatformsWeb, macOS, Windows, LinuxPython, .NET, Java
CategorySpreadsheetsMachine Learning

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 Quadratic

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

Only in Semantic Kernel

  • Multi-model support
  • Agent framework
  • Multi-agent systems
  • Plugin ecosystem
  • Vector database integration
  • Multimodal support
  • Local model support
  • Enterprise observability

What people use each for

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

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 Semantic Kernel
  • Ad hoc modelling where part of the logic is genuinely code and part is a judgement call typed into a cellnot Semantic Kernel
  • Sharing a reproducible analysis with a colleague who will only ever open a spreadsheet, not a notebooknot Semantic Kernel
  • Prototyping a data pull against a warehouse before committing it to a scheduled pipelinenot Semantic Kernel

Semantic Kernel

  • Building enterprise AI applications with LLM integrationnot Quadratic
  • Creating multi-agent systems for complex workflowsnot Quadratic
  • Developing AI-powered chatbots and assistantsnot Quadratic
  • Implementing RAG systems with vector databasesnot Quadratic

Where each one falls short

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

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.

Semantic Kernel

  • Steep learning curve for advanced features
  • Documentation focuses on Azure cloud services
  • Configuration complexity for multi-model scenarios
  • Requires understanding of AI/LLM concepts

Pricing, plan by plan

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

Semantic Kernel

Free
  • Open SourceFree
    • MIT license
    • Full framework access
    • All language SDKs

Which should you pick?

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

Choose Semantic Kernel if

  • You need multi-model support.
  • You want to start without paying.
  • You work on Python, .NET, Java.
  • You also want agent framework.
Semantic Kernel review

Questions people ask

Is Quadratic or Semantic Kernel better?
Neither clearly leads. Quadratic starts at Free and Semantic Kernel at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Quadratic or Semantic Kernel?
Quadratic starts at Free and Semantic Kernel at Free.
Does Quadratic or Semantic Kernel run on more platforms?
Quadratic runs on Web, macOS, Windows, Linux. Semantic Kernel runs on Python, .NET, Java.
Can I use Quadratic for free?
Both have a free tier, so you can try either at no cost before committing.
What is Quadratic best used for?
Quadratic is most often used for an analyst who wants a sql result, a python transformation and a set of manual assumptions visible in one file instead of three tools, ad hoc modelling where part of the logic is genuinely code and part is a judgement call typed into a cell, sharing a reproducible analysis with a colleague who will only ever open a spreadsheet, not a notebook, prototyping a data pull against a warehouse before committing it to a scheduled pipeline. Of those, an analyst who wants a sql result, a python transformation and a set of manual assumptions visible in one file instead of three tools and ad hoc modelling where part of the logic is genuinely code and part is a judgement call typed into a cell are not what Semantic Kernel is typically brought in for.
What can Quadratic do that Semantic Kernel cannot?
Quadratic covers Multi-language cells, Infinite canvas, Direct database connections, AI agent in the sheet. Semantic Kernel covers Multi-model support, Agent framework, Multi-agent systems, Plugin ecosystem.

Answered from the vendors’ own pages

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.

Semantic Kernel: What LLM providers does Semantic Kernel support?

Semantic Kernel supports OpenAI, Azure OpenAI, Hugging Face, Nvidia, and other providers through extensible model implementations.

Source
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.

Semantic Kernel: Can I run Semantic Kernel locally?

Yes. Semantic Kernel supports local models through Ollama, LMStudio, and ONNX for complete data control and offline operation.

Source
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.

Semantic Kernel: Is Semantic Kernel free?

Yes. Semantic Kernel is MIT-licensed open source and completely free. You only pay for external LLM APIs you use.

Source
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.

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