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AI build decisions, compared

Five decisions that come up early in AI projects, each laid out as a decision matrix you can weight for your own situation. They state criteria, not rankings, cite vendor facts from the vendors’ own pages with the date we read them, and each one says when we are the wrong choice.

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Decision guides

Five decisions at a glance

Each card previews the three criteria its matrix weights most heavily by default. Open a comparison to set your own weights.

  • RAG vs fine-tuning

    Should the model look your facts up, or learn a behaviour from examples?

    Options weighed

    Prompting only, RAG, fine-tuning, both

    Our fit ratings on the three criteria weighted most heavily by default
    OptionFacts change oftenAnswers must cite sourcesDifferent users see different content
    Prompting onlyGood fitPartial fitPoor fit
    RAG (retrieval)Strong fitStrong fitStrong fit
    Fine-tuningPoor fitPoor fitPoor fit
    RAG + fine-tuningStrong fitStrong fitStrong fit

    What usually decides it

    Whether the gap is knowledge that changes or behaviour that prompting cannot fix

    Open the matrix: RAG vs fine-tuning

  • Build vs buy an AI chatbot

    Subscribe to a chatbot product, or have one built on your own accounts?

    Options weighed

    Helpdesk AI add-on, chatbot SaaS, custom build

    Our fit ratings on the three criteria weighted most heavily by default
    OptionLive quicklyLow upfront costQuestions are mostly standard
    Helpdesk AI add-onStrong fitStrong fitStrong fit
    Standalone chatbot SaaSStrong fitStrong fitStrong fit
    Custom build on your accountsPartial fitPartial fitGood fit

    What usually decides it

    Whether it must act in your systems and where conversations may be stored

    Open the matrix: Build vs buy an AI chatbot

  • Chatbot vs AI agent vs automation

    Which kind of system fits the task in front of you?

    Options weighed

    Chatbot, rule-based automation, workflow with AI steps, AI agent

    Our fit ratings on the three criteria weighted most heavily by default
    OptionThe work starts from messy textThe steps are the same every timeIt must act in other systems
    ChatbotGood fitPartial fitPartial fit
    Rule-based automationPoor fitStrong fitStrong fit
    Workflow with AI stepsStrong fitStrong fitStrong fit
    AI agentStrong fitPartial fitStrong fit

    What usually decides it

    Who should choose the next step: your procedure or the model

    Open the matrix: Chatbot vs AI agent vs automation

  • n8n vs Make vs Zapier vs custom code

    Which platform should carry each automation pipeline?

    Options weighed

    n8n, Make, Zapier, custom code

    Our fit ratings on the three criteria weighted most heavily by default
    OptionYour team builds and edits flowsBranching, loops and error pathsCost stays low at high volume
    n8nGood fitStrong fitStrong fit
    MakeStrong fitGood fitPartial fit
    ZapierStrong fitPartial fitPartial fit
    Custom codePoor fitStrong fitStrong fit

    What usually decides it

    The billing unit at your volume, where data may go and who maintains it

    Open the matrix: n8n vs Make vs Zapier vs custom code

  • Agency vs in-house vs freelancer

    Who should build and own your AI system?

    Options weighed

    In-house team, freelancer, AI agency, founder-led studio

    Our fit ratings on the three criteria weighted most heavily by default
    OptionLong-term ownership after launchSpeed to a first working versionBreadth of skills
    In-house teamStrong fitPartial fitGood fit
    FreelancerPartial fitStrong fitPartial fit
    AI agencyGood fitGood fitStrong fit
    Founder-led studioGood fitGood fitGood fit

    What usually decides it

    How long you will run it, the skills you lack and your tolerance for one key person

    Open the matrix: Agency vs in-house vs freelancer

Method

How we write comparisons

Every comparison follows the same rules, so you can judge them by the method as well as the conclusion.

  • Criteria, not rankings. Each matrix explains every rating in words, and you set the weights. The result is guidance for a conversation, never a verdict.
  • Vendor facts come only from the vendor’s own public pages, cited with the date we read them. We quote no vendor prices, because they change faster than pages are reviewed.
  • No claims about named agencies or competitors, and no performance claims about our own work.
  • When we are one of the options, the page says so and rates our model by the same criteria.
  • Each page ends with the situations where MiniAI Labs is the wrong choice.
  • A comparison stays out of search engines until the founder has reviewed it, and its byline says whether that has happened.

FAQ

Frequently asked questions

Are these comparisons sponsored by any of the vendors they name?

No. Nobody pays for a placement and there are no affiliate links. We do build with several of the tools named, such as n8n, Make, Zapier and the main model vendors, which is why each rating states its reason and each vendor fact links to the vendor’s own page.

Why do the decision matrices not name a single winner?

Because the right answer depends on your weights. The same criteria can point to a helpdesk add-on for one team and a custom build for another. The matrix shows where your priorities lean; the discovery questions on each page are what settle it.

How current are the vendor facts in these comparisons?

Each source shows the date we read it. We re-read the sources whenever a page is updated, and the page’s updated date changes only when its content does. If you spot a fact that has changed, tell us and we will correct it.

Are the weights we choose saved or sent anywhere?

No. The weights live only in the open browser tab while you use the matrix. Nothing is stored or sent to us, and reloading the page resets them to the defaults.

Who writes and reviews these comparisons?

Vijay K, the founder, writes them from the systems we build. Each byline shows whether his review has happened yet, and pages awaiting founder review are kept out of search results.

Can we ask for a comparison you have not written?

Yes. Send the decision you are facing through the contact page. We cannot promise to publish every request, but we can usually walk through the trade-offs with you on a scoping call.

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