Avery vs BuildShip vs MakerKit: which AI builder ships production code in 2026?

Three AI app builders. Three different bets on the future of dev tools. The marketing for all three sounds similar. The reality of using each is very different.

I built the same product on all three to find out where they actually break. This post is the honest comparison. Not a feature checklist. The decision-relevant differences, written for the founder who needs to pick one this month.

The product I built: a SaaS for small businesses to manage their customer feedback. Forms, response tracking, simple analytics, billing via Stripe. Roughly the complexity of a real first product, not a TODO app.

The three approaches

BuildShip is a visual workflow builder. You drag nodes that represent API calls, database operations, AI prompts, and conditionals. The platform generates the underlying logic. Strength: speed for simple workflows. Weakness: you're locked into their visual layer.

MakerKit is a SaaS boilerplate. You start with a fully scaffolded Next.js application that includes auth, billing, multi-tenant database, admin UI, transactional email. From there, you write your own code on top. Strength: best-in-class starting point. Weakness: it's a boilerplate, not a builder. After day one, you're carrying the build yourself.

Avery uses the Change Request pattern. You scope features as CRs (with clear acceptance criteria), Avery writes the code and submits a PR, you review and merge. Strength: scales with project complexity. Weakness: more setup than dragging nodes.

How each builds the same feature

To make the comparison concrete, here's how each handles the same feature: "Add an admin dashboard showing all customer responses, filterable by date and form, with CSV export."

BuildShip: I drag a node for "list responses," configure the database query node, add filter inputs, drop a CSV export node from their marketplace. About 20 minutes for the basic version. The visual workflow runs. To customize the CSV format beyond what the node supports, I'd need to write custom JavaScript inside a BuildShip node, which is doable but breaks the visual abstraction.

MakerKit: I open the codebase. I find the admin folder. I write a new page using their existing components, query the responses table they've already scaffolded, add filter logic, write CSV generation. About 90 minutes including styling. The code is in my repo, I own it, I can customize anything.

Avery: I write a CR: "Build /admin/responses listing all customer responses with filters by date range and form_id. Allow CSV export of filtered results. Use the existing admin layout and response model." Avery opens a PR within 15 minutes with the full implementation. I review for 10 minutes, request one styling change, merge. Total elapsed: about 30 minutes of my time, but I wasn't writing the code.

The patterns reveal the strengths.

Production readiness

The clearest separator.

BuildShip: Prototypes ship fast. Production is harder. The visual abstraction makes debugging hard when something breaks in production. The workflow runs on BuildShip's infrastructure, so you don't have direct database access or full logging. For internal tools and prototypes, it works. For customer-facing SaaS, you're hoping the platform doesn't have an outage at 3 AM.

MakerKit: Production-ready from day one because it's your code on your infrastructure. You deploy to Vercel or wherever you want. You own the database. Full debugging access. The downside is you have to keep building the rest of the product yourself, in your time, with your engineering capacity.

Avery: Production-ready from day one and the build keeps going. Your code in your GitHub. Your infrastructure. Avery's CRs produce real code you can debug, modify, extend. The difference vs. MakerKit is that the build doesn't stop after the initial scaffold.

Code ownership

This is where most founders don't think clearly enough.

With BuildShip, the workflows are encoded in their platform. If you outgrow BuildShip or BuildShip changes pricing dramatically, your "code" doesn't exist in a portable form. You're not entirely locked in (you can export, manually rewrite, migrate), but the migration is non-trivial.

With MakerKit, the code is yours. It's in your repo. If MakerKit disappeared tomorrow, your product would keep running because you've been writing the code yourself.

With Avery, the code is yours. The CRs produce PRs to your GitHub. Avery's role is the AI that writes the code; the code itself is in your hands. If Avery disappeared tomorrow, your product keeps running and your team (or a contractor, or you) can continue from where the CRs left off.

For an early-stage SaaS, code ownership matters more than speed. Lock-in costs compound. The right approach is to use a builder that produces portable code from day one.

Scaling path

What happens at year two?

BuildShip: You've hit the platform's limits. Some features are hard to express in their visual model. Your team has been working around it by writing custom JavaScript nodes that break the visual abstraction. You're considering migrating off. The migration is a project.

MakerKit: You're scaling like any other Next.js SaaS. Hiring engineers, growing the codebase, managing the complexity. The boilerplate got you started; from here it's the same trajectory as any custom-built SaaS.

Avery: You're shipping more CRs. The AI is doing more of the build as your scope grows. You hire when you need humans for things AI can't do (customer interviews, specific architecture decisions, customer success). The team stays smaller because the leverage is higher.

For solo founders or small teams, Avery's scaling path is the most attractive. You don't have to build out an engineering org as fast.

Hiring implications

If you're using BuildShip, your hiring is constrained. Engineers don't want to work in visual workflow builders. The hiring pool shrinks. Or your engineers spend their time writing custom JavaScript inside BuildShip nodes, which is the worst of both worlds.

If you're using MakerKit, you hire Next.js engineers. Standard. Plenty of supply. The boilerplate is a starting point that any senior engineer can extend.

If you're using Avery, you hire engineers who are good at writing specs and reviewing PRs. The hiring profile shifts. Less "can you write production code from a vague requirement" and more "can you decompose a feature into shippable CRs and review AI-generated code critically." This is the new senior engineer skill.

Total cost over 12 months

A 12-month rough estimate for a solo founder shipping a real SaaS:

BuildShip: Platform fees plus per-execution costs. Scales with traffic, which is unpredictable. Estimate: a few hundred per month average, more at scale. Plus your time on workarounds.

MakerKit: One-time license fee plus standard SaaS infrastructure costs (Vercel, database, email). Estimate: hundreds to low thousands depending on scale. Your time is the main cost.

Avery: Subscription plus standard SaaS infrastructure. Estimate: a few hundred per month with significant CR throughput. Your time is the lowest of the three because the AI does the build.

The honest answer is that cost is not the deciding factor for most founders. Time is. The question is: how much of your time goes to building vs. selling? BuildShip and Avery both reduce build time. MakerKit accelerates the first month but doesn't change the trajectory after.

Who should pick what

Pick BuildShip if:

You're prototyping for a hackathon, client demo, or internal tool that won't see real production traffic. The visual workflow gets you to "working" fastest.

You're not technical and never want to look at code. BuildShip's visual abstraction is more accessible than reviewing PRs.

Pick MakerKit if:

You have a team or are about to build one. You want a strong starting point but you'll be writing your own code afterward.

You're an experienced engineer who wants control over every line and is willing to build the product yourself.

You're philosophically opposed to AI-generated code (which is a valid position).

Pick Avery if:

You're a solo founder or small team who wants to ship and keep shipping without hiring.

You want to own the code from day one but don't have the engineering capacity to write all of it yourself.

You're comfortable reviewing PRs and writing specs. The CR pattern fits your working style.

You want the AI to do the build while you focus on product, customers, and growth.

The honest weakness of each

Every tool has a failure mode. The fair comparison includes them.

BuildShip's failure mode: Lock-in. The visual workflows are not portable. If the platform changes pricing or capabilities, migration is painful.

MakerKit's failure mode: You're still building. The boilerplate accelerates the first month, but after that you're writing every line yourself. For solo founders, this is the time-killer that prevents shipping.

Avery's failure mode: You have to learn the CR pattern. Writing good CRs is a skill. Bad CRs produce bad PRs. There's a learning curve in the first week.

My honest recommendation

For a solo founder shipping their first SaaS: start with MakerKit if you're an experienced engineer, Avery if you're not (or if you want the AI leverage even though you can code).

For a non-technical founder: BuildShip for prototyping, but plan to either migrate or hire if it becomes a real business.

For a team of three or more: MakerKit boilerplate plus Avery for ongoing CRs is the best combination. Start with the strong scaffold, then let AI handle the build velocity.

For an existing product migrating from no-code: Avery, because it produces code you can iterate on with humans later.

The pattern across all three

The deeper truth: the boundary between "AI tooling" and "real software development" is blurring. All three of these tools are different points on a spectrum. BuildShip is closest to no-code. MakerKit is closest to traditional engineering. Avery is in the middle, leveraging AI for build velocity while keeping production-grade code.

In 2026, "should I use AI to build my SaaS" is no longer the question. The question is which AI tool fits your role, your team size, and your scaling plans.