Pick the technology stack you will still be glad of in five years.
Your stack decides who you can hire, how quickly you ship and how well AI coding tools can help your team. We help you choose deliberately, based on your product and people rather than whatever was trending when the first developer arrived.
The best tech stack is usually the boring one your team can hire for.
Stack debates online focus on performance benchmarks and developer preferences. In a growing business, the decision is driven by far more practical things: whether you can find engineers who know it, whether the libraries you need are mature, and whether the platform will still be supported when the product is five years old.
AI has added a new consideration. Coding agents and assistants are noticeably more effective in popular, well-documented languages and frameworks, because that is where their training data is richest. A mainstream stack now gives you access to a bigger hiring pool and to more capable AI assistance at the same time.
There are good reasons to choose something specialised. They should be reasons you can write down, not a preference someone could not explain.
How common stack choices fit different products
These are starting points, not rules. The right answer depends on your team, integrations and constraints, which is what a proper stack review works through.
| Typical sensible default | When to consider alternatives | AI tooling support | |
|---|---|---|---|
| Web application or SaaS | TypeScript with a mainstream framework, PostgreSQL | Heavy data processing or an existing team in another language | Strong |
| Mobile app | Cross-platform framework for most business apps | Demanding graphics, device hardware or platform-specific features | Strong for cross-platform, good for native |
| AI and data-heavy product | Python services for AI, with a TypeScript or similar web layer | Very high throughput needs a compiled language for hot paths | Strong, richest AI library ecosystem |
| Internal tools | Low-code or a simple web stack your team already knows | Complex permissions, workflows or integrations | Strong for mainstream web stacks |
| Enterprise integration | Whatever your existing IT estate supports well | Legacy platforms nearing end of support | Varies with platform age |
How to choose a tech stack: what we weigh up
Hiring market where you operate
How easy and costly it is to hire or contract engineers for this stack in your region and time zone.
Fit with the product's hardest problem
Real-time collaboration, heavy computation, offline mobile use or AI pipelines each push the choice in a particular direction.
How well AI coding agents work with it
Mainstream, strongly typed, well-documented stacks give AI tools better context and make generated code easier to check.
Ecosystem maturity
Libraries for payments, authentication, integrations and observability that are maintained and widely used.
Hosting and running cost
Some choices tie you to particular platforms or pricing models that become expensive at scale.
Existing skills and systems
What your current team and partners know, and what the new product must integrate with.
Long-term support and exit options
Maturity of the project or vendor, licence terms, and how hard it would be to move away later.
What an AI-ready technology stack looks like in practice
Planning for AI does not mean choosing exotic tools. It means making a few deliberate decisions early that keep options open.
Typed, conventional code
Type systems and consistent project structure help AI agents produce correct changes and help reviewers catch the incorrect ones.
A database that handles vectors
Many mainstream databases now support vector search, which often avoids adding a separate system for early AI features.
Model-agnostic AI layer
A thin internal interface to language models so you can switch providers or mix models as prices and capabilities change.
Automated tests and CI from day one
When AI writes a growing share of the code, fast automated checks are what keep quality under control.
Tech stack consulting that ends in a decision
A stack selection engagement usually fits inside a short Advisory Sprint. We learn about the product, the roadmap, your current team and constraints, and then compare two or three realistic options against the criteria that matter to you. The output is a written recommendation with the reasoning, trade-offs, rough hosting costs and what it means for hiring.
We are hands-on, so recommendations come from building and running software on these stacks, including with AI coding agents working in parallel. Where it is useful, we prototype the riskiest part of the product on a candidate stack before you commit.
Our advice is vendor-neutral across frameworks, clouds and tools. If your current stack is fine and the real problem lies elsewhere, such as process or architecture, we will tell you that rather than recommend a migration.
Questions we often hear
How do I choose the right tech stack for my app?
Start with the product's hardest technical problem, the skills available in your hiring market and the systems you must integrate with. Favour mature, widely used technologies unless you have a specific, written reason not to. Then check hosting costs and long-term support before committing.
What is the best tech stack for a startup?
For most startups building web or SaaS products, a mainstream stack such as TypeScript with a popular framework and PostgreSQL is a strong default. It is easy to hire for, has mature libraries and works well with AI coding tools. The best choice still depends on the product and the founding team's skills.
Does the tech stack affect how well AI coding tools work?
Yes. AI coding assistants and agents tend to perform best in popular, well-documented languages and frameworks, and typed code gives them clearer context. Niche or older stacks often get weaker suggestions and need more human correction, which reduces the productivity benefit.
Is it expensive to change tech stack later?
Changing the core language or framework of an established product is usually a significant project, which is why the early decision matters. Changing individual components, such as a database or hosting provider, is often more manageable if the architecture was designed with clear boundaries.
Should my stack be chosen by my first developer?
Their input matters, but a first hire naturally favours what they know best, which may not suit the product or future hiring. It is worth getting an independent view before the choice is locked in by months of code.
Related reading and tools
Weighing up your technology options?
Tell us what you are building, who is on the team and what you are considering. We will reply within 24 hours with initial thoughts and the questions that should drive the decision.
Working with companies globally · Response within 24 hours