How to Choose the Right Development Partner

Choosing a development partner is a significant decision. You are trusting an external team to build software that is critical to your business. A wrong choice costs not only money but months of lost time.

In this article, we share the criteria we believe matter most, honestly from the perspective of an agency that works with clients every day.

Every agency showcases their best work. But a polished portfolio does not tell the whole story. Ask follow-up questions: how long did the project take? What was the budget? How did the collaboration go after launch?

The best indicator is how an agency handles challenges. Ask about projects that were difficult. An honest answer says more than ten success stories.

Most failed software projects do not fail because of technical problems. They fail because of miscommunication. Choose a partner that communicates in clear language, not jargon.

Pay attention to how quickly and clearly they respond during the proposal process. This is a preview of how the collaboration will go. If things are already difficult during sales, the project will not get better.

A good partner gives you a realistic estimate, not the lowest price. Be suspicious of quotes that are significantly lower than the rest. Corners are being cut somewhere, and you will notice later.

Ask how the agency handles scope changes. In every project, requirements evolve. A mature agency has a clear process for this instead of treating it as a problem.

Software is never finished. After launch come updates, bug fixes, and new features. Choose a partner that remains available after delivery and offers a clear maintenance model.

At MG Software, we see ourselves as a long-term partner. We do not just build your software. We help you grow it alongside your business.

A good introductory conversation gives you a sharp picture of an agency within an hour. We ask these five questions to subcontractors ourselves, so feel free to fire them at us too. One: who will actually work on my project, and will I speak to that person during the project? Two: what does a typical week look like in terms of updates and meetings? Three: what happens when a deadline is at risk, who hears about it first? Four: who owns the source code and where is it stored? Five: what does an average year of maintenance cost after delivery?

Pay special attention to the answers to questions four and five. Source code should be yours, in a repository you can access yourself. And an agency that does not dare to name a concrete figure for maintenance probably does not have a mature process for it. As a rule of thumb, we budget 10 to 20 percent of the original build cost per year for hosting, updates, and small improvements. If you want a feel for project costs upfront, our calculator is a good starting point.

Many clients assume bigger means safer. In practice, we see the opposite for projects between €15,000 and €150,000. At a large agency, your project is often assigned to junior developers, while the senior you spoke to during sales has moved on to the next pitch. With a small team, you work directly with the people writing the code, in our case the founders themselves.

The flip side deserves honesty too: a small agency has less capacity for ten parallel workstreams or a 24/7 support desk. If you have an enterprise-scale app project or web application with dozens of stakeholders, a larger firm may fit better. For most SME projects, however, short lines and senior attention outweigh a big logo on the proposal.

Take the time to choose the right partner. Talk to multiple agencies, ask critical questions, and trust your instinct about the collaboration. The cheapest option is rarely the best one.

Update May 2026: In 2026, AI-coding fluency has become a hard differentiator. Partners that work fluently with Claude Code, Cursor, and Codex ship features in days where traditional agencies still need weeks. In every conversation, ask concretely how their developers use AI agents, how many commits flow through agentic workflows, and how they review AI-generated code. Verify their GitHub history for these patterns. In our experience, the productivity gap between AI-fluent and AI-naive teams is a factor of three to five, and the gap is widening every quarter.