Build vs. Buy: Making the Right SEO Decision

AI has dramatically expanded what SEO teams believe is possible to automate. Tasks that once demanded dedicated engineering resources are now tackled with a Claude chat or a custom GPT. That shift is exhilarating, but it introduces a dangerous assumption: that everything should be automated. In practical terms, this often boils down to the classic build versus buy decision.

This dilemma was never simple, and AI has layered on fresh complexity. The calculus goes far beyond upfront cost. You must weigh security, ongoing maintenance, data access, internal capabilities, workflow fit, and the long-term question of whether a custom solution will remain maintainable, reliable, and genuinely useful six months from now.

How AI Lowers the Barrier to Building

AI has undeniably lowered the barrier to experimentation. Without deep technical knowledge, you can spin up a custom GPT, connect data sources, or build an internal AI assistant. However, the person who prototypes a solution isn’t necessarily the person who can maintain a reliable, production-grade tool over several years.

In most cases, AI excels at helping SEO teams analyze data, identify patterns, summarize information, and recommend actions. It saves significant time, and teams ignoring AI are clearly falling behind. But for now, AI isn’t performing truly creative work in the human sense. It operates from existing patterns to predict likely outputs. That may evolve.

Crucially, AI introduces hidden costs. Internally built tools are often perceived as free because the invoice doesn’t land on the SEO team’s desk. But that ignores the real expenses of token usage, API calls, infrastructure, engineering time, security reviews, and ongoing maintenance. This effect is already visible. Reuters has described “corporate AI sticker shock,” with companies struggling to forecast usage-based costs. TechCrunch reported that Uber imposed AI spending caps after burning through its annual AI budget in four months.

Today, marketing teams aren’t the heaviest AI users compared to engineering. But that can change rapidly. And when usage grows, so will the bills, naturally forcing companies to ask which AI tools and workflows create value and which merely consume budget.

Start by Defining What You Need

Before deciding to build or buy, SEO teams must clearly define their needs. The term “AI solution” covers vastly different things:

• A custom tool: A complex internal system, often requiring engineering support, focused more on automation but potentially including AI.These are not interchangeable, yet they are often mislabeled. Calling everything an “AI agent” creates confusion and leads to inaccurate estimates of cost and complexity.

Look for Repetitive, Context-Rich Tasks

We are still in an experimental phase. Most of what our team has built focuses on daily tasks that require significant manual effort. For example, we created a custom GPT to evaluate whether our content matches specific personas and their pain points. The goal isn’t to replace the human writer, but to determine if a piece remains generic and what additions could make it more relevant.

We also use AI for translations, monthly reporting, and a weekly summary that combines meeting notes, Slack, and Jira to help me spot missed tasks or needed follow-ups. One of our latest workflows transforms recorded internal meetings into organized landing page briefs. These tasks are ideal for AI-powered custom workflows because they rely on internal context, repeatable processes, and company-specific knowledge.

Not Everything Should Be Built

Consider our experience with a prompt tracking tool that a colleague “vibe-coded.” It worked as a starting point, but data presentation was imperfect and creating trend graphs required manual steps. Soon, it became a maintenance burden. Every external change in the LLM tools required fixes that needed engineering help.

The real issue was reliability. For AI visibility and prompt tracking, we needed consistent data in one place, presented for analysis over time. That’s why we moved to a specialized platform like Peec AI instead of maintaining our own version. The experiment was still valuable. It helped us understand the problem, the complexity, and the features we actually needed from a vendor.

Here’s my advice: whether you plan to build or buy, always test what’s already available on the market. Only then will you truly understand what you need. You might think you need ten features, only to discover you use only three.

For business-critical tools like rank tracking, AI visibility tracking, and website crawling, small SEO teams without dedicated technical support should generally be cautious about building from scratch. If the data is fundamental to decision-making, reliability must be your primary concern.

Use AI Where Your Data Already Lives

Buy the crawler, rank tracker, or AI visibility platform. Then focus your internal efforts on connecting data from these tools to custom information, such as your GA and GSC accounts or CRM data. Once connected, create reports that combine all these sources for unified analysis.

MCP connections (Model Context Protocol) are worth considering. MCP is an open standard for connecting AI applications to external systems, data sources, tools, and workflows. With MCP servers, you can analyze data from your primary tools directly using AI, taking your current workflows to the next level.

This doesn’t require you to learn to code. But you need to know enough to ask the right questions. If a tool connects to an internal knowledge base, customer data, or proprietary research, be aware of potential security risks. It might be better for the company to dedicate an engineer to support you than to risk exposing sensitive information.

You must also understand the final cost of a custom tool for your company. Custom tools aren’t free just because the invoice doesn’t sit with SEO. Engineering time, security reviews, AI tokens, and API usage are all part of the cost. Before asking leadership for a tool, SEO teams should be able to explain the workflow problem, the expected value, the cost of buying versus building, and what happens if nothing is done. The best requests don’t start with “We need this tool.” They start with: “Here is the problem, here is why it matters, here is what we’ve tested, and here is the best way we think we can solve it.”

How to Prioritize What to Build First

There’s no single prioritization matrix for every situation. A website crawler, a content evaluation tool, a report builder, and a competitive intelligence system can’t be judged by the same criteria. If you need more than one tool, start by mapping your current workflow and your ideal situation. Once you do that, patterns become clear. Your strongest priorities will likely fall into two groups.

The first are tools that can support revenue creation. SEO teams are typically part of marketing, and marketing is expected to generate visibility or leads. If a tool helps identify content opportunities, improve conversion rates, increase AI visibility, or surface competitive gaps, it becomes a priority.

The second group includes workflows and tools that minimize repetitive manual work. This category may not directly create revenue, but it frees up your team for more strategic work.

Don’t underestimate quick wins. Stakeholders don’t want to wait three months for results. A smaller project that delivers value in three weeks builds trust and makes it easier to get support for bigger initiatives.

Cross-team value should also influence your decision. SEO problems are often not isolated. Competitive intelligence, for example, matters to PPC, ABM, content, product marketing, and sales. If several teams share the same pain, the business case becomes stronger. Don’t be afraid to act as a cross-team synchronization layer. Talk to other teams, understand their workflows and pain points, and identify where your needs overlap.

Remember, the best tool is not always the most ambitious one. Starting small is often the smartest move.

Good Decisions Start with Proper Scoping

AI has made building easier, but that doesn’t mean you should skip careful thought about what truly needs to be built. Before deciding to build, buy, or customize, take the time to properly scope the work.

Understand the problem, the expected value, who will use the solution, and who will maintain it after launch. Talk to your team and other teams. Determine whether this is only an SEO problem or a wider business problem. Don’t build because AI makes it possible. Don’t buy because a demo looks impressive.

Without proper scoping, you can end up with an expensive SaaS tool that doesn’t fit your workflow or an internal tool your team can’t maintain. Always think first. Dedicate enough time to scope properly. Then decide whether to build, buy, or customize.