AI for Coding: To Buy or Not To Buy

Guidelines for IT departments on where to start and where the journey leads.

The old advice - “buy for parity, build for competitive advantage” - still works, but only when what you’re buying is truly ahead of anything you could assemble yourself and will stay ahead long enough to matter.

Many AI companies claiming to offer solutions actually provide very little; they’re essentially selling DIY systems you could build yourself—systems that will become obsolete quickly and won’t stay competitive in the long run. How to spot them is explained below.

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When buying clearly beats building

1. Cloning a ChatGPT-style front-end
   The ChatGPT website isn’t just well designed; it’s continually updated with the latest breakthroughs. It rolled out deep search the moment the feature debuted, launched a custom-GPT builder, and will likely add an interface for agentic connections (and much more) before long. Keeping pace simply isn’t worth the effort—the complexity is greater than it looks. Behind the scenes, the model orchestrates context integration via RAG, web search, code execution, Canva, and other sources. You’ll always be a step behind. And remember, there are already plenty of alternatives from hyperscalers such as Gemini and Microsoft Copilot.

2. Home-rolled RAG plumbing
   LangChain, LlamaIndex, and LangGraph release updates several times a week such as new chunkers, embed-drift fixes, smarter caching, and more. Because these frameworks are community-driven and widely adopted, having your developers learn them will both advance their careers and deliver more value to you than reinventing the wheel.

3. Writing a “model-agnostic” SDK wrapper
   The OpenAI Python SDK v1.0 refactor broke dozens of community wrappers overnight; maintaining your own custom SDK would be a constant headache for your developers.

4. Training a frontier-scale LLM from scratch
   Compute alone now costs tens of billions of dollars, and to deliver something truly different from what’s already out there, you need large volumes of proprietary, high-quality data that no one else has. If you simply pour $10 million into training a domain-specific model, as BloombergGPT did, you’ll likely still be outperformed by a well-prompted OpenAI model.

5. Hand-building guardrails, observability and evaluation tools
   Managed layers, AWS Bedrock Guardrails, Helicone, TruLens, Prompt Flow and LangSmith, already give you policy controls, token-level tracing and rollback dashboards, while open-source evaluators such as OpenAI Evals and Ragas cover retrieval and answer-quality scoring. They all ship fixes every few weeks. Re-implementing that stack just ties up engineers on undifferentiated plumbing.

Moat vs. Effort to Maintain Matrix

Buying wisely

Buying feels safer than building but only if the thing you buy will still matter after the next hype cycle. Here are some signs that you might want to skip the purchase:

1. The vendor has no moat
   In 2025 the only durable moats are proprietary data and massive compute. DeepSeek reproduced GPT-4o-level reasoning with an open model, while Meta releases Llama weights for anyone to fine-tune. If a product leans on “secret algorithm” rather than unique data or compute, it probably could be replicated.

2. It solves everyone’s problem
   History shows that once a solution proves broadly useful, a hyperscaler clones or acquires it, think Netscape versus Microsoft. If the feature could logically be absorbed into Microsoft 365, Google Workspace or AWS, be ready that it will happen and will work much better.

3. It solves no one’s problem
   Drag-and-drop “AI-app studios” promise anyone can build what engineers write in fifty lines. In reality, developers do not have a problem with code-first tools and do not need a no-code approach. Non-coders most likely do not want to learn the UI in the first place and would rather have developers build a solution for them.

4. It’s a wrapper
   Some startups resell the same OpenAI or Anthropic API you can call directly, adding only a thin dashboard. Their margin disappears the day the underlying provider changes pricing or rate limits.

5. Hidden total cost of ownership & lock-in
   That low-cost SaaS plan can quickly balloon into big per-token and data-transfer fees. Gartner predicts more than 40 % of AI-agent projects will be scrapped by 2027 because costs spiral or value isn’t clear.

6. Weak enterprise foundations
   If the vendor isn’t already certified for SOC 2, ISO 27001, or the EU AI Act, you’ll have to build the security and compliance safeguards yourself.

Here is a list of well-known companies that, in my view, display these red flags and may lack a true moat despite their high valuations: