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Everyone is searching for the best AI model . Should we use GPT? Claude? Gemini? Local models? But after working with AI-assisted engineering workflows, we started asking a different question: What if there isn’t a single “best” model? What if the right answer depends entirely on the task at hand ? The deeper we explored enterprise AI adoption, the clearer it became: One AI model is rarely enough for an entire software development lifecycle. The “One Model for Everything” Trap Most teams begin their AI journey with a simple approach: Pick an AI provider. Standardize on that model. Use it for everything. Initially, this works well. But as adoption grows, cracks begin to appear. Some tasks need: deeper reasoning, faster responses, lower costs, stronger privacy guarantees, domain specializati
Полный текст и контекст у первоисточника: https://dev.to/flowsquad-ai/why-one-ai-model-is-not-enough-for-enterprise-software-development-1hbm