Opinion: The era of effortlessly switching between cutting-edge AI models is disappearing as vendor-specific constraints intensify and expenses rise.

It wasn’t long ago that developers casually jumped from one AI frontier model to another: first enamored with Gemini 3.1 Pro, then moving to Claude 4.6, and chasing the allure of GPT-5.5. This rapid turnover is manageable for hobbyist coders but a nightmare for enterprises like Janet’s Professional Software.

Enterprise AI buyers now face a dual challenge: vendor lock-in has become a significant barrier to switching, compounded by vendors imposing price hikes that are disrupting traditional software economics. Historically, AI services were priced as loss leaders, a trend now forcibly corrected.

Research by AI platform provider Zapier, surveying 542 US executives, discovered overconfidence was rampant: nearly 90% believed they could transition vendors within four weeks, with 41% claiming they could do so in under a week. Reality contradicts this naive optimism as shifting from one vendor to another is fraught with difficulties.

Zapier found only 42% of those switching platforms experienced smooth transitions, while 58% met with outright failure or unexpected complexity. Many had underestimated the intricate technical dependencies involved in AI models, including API specifications, proprietary training datasets, specialized deployment tools, and necessary workflow integrations.

Zapier highlights the issue: when AI is fully embedded into company processes, it creates undocumented dependencies and edge cases — supposed to be temporary — that prove challenging to port over. AI consultant Haroon Choudery notes that switching vendors now involves not just APIs, but organizational memory and workflows.

Meanwhile, pricing strategies are shifting. Once affordable, models like OpenAI’s GPT-5.2 have seen costs soar from $1.25 per input token to $5.75. Other companies like Anthropic have shifted from stable pricing to dynamic, usage-based billing, leading to spikes in costs for heavy users.

The landscape indicates a paradigm shift as AI expenses become much akin to infrastructure investments, driven by rising memory chip costs and the financial demands of extensive data centers. AI isn’t traditional SaaS; each operation incurs actual costs, emphasized by Datos Insights CEO Eli Goodman, warning that AI pricing is substantially different: providers can’t mitigate costs, which are proportional to usage.

As enterprise AI pushes forward, companies need to strategize to avoid lock-in pitfalls and prepare for inevitable price escalations dictated by market capabilities.