The Wall Street Journal reported that a $13 billion AI startup is betting on cheaper alternatives to OpenAI and Anthropic. Enterprises are shifting from pilots to production and seeking to control inference costs across support, copilots, and content workflows. Open source options such as Meta's Llama and models from Mistral enable targeted deployments with retrieval and fine-tuning to improve cost predictability. Procurement teams weigh SLAs, latency, security certifications, data retention, indemnity, and regional hosting against premium providers. Vendors distribute through AWS, Microsoft Azure, and Google Cloud marketplaces, while access to Nvidia accelerators influences performance and cost. Pricing includes per token and per seat plans, with some platforms routing simple tasks to lower cost models and reserving premium models for complex work. Founders are advised to build evaluation harnesses, track cost per outcome, and negotiate for predictable terms.
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