AI Integration

Articles, guides and tutorials on integrating AI into software products, covering APIs, language models and practical AI implementation strategies.

AI Agent Payments: What Agentic Commerce Means for Merchants

AI agent payments have produced four competing specifications, two industry foundations and an enormous amount of coverage in about a year. What they have not yet produced, for the overwhelming majority of merchants, is revenue. The gap between the noise and the numbers is the useful part to understand, because the...

Cloudflare AI Gateway: Control Your LLM Costs

Most teams call a model provider straight from application code. The API key sits in an environment variable, the SDK call is three lines, and it works first time. Cloudflare AI Gateway exists because of what happens next. The bill arrives and nobody can say which feature caused it. The provider has a bad afternoon and...

Fine-Tuning vs RAG vs Prompting: What Each Costs

The fine-tuning vs RAG question usually arrives as a statement: “we need to fine-tune a model on our data.” It is one of the most expensive sentences in enterprise AI, and it is usually wrong. Not always, but usually. The request nearly always means one of two things: the model does not know about our business, or the...

Moving Off OpenAI: What an Open-Weight Switch Costs

The case for moving off OpenAI got considerably stronger during 2026. Open-weight models reached a level where the quality gap on ordinary production work became narrow, published pricing undercut the frontier providers, and the weights themselves became downloadable, which turns a supplier relationship into an option....

Self-Hosting Kimi K3: Hardware, Cost and Sovereignty

Self-hosting Kimi K3 became technically possible on 27 July 2026, when Moonshot AI published the weights for a 2.8-trillion-parameter model alongside production inference support. A great many organisations read that news and concluded they could now run frontier-class reasoning on their own hardware and stop paying...