Learn practical, battle-tested ways to cut LLM inference costs — from model-level optimizations and smarter hardware choices to operational tricks like batching, caching, and context engineering — and get clear guidance on when to apply each technique and what to watch for. Running large language models in production is a recurring bill you can’t ignore. […]
Quick summary: Buying or integrating a third-party model isn’t the same as choosing a library. It’s a cross-functional program that touches product, engineering, security, privacy, legal, finance, and customer teams. This playbook gives product managers an operational process and downloadable-ready artifacts (scoring matrices, test suites, sandbox checklist, contract snippets, rollout stages) so you can evaluate […]
Reinforcement Learning from Human Feedback (RLHF) is no longer just a research trick — it’s the practical way teams align large language models to be helpful, safe, and on-brand. But the algorithm (reward model + policy tuning) is only half the work. To operate RLHF at SaaS scale you need robust human-feedback pipelines: consistent rating […]
A hands-on playbook for engineering, product and ML teams — design patterns, routing, fallbacks, arbitration, and ops for running multiple models from different vendors in production. Running multiple vendor models together isn’t just a vendor-diversity exercise or a cost play. It’s about resilience (one vendor has an outage), economics (use cheaper models when good enough), […]
Building AI features into a SaaS product is exciting — and expensive if you don’t account for where that cost actually lands. Most FinOps writeups focus on high-level savings: batching, quantization, or switching models. That’s useful, but it misses the question product teams desperately need answered: How do you allocate LLM costs to features and […]
Legal disclaimer: This article summarises California Senate Bill SB-53 for informational purposes only and does not constitute legal advice. Consult a licensed attorney for compliance guidance. TL;DR SB-53 (Transparency in Frontier AI Act) requires large frontier model developers to publish safety frameworks and testing information. SaaS vendors should review procurement & provider disclosures and update […]
Anthropic’s new Claude Sonnet 4.5 is being positioned as a milestone release for agentic AI and developer productivity: the company claims it sustains truly long-running autonomous tasks while delivering top-tier coding ability and better, safer behavior than prior Claude models. For engineering teams building agent-driven workflows, IDE assistants, or production-grade automation, Sonnet 4.5 is worth […]
The EU’s landmark AI Act is changing the rules of the road for software vendors that embed AI into products and services. For SaaS vendors, the Act is not a theoretical policy exercise — it forces concrete lifecycle changes: classification of products by risk, ongoing monitoring, documentation requirements, incident reporting, human oversight, and data-governance obligations. […]
Enterprises are rapidly adopting large language models (LLMs) to power search, assistants, content generation, summarization, and novel customer experiences. But dropping a foundation model into production is not the endpoint — it is the start of a complex operational journey. LLMOps (Large Language Model Operations) has emerged as the discipline and toolchain that closes the […]
Meta recently launched Vibes, a short-form feed of AI-generated video clips inside the Meta AI app and on meta.ai. The feature lets users generate, remix, and share bite-size videos created by generative models. Each clip can be altered — change the music, update the visual style, remix motion or color — and the platform surfaces […]