FastRouter.ai

Black Friday just got interesting. From today until Cyber Monday, every FastRouter.ai credit purchase gets a 25% bonus, up to $1,000. If you’ve been meaning to scale your AI workloads, now’s the time: more tokens, more experiments, more room to build. The offer is live on the site. Go claim your credits while they last. https://fastrouter.ai

Most teams ship an LLM feature, watch it work in a demo, then fly blind on cost and quality once it hits production. Join us October 1 for a live masterclass on using continuous evaluation to fix that, a practical 7 day playbook, not just theory. Ritesh Prasad walks you through logging, custom evals, and fast evals on a real production use case, plus how teams have cut AI costs by up to 87 percent by catching the right model swap. 8:00 AM PT / 11:00 AM ET / 8:30 PM IST. 60 minutes, live demo, and Q&A. Save your spot below.

Most teams have no idea if a cheaper model would perform just as well. Oct 1 — we're showing PMs and eng leads exactly how to find out, with a live demo included. Register for free.

People are running AI agent teams for $15–100/month in API costs. Not demos. Production deployments. A stock-monitoring agent that delivers a morning brief, real-time price alerts, and an end-of-day summary — $20/month for infrastructure. An autonomous business operator that has generated $195,000 in verified revenue. A four-agent team running strategy, operations, research, and dev from a single Telegram message. The architecture behind all of them is the same: narrow scope, approval gates on anything that acts in the world, and cheap models for cheap tasks with expensive models reserved for decisions that actually matter. Part 3 of our OpenClaw + FastRouter series breaks down exactly how these deployments work and what they cost to build. Tip of the 🎩 to Aishwarya Naresh Reganti and Kiriti Badam at LevelUp Labs — their OpenClaw Mastery course is the fastest structured path to getting a working agent setup off the ground. Read the full article here: https://lnkd.in/d6-jncxY

In Part 1, we explained why AI agent costs broke when systems moved from chat-based usage to always-on execution. Part 2 focuses on the solution. The core issue is that most agent workloads are treated as if every request requires a high-capability model. In reality, the majority of calls are routine tasks that don’t need that level of reasoning. When everything is routed to the same model, costs scale quickly without improving outcomes. Intelligent routing changes this by matching each request to the right model based on task complexity, cost, and performance requirements. This is what FastRouter’s auto mode does in real time, without requiring manual configuration. Read more here: https://lnkd.in/d5pM43Cd

The shift on April 4 caught many teams off guard. Once Anthropic moved agent usage to API pricing, many setups that had previously run on fixed subscriptions suddenly became expensive. In some cases, dramatically so. That change exposed something deeper: most agent architectures are not designed with cost in mind. The problem isn’t just pricing. It’s how models are being used. What we’ve been seeing across real use cases is that once you introduce routing based on task complexity, the economics start to look very different. You can maintain output quality while significantly reducing spend simply by matching the right model to the right task. In this piece, we break down what changed, why it matters for anyone building with agents, and how to approach it using FastRouter with OpenClaw. Full article in the comments.