FastRouter

Two of the three ways FastRouter Insights finds savings cost nothing to generate. The third one costs something on purpose. Model Switch replays your real requests against alternative models and scores both outputs with an LLM judge before it ever recommends a change. That's real inference, so it's opt-in, not on by default like the other two. The tradeoff is worth it. You're not guessing whether a cheaper model holds up on your traffic; you're looking at evidence from your own requests before you touch anything in production.

LLM gateway pricing looks simple at evaluation time and gets re-litigated by finance six months later. There are four pricing models hiding behind that one pitch, and only one of them doesn’t compound as you grow. Here’s the math to run before you sign anything. Read more in our newest article: fastrouter.ai/blog/posts/llm…

We finished #2 Product of the Day on Product Hunt 🏆 Huge thanks to everyone who upvoted, commented, shared our launch, asked sharp questions, and tried FastRouter; you made this day what it was. We read every comment, and we're grateful for each one. To our team: thank you for the long hours and the care you put into this launch. This is only the beginning. We have some very good features coming, and we'll be back with another launch soon. Stay tuned. producthunt.com/products/fas…

We're currently #1 on Product Hunt today 🚀 Thank you to everyone who has upvoted and commented so far. This is a huge moment for our team. There's still time left in the day, so if you haven't yet, check out the launch and leave a comment or an upvote if you feel like it. producthunt.com/products/fas…

We're live on Product Hunt today 🚀 This one means a lot to the whole team. If FastRouter has helped you, an upvote or a comment on the launch page goes a long way, and we'd love to hear your feedback. 👉producthunt.com/products/fas… Thank you for being part of this. 🙏

Running Cursor, Cline, and Claude Code every day? Your AI spend is probably growing for reasons none of your dashboards explain. Coding agents are spend amplifiers by design. A single request turns into dozens of tool calls, and independent benchmarking shows the cost gap between tools is real in both directions: Claude Code uses roughly 5.5x fewer tokens on complex, multi-file work, while Cursor delivers better accuracy-per-dollar on simple, single-file tasks. We broke down why standardizing on one tool means overpaying on the other half of your traffic, and how routing by task shape, plus full cost visibility across all three tools, actually closes the gap. Read it here: fastrouter.ai/blog/posts/you…

Something big is coming from FastRouter. We've been building this for weeks, and it's almost ready to share. Stay tuned, more very soon.

Your multi-provider router is probably taking a cut of every token you send it. At $30K/month in AI spend, a "small" 5% markup = $1,500+/month for doing nothing but sitting in the middle. Here's the actual cheapest way to access GPT-6, Claude & Gemini through one API. fastrouter.ai/blog/posts/che…

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: fastrouter.ai/webinar-stop-p…

Every AI product starts the same way. One provider's SDK, one model, one clean API call. It works, and for a while it even feels predictable. Then a modality gets added. Then another provider. Spend splits across dashboards that don't add up to an answer finance can use. Adding a gateway fixes the visibility problem. It does not automatically fix the cost problem. A dashboard can show you what happened without ever telling you what to change next. That's the gap FastRouter Insights is built to close. Your traffic already knows where the savings are. Insights just has to be watching. Learn more: fastrouter.ai/features/insig…

Most teams paying for LLM API calls resend the same system prompt, tool schema, or RAG payload on nearly every request, and pay the full input token price each time. FastRouter's Insights engine catches this automatically. It's a free, read-only weekly scan of your own traffic that finds repeated prompt prefixes, models what you'd save with caching turned on (cache reads run up to 90% cheaper), and shows you the exact key and model to fix. No model swap. No output change. No config change until you decide to act. Just evidence from your own usage, pointing at your own bill. Read how the detection and savings math actually works → lnkd.in/dy7C_MSp

One model, one pass, one confident-sounding response, and no way to know if it's actually right or just fluent. For anything ambiguous or high stakes, that's a bad way to make a decision. FastRouter Blend runs the same question through a panel of models and has a judge compare the results: agreements, disagreements, what got missed, where the confidence looks shaky. You get a second opinion before you act, not after something breaks. Link: lnkd.in/d9tuwpJQ

GPT-6 Astra is live on FastRouter. ~1.05M token context. Configurable reasoning effort. Top Coding and Top Intelligence badges. Full agentic tool use through the Responses API: web search, code execution, file search, computer use. One model ID away: openai/gpt-6-astra Learn more: lnkd.in/dbgJASuN

Live Anthropic incident hit Opus 5 for ~3 hours today. We ran 150 requests through FastRouter during that window, split across Anthropic, Bedrock, and Vertex AI. 150/150 succeeded. Full breakdown: fastrouter.ai/blog/posts/ant…

Most teams optimize LLM spend like it's a prompt problem. In reality, a lot of waste is operational: background and batch workloads quietly run on standard tiers long after they stopped needing real-time speed. FastRouter's Insights makes this visible. Every week it reviews your last 7 days of traffic and produces ranked, evidence-backed recommendations scoped to the exact project, API key, and model. You get the math and the request evidence. Nothing is auto-applied. One of the quickest wins it surfaces: Flex Tier. Same model, same output quality, cheaper tier. The trade-off is slower processing and occasional queuing, so it's ideal for batch enrichment, offline document processing, evals, and scheduled pipelines, but not for user-facing chat. If you're using FastRouter, open Optimize → Insights, filter to Flex Tier, sort by savings, and review the top recommendation first. One change is often worth more than a dozen small tweaks. Read more: fastrouter.ai/blog/posts/sto…

Ask three models the same hard question and you'll get three different answers. Most tools quietly pick one, or worse, blend them into something that reads confident and means nothing. FastRouter Blend does the opposite. It sends your prompt to a panel of models in parallel, then has a judge model tell you where they agree, where they disagree, what only one model thought to raise, and where the answers sound more confident than they should. It's not a consensus engine. It's a second opinion that shows its work.

Ask one model a hard question and you get one confident answer. Ask three and you find out where they actually agree. FastRouter Blend sends your prompt to a panel of models in parallel, then a judge model compares the results: where they agree, where they disagree, and what only one of them thought to raise. It doesn't average the answers into something safe. It shows you the disagreement. Read more: fastrouter.ai/blog/posts/fas…

We spend a lot of time in developer discussions lately, and the same story keeps showing up in different words. Someone's app goes down. They check their own code first, find nothing wrong, then realize a single provider had an incident and their whole product inherited it. A rate limit hits at the worst possible hour and there's no plan B in place. It's not a code problem. It's an architecture problem, and it's the exact one a routing layer with automatic fallback is built to remove.