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getting your inference right shouldn't be hard. use https://nitter.cf/t.co/cY1dYE2kNN

Joined April 2026
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Launch Day! Launch Day! Launch Day!
AI teams overpay for inference twice: once for models bigger than they need, and again for idle capacity they don’t use. @useRouterPlus is the extended inference team for fast-growing AI companies. We test models and providers on your traffic and get providers to compete for your work. Make your inference better. Go to routerplus.com
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RouterPlus (YC F26) retweeted
AI teams overpay for inference twice: once for models bigger than they need, and again for idle capacity they don’t use. @useRouterPlus is the extended inference team for fast-growing AI companies. We test models and providers on your traffic and get providers to compete for your work. Make your inference better. Go to routerplus.com
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RouterPlus (YC F26) retweeted
call for startups : corgi for ai inference. anyone know of a fast way to underwrite insurance for inference contracts ? we are seeing many mid six to seven figure contracts where we need this desperately. ideally it should be from someone who truly understants the inference markets, variance in providers and can run their own evals if need be. also better if the cost of insurance can be baked in the token price itself somehow
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RouterPlus (YC F26) retweeted
if you wanna make the most of the proliferation of inference providers and get the best price possible - reach out to us. today we have a network of 40+ that we work with we have been able to get our customers something even as extreme as 70% off of public listing price with BETTER SLAs than they had before routerplus.com/capacity/#inf…
There is an absolute barrage of inference providers popping up that get to $1M/mo extremely quickly. This is the rough economics of inference providers: Even if you rent on-demand B300s at $8/GPU-hr, many open source models (GLM 5.3, Kimi K3, DeepSeek 4.1 Flash / Pro) can get you >$10-20/GPU-hr at reasonable throughputs (100tok/s). DeepInfer from DeepSeek directly shows they can get $38/GPU-hr on off-peak hours. Every chip / model has a different throughput / interactivity curve, but there is a lot of arbitrage between even high rental spot prices and token prices. New chips (Vera Rubins) also change this meaningfully. Your ability to be making gross margin relies on your utilization and how good your stack is. Gateways like OpenRouter, Vercel and more help solve that problem by aggregating demand to you. May inference providers discount their tokens to get longer term commits too. I've even heard stories of selling back Chinese model tokens to Chinese labs! One thorn in the economics of these players is that many Chinese open source model labs are now asking for rev share if you're at a certain scale, espp for pre-release access to their models for day 1 support. That further pushes the threshold to be in the money. The barrier to entry seems low and falling. There are even stories of smaller ones who can "vibe code" a way to stand up OSS models on a cluster they rent. And as a result, we're seeing 10s of such companies pop up almost every week and immediately skyrocketing revenue at low-ish gross margin and raising rounds on the back of that. Unsurprisingly, because the actual service provided seems commodity, this eventually drives up compute prices and drives down token prices. In theory, it would seem like neoclouds who have the lowest cost of compute and themselves have started inference businesses would win the race to the bottom (Crusoe Coreweave Nebius and GMI all have inference businesses), with larger players (Modal Fireworks Baseten) being second because they are well capitalized to make bigger compute commits cheaply and have demand (high utilization), but the reality is the pie is growing so quickly that even upstarts are absolutely crushing it and growing aggressively (inference dot net, DeepInfra, Wafer, Relace, Morph). Strategies vary across the board: neoclouds want to serve inference, inference players want to be neoclouds and do higher margin reserved instances / post training, and some want to go heterogeneous hardware or creatively flexible compute constructs. The risks are: do open source models continue to be competitive? Will their creators rent seek more? Will their shape change compute needs? How do different chips change economics? Who gets compute in a supply crunch? Below is a aggressive sketch of how gross margin scales with utilization for various models / chip sets. For now, there are many inference providers and they’re all ripping!
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routerplus is inviting providers to submit bids for a Kimi K3 contract worth $580k a month. list your inference needs too. providers.routerplus.com/rfq…
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RouterPlus (YC F26) retweeted
Krea 3 by @krea_ai is live on @useRouterPlus great to have partnered with the team for day 0 support would love to see examples of what you folks do with it on RouterPlus !! What models would you like us to see support next ?
Krea 3, the new image editing model from @krea_ai, is now on @useRouterPlus . High-quality edits in under 6 seconds. Reimagine the scene while keeping your subject consistent, or blend multiple reference images with a simple text prompt.
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Krea 3, the new image editing model from @krea_ai, is now on @useRouterPlus . High-quality edits in under 6 seconds. Reimagine the scene while keeping your subject consistent, or blend multiple reference images with a simple text prompt.
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RouterPlus (YC F26) retweeted
CDMs are the future! Was great working with @scaledown_team on this release. Scaledown's purpose built CDMs - classify, extract and summarize are now available on @useRouterPlus
You already gave the LLM the answer. So why is it writing it out from scratch? Introducing a new AI model architecture: Constrained Decoding Models (CDMs) LLMs were built to write. Most production workloads just need an answer: fast, consistent, and affordable. CDMs are purpose-built for 1 task each. They read your input once and skip the token-by-token generative loop that makes LLMs slow and expensive. Not just a decision model, but a family of 3 CDMs live today: • Classify: define your labels and get back a calibrated probability + reason for each. Used for routing, tagging, and intent detection. • Extract: describe the entities in natural language and get back well-formed JSON with confidence scores + citations. Used for web scraping, document processing, and PII redaction. • Summarize: pass in long text, images, or PDFs and get back a consistent summary under 1,000 tokens. Used for context compaction, metadata generation, and transcript digests. Built for production: • 1M tokens of multimodal (text + image) input, 31x larger than Jev • 77.2% on DecisionBench vs Jev's 72% • 200x faster, 467x cheaper than frontier LLM APIs • $0.05/M input tokens. Output is free (yes, even for summarize) Live on your favorite platforms today: @OpenRouter @vercel_dev @LiteLLM @LangChain @TrustedRouter @itsArthurAI @useRouterPlus Get 50M input tokens for free (for the first 50 hours) at link below 👀
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ScaleDown's Constrained Decoding Models (CDMs) are now available on @useRouterPlus. You can now use their Classify, Summarize, and Extract models which feature a 1M input token context window, multimodal (text + image), and are reported to be 200x faster and 467x cheaper than traditional LLMs! Try to today at: app.routerplus.com/playgroun…
You already gave the LLM the answer. So why is it writing it out from scratch? Introducing a new AI model architecture: Constrained Decoding Models (CDMs) LLMs were built to write. Most production workloads just need an answer: fast, consistent, and affordable. CDMs are purpose-built for 1 task each. They read your input once and skip the token-by-token generative loop that makes LLMs slow and expensive. Not just a decision model, but a family of 3 CDMs live today: • Classify: define your labels and get back a calibrated probability + reason for each. Used for routing, tagging, and intent detection. • Extract: describe the entities in natural language and get back well-formed JSON with confidence scores + citations. Used for web scraping, document processing, and PII redaction. • Summarize: pass in long text, images, or PDFs and get back a consistent summary under 1,000 tokens. Used for context compaction, metadata generation, and transcript digests. Built for production: • 1M tokens of multimodal (text + image) input, 31x larger than Jev • 77.2% on DecisionBench vs Jev's 72% • 200x faster, 467x cheaper than frontier LLM APIs • $0.05/M input tokens. Output is free (yes, even for summarize) Live on your favorite platforms today: @OpenRouter @vercel_dev @LiteLLM @LangChain @TrustedRouter @itsArthurAI @useRouterPlus Get 50M input tokens for free (for the first 50 hours) at link below 👀
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RouterPlus (YC F26) retweeted
Self hosting has inference providers alternative now. So unless you have reasons to own hardware and self host or you are post training, everyone doesnt need to download anymore. You can access these services like subscription. Routers make switching, discovery and experimentation far more convenient than you downloading and trying. Routers are also catalog now, an active one and not a passive listing. People now go to marketplaces like @openrouter, @vercel , @useRouterPlus for their discovery vs huggingface h/t to @ParikhRajesh07
We've been monitoring a decline in HuggingFace downloads across the top open LLMs we track for interconnects. It looks like there was a slight change in counting of downloads, as we saw a ~30% download rate drop for many top models, but this is continuing over time. What else do people think it may be?
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Nimble v3 from @bespokelabsai is now FREE on RouterPlus ! check it out app.routerplus.com/models/be…
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RouterPlus (YC F26) retweeted
Nimble is available on @useRouterPlus!
Launching Decision models on @useRouterPlus. You can now use various system 1 decision models such as Jev, @perplexity_ai Decider, @bespokelabsai Nimble, Mercury Decide, Clef, Kev 4B and Decider 2B with 3 models free !! app.routerplus.com/playgroun…
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checkout decision models on routerplus.com/playground we have some free decision models as well !
Launching Decision models on @useRouterPlus. You can now use various system 1 decision models such as Jev, @perplexity_ai Decider, @bespokelabsai Nimble, Mercury Decide, Clef, Kev 4B and Decider 2B with 3 models free !! app.routerplus.com/playgroun…
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RouterPlus (YC F26) retweeted
btw at @useRouterPlus we have been using numerous techniques to ensure that inference providers are ACTUALLY delivering what they claim - this involves running relatively expensive live evals , checking quality and also measuring symmetric KL divergence to monitor deviations from actual expected behaviour since a lot of inference contracts go through us we can actively monitor for such discrepencies and hold providers accountable. its only done now for certain larger contracts - but we expect to roll it out to the wider platform soon
Yesterday I posted about inference price war pushing margin down. But I since learned inference scammers are a thing with shady price manipulations: As inference providers, - You can lower your input token price 100x cheaper but up your output price 5x. You rank higher in openrouter in price but tots cost is much higher. - You can quantize the model to serve cheaply but users are buying a shittier model. - You can retain and sell user data elsewhere to make more money. - You can secretly swap to DeepSeek v4 flash but say it’s GLM 5.3 10% of the time. Therefore it’s hard to know 1. You get the model you ask for 2. You pay for the tokens you actually burn across cache input, input, output and 3. Your data is safe. I’m sure openrouter is working on it AND baseten, fireworks, together and other enterprise brands will continue to thrive even with higher pricing because they sell peace of mind.
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