I'm Nigel, one of the team here. 👋 We're excited to bring Coworker.ai to the world. Would love to hear from anyone who's already hit their AI spend wall, curious what the breaking point looked like for you/your org. And happy to answer any questions on how the credit system or model routing works under the hood!
Human Signal
创始人 / Maker 评论
优先展示 Product Hunt 上对理解条目有帮助的人类文本。
Topics
ProductivitySaaSArtificial Intelligence
痛点分析发布于 2026/05/27
痛点为 AI 基于上游原始证据的初步提炼；未包含额外中国市场检索。
痛点
企业团队在使用AI时面临不可预测的token成本激增，从每月50万美元飙升至1500万美元，迫使CFO在削减AI支出或裁员之间做选择。现有流程中，团队习惯性地默认使用最强大的模型（如Opus 4.7），但80%的任务并不需要如此高的能力，导致大量浪费。手动按任务类型路由模型虽然可行，但维护成本高，且分类器需要足够快以避免延迟，低置信度时的回退机制也缺乏保障。这种成本失控和路由决策的摩擦，直接导致预算压力、协作效率下降，以及因信任问题而难以切换到更经济的模型。
Discussion
精选 Product Hunt 讨论
保留原始讨论语境，用来交叉验证上游条目的真实反馈。
Hey Product Hunt 👋 We keep hearing the same thing on repeat: enterprise AI token costs are exploding. Orgs that were spending $500K/year in December are spending $15M/year in May. And CFOs are starting to ask the same question: do we cut back AI spend, or cut heads? Coworker gives organizations a third choice: more AI, less spend. Coworker delivers the same frontier-quality chat, cowork, and code for 80% less. We do that by pairing every task with the right context and model for the job - open or closed. That means you get the same output quality as Opus 4.7, but 5x the tokens for the same spend versus Anthropic or OpenAI API rates across: Chat - grounded in your company's real context and a persistent knowledge graph Build - docs, decks, pdfs, real-time dashboards, apps or any artifact and share across your org Code - any arbitrary task in a virtual sandbox Agents - automate workflows end to end with long-running agents and complex triggers Meet - meeting summaries, transcripts, and follow-up actions via a meeting notetaker or ambient transcription Enterprise-ready - all models hosted in the US, SOC 2, pen-tested, 30+ enterprise connectors We're getting things started by giving everyone who signs up this week 500 credits on us. And if you sign up in the next 24h you'll get an additional 200 credits. Head over to Coworker.ai - I can't wait to see what you build. Alex
Running AI agents across Tuple's client base, model cost was the biggest variable we couldn't predict. The instinct is always to default to the most powerful model, but 80% of tasks don't need it — and that 80% is where the bill comes from. Context-aware routing is the right architectural call. The hard part isn't the routing logic, it's getting teams to trust the cheaper model when it handles something well. People revert to expensive defaults out of habit. Design the confidence score UI carefully — that's where user trust actually lives or dies.
Context-aware routing that downgrades requests to cheaper models based on complexity is genuinely hard to get right. The classifier has to be fast enough not to add meaningful latency. At RetainSure we've been hand-routing between models by task type and it's become its own maintenance burden. How do you handle classification confidence thresholds, and what's the fallback when confidence is low?
Congrats on the launch @alex_calder , very timely! Upvoted :) So is this about storing memory/context efficiently to avoid agents running same queries again and again? Or you have a mechanism to stop agents from traversing some paths because you somehow figure out that is dead end?
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"body": "<p>I'm Nigel, one of the team here. 👋</p><p></p><p>We're excited to bring <a href=\"https://Coworker.ai\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">Coworker.ai</a> to the world. Would love to hear from anyone who's already hit their AI spend wall, curious what the breaking point looked like for you/your org. And happy to answer any questions on how the credit system or model routing works under the hood!</p>",
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"body": "<p>Hey Product Hunt 👋</p><p></p><p>We keep hearing the same thing on repeat: enterprise AI token costs are exploding.</p><p></p><p>Orgs that were spending $500K/year in December are spending $15M/year in May.</p><p></p><p>And CFOs are starting to ask the same question: do we cut back AI spend, or cut heads?</p><p></p><p>Coworker gives organizations a third choice: <strong>more AI, less spend.</strong></p><p></p><p>Coworker delivers the same frontier-quality chat, cowork, and code for 80% less. We do that by pairing every task with the right context and model for the job - open or closed.</p><p></p><p>That means you get the same output quality as Opus 4.7, but 5x the tokens for the same spend versus Anthropic or OpenAI API rates across:</p><p></p><p><strong>Chat</strong> - grounded in your company's real context and a persistent knowledge graph</p><p><strong>Build</strong> - docs, decks, pdfs, real-time dashboards, apps or any artifact and share across your org</p><p><strong>Code </strong>- any arbitrary task in a virtual sandbox</p><p><strong>Agents</strong> - automate workflows end to end with long-running agents and complex triggers</p><p><strong>Meet</strong> - meeting summaries, transcripts, and follow-up actions via a meeting notetaker or ambient transcription</p><p><strong>Enterprise-ready</strong> - all models hosted in the US, SOC 2, pen-tested, 30+ enterprise connectors</p><p></p><p>We're getting things started by giving everyone who signs up this week 500 credits on us. And if you sign up in the next 24h you'll get an additional 200 credits.</p><p></p><p>Head over to <a href=\"https://Coworker.ai\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">Coworker.ai</a> - I can't wait to see what you build.</p><p></p><p>Alex</p>",
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"body": "<p>Congrats on the launch <a href=\"https://www.producthunt.com/@alex_calder\" data-node-type=\"mention\" data-mention-type=\"user\" data-mention-id=\"alex_calder\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">@alex_calder</a>, very timely! Upvoted :)</p><p>So is this about storing memory/context efficiently to avoid agents running same queries again and again? Or you have a mechanism to stop agents from traversing some paths because you somehow figure out that is dead end?</p>",
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