在人工智能价格战中生存而不摧毁你的企业 Surviving AI Price Wars Without Destroying Your Business —— A16Z

Surviving AI Price Wars Without Destroying Your Business在人工智能价格战中生存而不摧毁你的企业https://www.a16z.news/p/surviving-ai-price-wars-without-destroyingEveryone is talking about outcome-based pricing in AI apps. But almost no one is talking about what is actually happening behind closed doors: price wars.Pricing wars are terrifying for companies of all sizes, but especially for startups. A great team, with the right product, at the right time, can still die from pricing their product incorrectly. And everyone knows that the hard part about pricing products is resisting the pressure to underprice, in order to win competitive deals.Every AI company knows this struggle. New entrants flood the market weekly, burning investor capital to buy distribution at low cost due to falling (and subsidized) cost of tokens. Incumbents feel compelled to respond. “Match all competitors” has quietly become a standard entry in many AI sales playbooks. And once retaliation begins, price cuts cascade — great for buyers who are aware and taking advantage, but brutal for the businesses competing for them. As an AI app startup today, facing feature-similar and well-funded competitors, how do you not die by price war?所有人都在讨论AI应用中的基于结果的定价模式。但几乎没人提及幕后真正上演的戏码价格战。价格战对所有规模的企业都堪称恐怖对初创公司尤甚。再优秀的团队手握再好的产品若在错误的时间点错定产品价格仍可能万劫不复。业界皆知定价最难之处在于抵抗低价抢单的竞争压力。每家人工智能企业都深谙此痛。每周都有新玩家涌入市场借着代币成本下降及补贴的东风烧着投资人的钱低价抢占渠道。老牌企业被迫应战匹配所有竞品报价已悄然成为众多AI销售手册的标准动作。一旦价格反击启动降价潮便接踵而至——这对精明的采购方是天赐良机但对争夺客户的企业而言无异于血腥屠杀。作为当今功能雷同、资金充足的人工智能应用初创公司如何才能避免死于价格战In speaking with many of the large buyers of these AI services (I can’t say who they are, but they’re big, obvious brands you know), it’s actually pretty clear thatcompeting on price isn’t so necessary:These brands have huge budgets to spend, to the extent that they regularly hiremultiple AI products for the same job.Even if you and your competitor are similar,the solution you offer is very different from the customer’s status quo. Price according to the value you provide, and then make sure you’re the product they actually want.Everyone can sense that “category leaders” constantly change, so everyone worries about pricing as the premium option. But these brands have no problem paying a premium,ifyou give them theflexibility and the predictabilityin how they pay for it (in addition to the premium solution). Most startups underestimate this.在与许多大型AI服务采购方不便透露具体名称但这些知名品牌众所周知交流时一个事实非常清晰价格竞争并非必要这些品牌拥有巨额预算甚至会同时采购多款AI产品来完成同一项任务。即便你和竞争对手产品相似但你们提供的解决方案与客户现有模式截然不同。应根据创造的价值定价并确保成为客户真正想要的选择。所有人都能感受到行业领导者不断更迭因此企业常担忧溢价定位。但只要你能在支付方式上提供灵活性和可预测性除优质解决方案外这些品牌完全愿意支付溢价——这一点被多数初创企业低估了。There is no silver bullet for pricing wars; there never has been. But, for AI application companies anyway (this piece isn’t for model providers; that’s a whole different story), if these enterprise buyers are any indicator, you don’t have to give it all away. Here’s what we learned:1. The budgets are there. Stop acting like they aren’t.The most dangerous assumption in a price war is that customers are fighting you on pricebecause they don’t have the money. In enterprise AI right now, that is often simply not true. Leaders understand the magnitude of the opportunity. AI has the potential to reduce costs more dramatically than anything before it—and the risk of moving too slowly is existential. As a result, many large enterprises already have meaningful, pre-allocated AI budgets, and they are actively deploying them. The money is there: it’s just a matter of earning it.价格战中最危险的假设是客户之所以在价格上与你对抗是因为他们没钱。在当前的企业人工智能领域这往往根本不是事实。领导者们深知这一机遇的份量。人工智能比以往任何技术都更有潜力大幅降低成本——而行动过慢的风险关乎生死存亡。因此许多大型企业早已准备了可观且预先划拨的AI预算并正在积极部署这些资金。资金就在那里关键是如何赢得它。In a recent conversation with the Head of AI at a top financial institution, one insight stood out:They intentionally deploy two or three AI tools for thesameuse case. Not because of indecision—but by design. Redundancy is policy.They do not want to rely on a single vendor for any critical workflows. AI apps are still maturing. Performance can fluctuate. Hallucinations happen. Outages are possible. So they hedge.They also recognize that each vendor has distinct strengths. Instead of forcing a single solution across the organization, they match tools to different personas and use cases—for example, deploying multiple coding tools tailored to different teams.Our conversations with mid-market and smaller enterprise companies reveal a different dynamic: they move faster. Teams run parallel demos, and once something shows promise, they quickly move into a proof of concept. One leader at a B2C hardware company described walking away from a low-cost contract with an incumbent technology giant in favor of a smaller, AI-native provider—simply because it delivered the most advanced agent. Across both segments, the pattern is consistent. The winning tool is rarely the cheapest. It is the one that proves indispensable. Which means the real question is not “can we afford this?”—it is “which of the tools we are piloting do we standardize on?”The implication is significant: if you are discounting defensively against a cheaper competitor, you may be giving away margin you never needed to give.The buyer you’re worried about losing may have already preferred you because you are better or already have a budget for both of you.What wins in that environment isn’t the lowest price — it’s becoming the tool they can’t imagine removing. Reliability, security posture, onboarding quality, and — more than anything else — whether you are visibly listening and building: these are the factors that determine which tool survives the consolidation phase.最近与一家顶级金融机构的人工智能主管交谈时有一个观点尤为突出他们有意为同一用例部署两到三种人工智能工具。这不是因为犹豫不决而是有意为之。冗余是他们的策略。他们不希望任何关键工作流程依赖单一供应商。人工智能应用仍在成熟阶段性能可能波动幻觉问题时有发生服务中断也有可能。因此他们采取对冲策略。他们也认识到每个供应商都有独特的优势。与其在整个组织中强制推行单一解决方案他们根据不同角色和用例匹配工具——例如为不同团队部署量身定制的多种编程工具。我们与中型市场和小型企业客户的对话揭示了不同的动态他们行动更快。团队会并行测试多个演示版本一旦某个工具展现出潜力就迅速进入概念验证阶段。一位B2C硬件公司的负责人描述了他们如何放弃与技术巨头签订的低价合同转而选择规模较小但人工智能原生的供应商——只因后者提供了最先进的智能体。两种场景都呈现出一致的规律胜出的工具很少是最便宜的而是被证明不可或缺的那个。这意味着真正的问题不是我们能否负担这个而是我们试用的工具中该标准化哪一个这带来的启示至关重要如果你为了防御更便宜的竞争对手而降价可能是在不必要地牺牲利润。你担心失去的那个客户可能本就因为你更优秀——或者已经为你们双方都预留了预算——而更倾向于选择你。在这种环境下胜出的不是最低价——而是成为他们无法割舍的工具。可靠性、安全态势、上手体验质量以及最重要的——你是否展现出倾听和持续改进的姿态这些才是决定哪个工具能在整合阶段存活的关键因素。2. Premium perception is real — but it’s fragile.Not every AI app company needs to match competitor prices. If your product is genuinely premium — or is perceived as such — you likely have more pricing room than you think. A useful rule of thumb: a strong premium perception can sustain prices 10 to 20 percent above direct competitors without materially increasing churn or creating friction in the purchasing process.并非所有AI应用公司都需要与竞争对手的价格相匹配。如果你的产品确实优质——或被市场如此认可——你可能拥有比想象中更大的定价空间。一个实用的经验法则强大的高端品牌认知度可以支撑比直接竞争对手高出10%至20%的价格而不会显著增加客户流失率或在购买过程中产生阻力。There is even a more surprising way enterprises tolerate price gaps. A VP at an on-demand logistics platform we spoke with found one agentic AI app to be clearly superior to another (to my surprise, the underperforming cheaper one is seen as the market leader) and the superior one is significantly more expensive,yet he still chose to deploy both, despite the price gap.His reasoning was simple: allocate intelligently. Use the premium tool where performance matters most, and rely on the lower-cost option for simpler tasks. The result is not necessarily higher spend, but a more efficient one—better outcomes without materially increasing the budget.But that premium is not a fixed asset. In a market moving this fast, perception can erode quickly — a new entrant with a cleaner UI, a better benchmark, or a louder content presence can shift buyer expectations within a quarter. The companies that hold premium positioning do so actively, not passively. They monitor the signals: sales cycle length, win/loss rates on competitive deals, the language prospects use when they push back on price. When those signals shift, your window to respond is short.That being said, the fact that “market leadership” is so fleeting for AI companies is somethingeveryone already knows, and for that matter, probably overestimates. The proof is in the price wars! The key to avoiding price wars entirely (and protecting your business) is finding another way for your customers to recognize and pay for value. This is where creativity matters, far more than most startups realize.企业容忍价格差距的方式甚至更令人惊讶。我们采访的一家按需物流平台副总裁发现某款自主AI应用明显优于另一款令我意外的是表现较差但更便宜的反而是市场领导者而优质产品的价格要高得多但他仍选择同时部署两者。他的理由很简单智能分配。在性能最关键处使用高端工具简单任务则依赖低成本方案。结果未必是支出增加而是效率提升——在不显著增加预算的情况下获得更好成果。但这份溢价并非固定资产。在快速变化的市场中认知可能迅速消退——拥有更简洁界面、更优基准测试或更强内容声量的新进入者可能在一个季度内改变买家预期。保持高端定位的企业都在主动作为监测销售周期长度、竞争交易的输赢率、潜在客户议价时的措辞。当这些信号变化时企业的反应窗口非常短暂。尽管如此AI公司市场领导地位转瞬即逝已是共识——甚至可能被高估了价格战就是明证完全避免价格战并保护业务的关键在于找到让客户识别价值并为之付费的新方式。这里的创新空间远比大多数初创企业意识到的更重要。3. Pricing units and structures are more important than you think.The most underused lever in AI apps pricing isn’t the number — it’s the unit. How you charge is as much a competitive lever as what you charge. AI app companies are experimenting with per-seat, per-outcome, per-workflow, and consumption-based models. Each unit frames value differently. A per-outcome model, for example, makes price comparisons much harder. You’re no longer being compared on cost per seat but on cost per result. That shift changes the conversation.Across all of our conversations, one theme came up consistently: pricing has to match value. Even very large enterprises are pushing for outcome-based models—gainshare, success-based pricing, or other structures that tie spend directly to results. There is a clear desire to move away from traditional usage-based or seat-based models, which many feel are misaligned with how AI actually delivers impact.At the same time, there is an apparent tension. While buyers want vendors to have meaningful skin in the game, they also need predictability. Budgets still matter. Planning cycles still exist. Fully variable pricing, in practice, can be difficult to operationalize.A VP of AI adoption at a large real estate company put it most directly: vendors should offer dual models, allowing customers to decide between predictability and performance-based upside. In other words, give buyers the ability to trade off flexibility for certainty depending on their internal constraints. In a market where everyone is competing on price, letting buyers choose their own model may be the edge that has nothing to do with price at all.人工智能应用定价中最未被充分利用的杠杆并非价格数字本身而是计费单位。收费方式与收费金额同样能成为竞争利器。AI应用企业正在尝试按席位收费、按成果收费、按工作流程收费以及基于用量的计费模式。每种计费单位对价值的界定都截然不同——以按成果收费为例这种模式让比价变得异常困难因为比较维度从每席位成本转变为每结果成本这种转变彻底改变了商业谈判的逻辑。在我们所有访谈中有个观点被反复强调定价必须与价值匹配。即便是超大型企业也在推动基于结果的收费模式——收益分成、成功付费等将支出与成果直接挂钩的架构。市场明显渴望摆脱传统的按用量或按席位计费模式多数人认为这些模式与AI创造实际价值的方式并不契合。但与此同时存在明显矛盾采购方既希望供应商承担实质风险又需要预算可预测性。毕竟预算审批依然存在规划周期仍需遵守。完全浮动的定价机制在实践中往往难以落地。某大型房地产集团的AI应用副总裁说得最直白供应商应提供双轨模式让客户在可预测性和绩效溢价之间自主选择。换言之根据内部约束条件让采购方自主权衡灵活性与确定性。在这个人人比拼价格的市场中允许客户自选计费模式或许能成为与价格完全无关的制胜优势。4. Don’t discount the product. Discount the proof of concept.One of the most utilized moves in AI competitive selling is also one of the simplest: lower the cost and friction of entry, not the cost of the product itself.Enterprise buyers in AI are often in long, cautious evaluation cycles. Procurement timelines are slow. Security reviews take months. One enterprise buyer at a large bank said that their POC of an AI app took almost a year. The decision to standardize on a tool is significant — and risky. What stops deals from progressing is often not price objection on the full contract; it’s the cost and commitment of getting started.The move is to make the proof of concept (POC) more accessible. Faster to initiate. Cheaper to run. Lower in upfront scoping. Then convert at a fair pricing once you have won the evaluation. A B2C company that purchased an AI app after POC said that they had a flat price for unlimited usage (albeit limited workflows) during their POC. Once the purchase was made, they started paying for exactly what they used. A VP at a large bank said that their AI app provider gave them credits during POC at a discounted rate, but once they started running into performance issues, the strings of the credit pool loosened as both parties wanted the agents to work.This is already pretty common. Companies are offering significantly expanded free tiers or over-delivering during POCs — sometimes by an order of magnitude — to get customers onboarded and deeply engaged. In some cases, this can look like 10–25x more value than what is ultimately included in the paid plan.The goal is not to win on price but to win on adoption: get customers hooked early, before the market consolidates. Elena Verna, Head of Growth at Lovable, puts it plainly: “[The freemium plan] costs us an arm and a leg, but we view it as a marketing expense, not as a cost center.”人工智能竞争性销售中最常用的策略之一也是最简单的降低入门成本和障碍而非产品本身价格。企业采购人工智能产品时往往陷入漫长谨慎的评估周期。采购流程缓慢安全审查耗时数月。某大型银行的采购负责人透露他们测试一款AI应用的概念验证(POC)就花了近一年时间。选定标准化工具是重大且高风险的决定。阻碍交易推进的通常不是全合同价格异议而是启动阶段的成本和投入承诺。关键在于降低概念验证门槛更快启动、更低成本、更简化的前期规划。待通过评估后再以合理价格转化客户。某家B2C公司在POC后采购AI应用时表示测试期间他们以固定价格获得无限使用权限尽管功能受限正式采购后才按实际用量付费。某大银行副总裁指出其AI供应商在POC阶段提供折扣额度但当出现性能问题时双方都希望智能代理正常运行信用额度限制便随之放宽。这种做法已相当普遍。企业通过大幅扩展免费层级或超额交付POC资源有时达十倍量级来吸引客户深度参与。某些情况下客户获得的测试价值可能是付费方案的10-25倍。核心目标不是价格制胜而是抢占采用率在市场整合前就让客户形成使用依赖。Lovable增长负责人Elena Verna直言不讳免费版让我们血本无归但我们将其视为营销支出而非成本中心。5. The real long-term price war isn’t with your competitors. It’s with your customer’s engineering team.There is a threat to AI app companies that gets far less attention than it deserves: as foundation model costs continue to fall, the build-vs-buy calculus shifts. Companies are a lot more sophisticated than expected on this.AI应用公司面临着一个远未得到应有重视的威胁随着基础模型成本持续下降自建与采购的权衡正在发生变化。企业在这方面的考量远比预期更为成熟。When models are expensive and complex to deploy, the case for buying a purpose-built AI app is straightforward. But as inference costs drop and model APIs become easier to integrate, the internal cost of building a custom solution approaches — and in some cases falls below — the cost of a third-party subscription.At that point, the conversation changes. Engineering teams start asking whether they can just build it themselves. And in many cases, they can.Companies we talked to had mixed perspectives on the build vs. buy question. There is, of course, some selection bias—everyone we spoke to had already adopted AI applications in some form. But even within that group, the long-term strategies varied meaningfully.One B2C logistics company expects to move away from third-party tools over time. Their view is that current costs are unlikely to scale efficiently, and that building in-house will ultimately be more economical.In contrast, a smaller B2C hardware company reached the opposite conclusion. For them, building is simply not viable. They do not have the engineering capacity to support it, and even a small team would cost more than their current vendor contracts.Others are taking a more segmented approach. A VP at a financial institution described a clear boundary: anything non-core will be purchased, while anything tied directly to their core product—mortgages and other financial services—will be built in-house, both in the short and long term. VPs and Directors from other industries agreed with this approach.Taken together, these perspectives highlight that the decision is not binary. It is shaped by economics, internal capabilities, and, most importantly, how close the use case is to the company’s core value proposition.This is the price war that matters most in the long run — and the one that can’t be won by discounting. The defense is differentiation that is genuinely expensive to replicate internally: deep workflow integration, continuous model improvement, domain-specific training data, dedicated customer success, and forward-deployed engineers embedded in the customer’s operations. Scale buys time. That depth is what earns loyalty.当模型部署成本高昂且复杂时购买专用人工智能应用的理由显而易见。但随着推理成本下降和模型API更易集成定制解决方案的内部成本逐渐接近——在某些情况下甚至低于——第三方订阅费用。此时讨论方向开始转变。工程团队开始质疑是否应该自主构建。而在多数情况下他们确实具备这种能力。我们调研的企业对自建还是采购问题持有不同观点。当然存在样本偏差——所有受访者都以某种形式采用了AI应用。但即便在这个群体中长期战略也存在显著差异。某B2C物流公司计划逐步淘汰第三方工具。他们认为当前成本难以高效扩展而自主构建最终将更具经济效益。与之相反某小型B2C硬件公司得出相反结论。对他们而言自主开发根本不现实。既缺乏工程能力支持即便组建小型团队的成本也超过现有供应商合约。其他企业采取更分化的策略。某金融机构副总裁划定了明确界限非核心业务全部采购而直接关联核心产品抵押贷款等金融服务的解决方案——无论短期长期——都将自主构建。多位跨行业高管认同这一策略。这些观点共同揭示决策并非二元选择。它受经济性、内部能力以及最关键的因素——应用场景与企业核心价值主张的关联度——共同塑造。这才是最具长期意义的价格战且无法通过简单降价取胜。真正的防御壁垒在于那些内部复制成本极高的差异化优势深度工作流整合、持续模型优化、领域专属训练数据、专属客户成功团队以及前置部署到客户业务中的工程师团队。规模优势赢得时间窗口而专业深度才能赢得客户忠诚。The thread connecting all fiveAcross every conversation we had — financial institutions, logistics platforms, hardware companies, real-estate, travel platforms, and others — the pattern was the same. Nobody said they chose a tool because it was cheapest. They chose the one that proved indispensable, that listened, that made the evaluation easy and the contract fair.The price war is real. But it’s mostly being fought on the wrong battlefield. You don’t get to choose when it starts. You do get to choose whether it’s the fight that defines you.Post script: There is sparse, but some literature on pricing wars which do not provide any silver bullets. If you want, ChatGPT can give you all the advice that has been given. Here’s the prompt:在我们所有的对话中——无论是金融机构、物流平台、硬件公司、房地产商、旅游平台还是其他行业——模式都如出一辙。没有人说选择某款工具是因为它最便宜。他们选择的工具必须具备三个特质无可替代的价值、倾听用户需求的能力、让评估流程简单化且合同条款公平透明。价格战确实存在。但多数人把战场选错了地方。你无法决定战争何时爆发但你能决定是否让它成为定义你企业的战役。附注关于价格战的文献资料虽少但确实存在不过这些研究并未提供万能解决方案。如果需要ChatGPT可以为你汇总所有已知建议。触发指令如下Act as my strategic thought partner and advisor. I want to fully understand the landscape of expert advice on price wars — what strategies, frameworks, and counter-moves have been recommended by the best business thinkers, economists, and practitioners. Cover the full range of current thinking.作为我的战略思维伙伴和顾问。我希望全面了解关于价格战的专家建议格局——顶尖商业思想家、经济学家和实践者推荐了哪些策略、框架和应对措施。涵盖当前所有的思考范围。---