Inference Is the Most Important Market in Software
Inference Is the Most Important Market in Software
- 1作者判断 AI 推理市场 2027 年将达约 3500 亿美元,接近数据库市场 1900 亿美元的两倍
- 22025 年企业为运行 AI 模型支付约 250 亿美元,作者预计今年推理市场规模约 1300 亿美元
- 3作者认为推理消耗账单将超过席位订阅费,经典软件 72% 的混合毛利率将会下滑
- 4前沿模型混合定价 18 个月内从 $0.96 涨至 $11.25,GPT-6 Sol 随后将该档位重置为 $4.00
推理市场规模将超过数据库,应用层公司的收入与成本结构随之转向按用量计费,实质上在变成推理分销商。
- 影响:软件采购预算从按席位转向按用量,客户会要求可预测的支出方案;毛利率可能跌破传统 SaaS 水平,CAC 与回本周期需要重算;客户自带密钥时厂商在用收入换毛利。
- 参考:可考虑先算出自家产品的每客户 token 成本占收入比例,再决定按席位还是按用量定价;值得评估小模型、更优 harness 与路由方案来压缩推理开销。
- 依据:推理市场规模与 2027 年预测为作者估算,数据库规模引 Gartner 预测,token 成本每季降约 47% 引 Epoch AI,属 VC 个人分析口径。
In short : AI inference is set to surpass the database market to become the most important market in software, reaching ~$350b by 2027. This shifts software unit economics: usage dwarfs platform fees, gross margins compress below classic SaaS norms, and infrastructure becomes the dominant component of COGS.
If a founder built a startup in the database market in the mid-2010s, 2% market share meant the company could IPO. No surprise the database was the most important software category in that era.
Inference is about to surpass it.
In 2025, companies paid about $25b to run AI models. This year the market reaches roughly $130b, within striking distance of Gartner’s $161b forecast for databases.1 By 2027 AI inference reaches ~$350b, passing databases ($190b) by nearly 2x.
That crossover makes inference the most important market in software : a structural transformation mutating every application into an inference reseller.
That shift rewires application/harness unit economics in three ways :
-
Usage could dwarf platform fees & fuel record-setting growth. The inference consumption bill will exceed the seat license or base subscription. This changes AE compensation plans, demands predictable plans for customers who fear blown budgets, & complicates forecasting.
-
Gross margins compress below SaaS norms. The blended 72% gross margin of classic software2 will decay unless an application develops a proprietary harness to aggressively compress token overhead,3 or fierce model competition drives inference costs down faster than limited supply gooses prices. This upends CAC & payback economics.
-
Customers who bring their own keys (BYOK) force vendors to trade revenue for gross margin. When an enterprise supplies its own GPU cluster or model API credentials, the software vendor books almost pure software margin, but on a vastly smaller top-line contract.
Could the AI price war prevent some of these changes? Capability-adjusted token costs fall ~47% each quarter.4 But list prices for frontier flagships keep resetting upward as capability leaps : the frontier blended price rose from $0.96 to $11.25 in 18 months before GPT-6 Sol reset the tier to $4.00.
These economics matter tremendously to the application layer. Infrastructure costs are suddenly a significant contributor to overall COGS, & potentially more than half of revenue is allocated to it.
Just like a long-haul trucker watching diesel surge beyond $8 per gallon & wondering about their economics, software companies will watch price per token & hunt for efficiencies on their rigs with smaller models, better harnesses, & new routing techniques.
& that creates plenty of opportunity for innovation.
Get the next one in your inbox
The 1-minute read that turns tech data into strategic advantage.
Read by 150k+ founders & operators.
GP at Theory Ventures. Former Google PM. Sharing data-driven insights on AI, web3, & venture capital.
来源:Tomasz Tunguz 博客 · tomtunguz.com