The AI market expanded rapidly across all four layers (data, compute, models, and applications). For example, NVIDIA’s data center revenue jumped from $47.5B to $115.2B in a single year; OpenAI reached about $13B in annual revenue; and Anthropic approached $7B in ARR.
We tracked revenue data from over 100 AI companies. Explore how revenues shifted across compute, data, models, and applications layers from 2023 to 2025.
AI revenue breakdown by subcategory
Note: We identified over 20 subcategories under data, compute, model, and app layers. For simplicity, we included 5 subcategories with the highest revenue.
See the methodology to learn how we gathered AI revenues data.
AI revenue growth rate
The table above shows the percentage increase in AI revenues from 2023 to 2024 and from 2024 to 2025. For example, the total revenue of companies in the data layer increased from $7.907B in 2023 to $12.606B in 2024, and from $17.974B in 2025, corresponding to an approximately 159% between 2023-2024 and 143% increase between 2024-2025.
The table above shows how revenue is distributed across the data, compute, model, and app layers in 2023, 2024, and 2025.
Data layer revenues
For the data layer, Databricks ($4.8B), Snowflake ($4.68B), and MongoDB ($2.46B) have the highest revenue in 2025. These three dominate because they own the foundational data infrastructure every AI application sits on: the lakehouse (Databricks), the warehouse (Snowflake), and the operational database (MongoDB). They capture AI demand regardless of which models or apps win.
Although the top of the data stack is dominated by data platforms, the middle and bottom layers tell a different story. Vector DBs (Pinecone, Qdrant, Weaviate) are all sub-$100M despite years of hype around RAG, and several companies were acquired before proving standalone viability.
Compute layer revenues
NVIDIA leads the compute layer with $115.2B in 2025 revenue, compared to $47.5B in 2024 and $15B in 2023. Qualcomm ($44.3B) and AMD ($34.6B) are the next-largest players in the table, though their AI chip revenue is reported within broader semiconductor segments, limiting direct comparability.
The interesting dynamic in compute is the emergence of a second tier, with CoreWeave ($5.1B), Lambda ($760M), and Together AI ($300M) in 2025. One possible explanation for the growing interest in cloud GPU players is that the existing GPU leaders (such as AWS, Azure, and GCP) are proving insufficient to meet market demand.
The open question is which moves faster: efficiency gains that reduce compute per query (smaller models, quantization), or new use cases that expand total demand (agents, video, enterprise rollouts). If efficiency wins, hyperscalers absorb the market; if adoption wins, specialist GPU clouds keep growing.
Model layer revenues
OpenAI ($13B) and Anthropic ($7B) are rapidly separating from the field. All other major players: Mistral ($400M), Cohere ($240M), xAI ($500M), ElevenLabs ($330M in voice) are clustered well below in comparison to OpenAI and Anthropic.
ElevenLabs in voice and Midjourney in image generation are two leaders in the model category, outperforming general-purpose foundation models. The hardest position in this layer is being a general-purpose model company without a major cloud distribution deal or an attractive consumer product. Mistral and Cohere both face that problem.
Application layer revenues
The pattern in the application layer shows that AI-native apps that replace an entire workflow are the best performing. One of the signals for this is the coding category, where Cursor, Replit, Lovable, and Bolt collectively indicate that developers will pay more for tools that can both automate work.
Cursor’s jump from $1M to $1B in two years and Lovable’s increase from $1M to $400M in a single year are the most extreme growth figures in the dataset. They mark the shift from AI as a coding assistant to AI as the primary development environment, which fundamentally shifted the typical SaaS growth.
In terms of subcategories, healthcare AI (Abridge, Tempus) is growing, as it operates in high-value, regulated areas where the ROI of automation is easy to quantify.
Jasper AI’s revenue fell from $120M to $55M before partially recovering to $88M, still below its 2023 level. The drop shows that horizontal writing assistants without workflow lock-in are at risk of being displaced by both foundation models (such as ChatGPT) and embedded features in tools users already own (such as Notion AI and Google Docs).
Methodology for revenue estimates
We gathered public data on AI revenues from research platforms such as Sacra, GetLatka, Macrotrends, and Crunchbase; company-owned sources such as investor relations reports, company newsrooms, official blogs, and SEC filings; financial media organizations such as Fortune, CNBC, Reuters, Bloomberg; tech media reports from TechCrunch; and regulatory/official sources for public companies such as SEC EDGAR filings.
Revenue figures reflect calendar year 2023, 2024, and 2025, or the fiscal year ending closest to those dates. Differences in fiscal calendars can also affect comparisons across different companies.
Note: For the many private companies in this dataset (for example, Anthropic, Mistral, ElevenLabs, and Cursor), the revenue figures are essentially informed estimates.
Cite this research
Pick the format that matches where you're publishing. Pasting the link version into your CMS preserves the backlink.
@misc{ermut2026,
author = {Ermut, Sıla},
title = {{Compare AI Revenues Across the Stack}},
year = {2026},
month = may,
howpublished = {\url{https://aimultiple.com/ai-revenues}},
note = {AIMultiple. Retrieved May 22, 2026}
}Results and timestamps of 88 data points. Download the data used in this article as a ZIP file containing one csv file and a README.
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