Good day, and welcome to Appian's Q4 2024 earnings conference call. At this time, all participants are in listen-only mode. After the speaker's presentation, there'll be a question-and-answer session. Instructions will be given at that time. As a reminder, this call may be recorded. I would like to turn the call over to Jack Andrews, Vice President of Investor Relations. Please go ahead.
Good morning, and thank you for joining us. Today, we'll review Appian's Q4 2024 financial results. With me are Matt Calkins, Chairman and Chief Executive Officer, and Mark Lynch, Interim Chief Financial Officer. After prepared remarks, we'll open the call for questions. During this call, we may make statements related to our business that are considered forward-looking. These include comments related to our financial results, trends and guidance for Q1 and full year 2025, the benefits of our platform, industry, and market trends, our go-to-market and growth strategy, our market opportunity and ability to expand our leadership position, our ability to maintain and upsell existing customers, and our ability to acquire new customers. These statements reflect our views only as of today and don't represent our views as of any subsequent date. We won't update these statements as a result of new information unless required by law.
Actual results may differ materially from expectations due to the risks and uncertainties described in our SEC filings. Additionally, non-GAAP financial measures will be discussed on this conference call. Reconciliations of GAAP to non-GAAP financial measures are provided in our earnings release. With that, I'd like to turn the call over to our CEO, Matt Calkins. Matt.
Thanks, Jack, and thanks everyone for joining us today. In the Q4 of 2024, Appian's cloud subscription revenue grew 19% to $98.9 million. Subscriptions revenue grew by 18% to $136.8 million. Total revenue grew 15% to $166.7 million. Our Adjusted EBITDA was positive $21.2 million, and our cloud subscription revenue retention rate was 116%. Our non-GAAP gross margin was 80% in Q4, our best performance since the IPO. For the full year, Appian's cloud subscription revenue grew 21% to $368 million. Subscriptions revenue grew 19% to $490.6 million. Total revenue grew 13% to $617 million. Our Adjusted EBITDA was positive $20.3 million. The world of AI is very exciting, but it contains an unsustainable imbalance, and all of you know what it is. There's a monstrous amount of investments in AI.
The largest American tech firm spent nearly $250 billion of CapEx last year without a proportional return. AI doesn't generate enough revenue because AI doesn't generate enough value, and this is where Appian can help. Appian creates real value with AI by putting it where it can do the most good. While other firms bring work to AI, we bring AI to work. I mean, we go where work happens, and that's where we deploy AI. We equip AI to make an impact directly in the places where the heaviest and most valuable work already occurs. Work happens inside of a process. A process is a high-volume flow of tasks handled individually and procedurally inside a corporation. These tasks are carefully orchestrated to serve an important goal. Process is how an insurer manages claims and a bank validates money with it.
It's how the government operates procurement cycles and pharma companies run clinical trials. Process is how organizations spend their money, serve their customers, comply with regulation, and build their reputations. Appian is called the process company, and we do a lot of processes. Appian runs 10-20 billion transactions per day on AWS alone. A lot of those transactions will run better when we apply AI. We used to staff our processes with digital workers like RPA and business rules. Now that we have AI, I believe that the value of process automation technology has rou
ghly doubled. It will take years for that value to reach our addressable market, but it's real, and the value of AI can also double when it's used in a process.
That is a bold statement, but if it sounds like an exaggeration that the value of AI could double when used in a process, then hear me out as I make the case for AI process synergy. Here are six ways AI is better when deployed in an Appian process. I'm starting on slide five in the earnings deck. First, it's easy to instantiate AI within an Appian process. To launch an AI agent in any node of a process model takes only a few clicks. Customers can use AI to make suggestions, generate content, parse documents, or take action. We're agnostic about the AI model. Customers can use ours, usually Claude, or bring their own. We're making it easier to access AI. And if DeepSeek commoditizes AI or makes it cheaper, we and our customers stand only to gain. Second, on slide six, our process gives AI structure.
For AI to make value in high-volume workflows, it must have a structured role. Process gives AI a job within a coordinated effort working toward an important goal. AI gets a team of coworkers, an inbox and an outbox, an escalation path, exception handling, and human oversight. I want to emphasize this human-to-AI coordination. Many processes that can benefit from AI require humans as well, as overseers, exception handlers, or final results checkers. For example, one of our customers, an international health technology company, uses AI in a process to allocate human attention to incoming work. They sell medical devices and supplies to healthcare providers, and they're working with us to automate order fulfillment. A third of their orders are submitted through email, and those emails will be processed by Appian AI Agents, whose job is to parse, appraise, and route correspondence to the right actor within the organization.
Many of these requests require a human response, so the AI will frequently assign the next stage of the work to a person. The third reason why Appian process is good for AI is data. AI needs data, and different implementations require different provisioning strategies. It's not always enough to make a heap of data and train your AI model once. Sometimes you need data from disparate systems, or you need fresh data in real time from remote sources of record, or you may wish to retrain periodically on a freshly assembled data set. If you're privacy-minded, you may wish not to train at all, but instead want data provided in the moment. Our process platform sends AI the right data at the right time. The fourth reason AI is better in a process is that we give agents what they need to be successful. Agents seek data, then act.