Gradient, a startup enabling developers to create and tailor AI applications in the cloud using large language models (LLMs), has emerged from stealth mode with $10 million in funding. The investment is led by Wing VC, with participation from Mango Capital, Tokyo Black, The New Normal Fund, Secure Octane, and Global Founders Capital.
Gradient’s CEO, Chris Chang, co-founded the company alongside Mark Huang and Forrest Moret. They conceived the idea while working on AI projects at major tech companies such as Netflix, Splunk, and Google. They recognized the transformative potential of LLMs like OpenAI’s GPT-4 for enterprises but saw the need for a reliable method to incorporate private, proprietary data into these models.
Traditionally, the focus had been on improving a single, generalized model, and existing solutions supported this approach. However, relying solely on one model posed limitations in terms of task-specific performance. Gradient was designed to simplify the deployment of specialized and finely-tuned LLMs at scale. The platform operates in the cloud, allowing organizations to develop and integrate thousands of LLMs into a single system.
Gradient’s customers don’t need to start training LLMs from scratch. The platform hosts various open-source LLMs, such as Meta’s Llama 2, which users can fine-tune to suit their needs. Gradient also offers models designed for specific use cases (e.g., data reconciliation, context gathering, paperwork processing) and industries (e.g., finance and law).
Gradient can host and serve models through an API, similar to Hugging Face, CoreWeave, and other AI infrastructure providers. Alternatively, it can deploy AI systems in an organization’s public cloud environment, including Google Cloud Platform, Azure, or AWS. In both scenarios, customers retain full ownership and control over their data and trained models.
Chris Chang emphasized that Gradient aims to lower the barriers to AI development, making it more accessible and affordable for businesses. The platform simplifies harnessing AI’s potential for businesses, which is a significant value proposition.
Now, you may wonder what sets Gradient apart from other startups offering tools to combine LLMs with in-house data and the many companies customizing LLMs for enterprise clients as a service. Chris Chang argues that Gradient stands out by allowing companies to “productionize” multiple models simultaneously. Furthermore, the platform is cost-effective, with pricing based on demand, ensuring users only pay for the infrastructure they utilize.
Despite the competition in the LLM development space, Gradient is well-positioned to benefit from the growing interest in generative AI, including LLMs. The AI sector has seen a substantial influx of venture capital funding, and the generative AI market is expected to reach $42.6 billion in 2023.
Currently, Gradient collaborates with approximately 20 enterprise customers, serving thousands of users collectively. Its short-term objectives include expanding its cloud infrastructure and increasing its team size from 17 full-time employees to 25 by the end of the year.
