Simplismart announced this week a $7 million funding round for its infrastructure, which enables organizations to deploy AI models. The company hopes to position itself as the critical enabler for AI’s transition into mainstream enterprise operations.
The Series A funding round was led by Accel, with participation from Shastra VC, Titan Capital, and angels, including Notion co-founder Akshay Kothari. This tranche, more than ten times the size of their previous round, is earmarked for research, development, and growth of their enterprise-focused MLOps orchestration platform.
Simplismart was co-founded in 2022 by Oracle Cloud alum Amritanshu Jain and Devansh Ghatak, formerly of Google Search. Its engine allows organizations to more quickly run machine learning models.
“Building generative AI applications is a core need for enterprises today,” Jain said. “However, the adoption of generative AI is far behind the rate of new developments. It’s because enterprises struggle with four bottlenecks: lack of standardized workflows, high costs leading to poor ROI, data privacy, and the need to control and customize the system to avoid downtime and limits from other services.”
Simplismart’s platform offers organizations a declarative language (similar to Terraform) that simplifies fine-tuning, deploying, and monitoring GenAI models at scale. Third-party APIs often bring concerns about data security, rate limits, and lack of flexibility. Issues with deploying AI include access to computing power, model optimization, scaling infrastructure, CI/CD pipelines, and cost efficiency. Simplismart’s end-to-end MLOps platform standardizes these orchestration workflows, allowing the teams to focus on their core product needs rather than spending numerous manhours building this infrastructure.
“Until now, enterprises could leverage off-the-shelf capabilities to orchestrate their MLOps workloads since the quantum of workloads, be it the size of data, model or compute required, was small,” Jain said. “As the models get larger and the workload increases, it will be imperative to have command over the orchestration workflows. Every new technology goes through the same cycle: exactly what Terraform did for cloud, Android Studio for mobile, and Databricks/Snowflake did for data.”
“As GenAI undergoes its Cambrian explosion moment, developers are starting to realize that customizing and deploying open-source models on their infrastructure carries significant merit; it unlocks control over performance, costs, customizability over proprietary data, flexibility in the backend stack, and high levels of privacy/security,” said Anand Daniel, partner at Accel.
Solving MLOps workflows will allow more enterprises to deploy GenAI applications with more control. They want to manage the tradeoff between performance and cost to suit their needs. Simplismart said it believes that providing enterprises with granular building blocks to assemble their inference engine and deployment environments is key to driving adoption.