Amritanshu Jain
CEO
Simplismart
Simplismart provides a high-performance inference engine and MLOps platform that simplifies deploying, fine-tuning, and scaling generative AI models across cloud and on-premises environments. The platform features sub-60-second predictive autoscaling, custom-built CUDA kernels for optimized latency or throughput, and native integration with 15+ cloud providers. It serves production workloads for companies including HeyGen, Higgsfield, InVideo, Shiprocket, Apna, and Ema.
- Artificial Intelligence
- Developer Tools
- Software Only
- Enterprise Software
Updated 4 days ago
Funding
Raised to date
$21MRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
across 2 rounds
AKDVM+1$14M · Series B
Announced Oct 2026 · from article date
Disclosed cumulative: $21M
2/2
RoundDateAmountValuationInvestors
Founders
Product
Problem
Deploying and optimizing generative AI models for on-premises or cloud environments is complex and time-consuming, often requiring extensive manual configuration and specialized expertise. This complexity leads to prolonged deployment cycles, increased operational costs, and hinders the ability to rapidly scale AI-powered applications.
Solution
Simplismart provides a high-performance inference engine and MLOps platform that simplifies the deployment, fine-tuning, and scaling of generative AI models. The platform supports various cloud providers and on-premises infrastructure, enabling users to import models from repositories like Hugging Face or deploy custom models. Simplismart's solution offers optimized inference speeds, rapid autoscaling, and comprehensive monitoring tools to minimize compute costs and maximize performance. With features like one-click fine-tuning and a streamlined deployment process, Simplismart reduces the complexity of MLOps, allowing organizations to accelerate their AI initiatives.
Target Audience
The primary target audience includes data scientists, machine learning engineers, and MLOps teams seeking to streamline the deployment and optimization of generative AI models in various cloud and on-premises environments.
Features
- Optimized inference engine with custom-built CUDA kernels for lowest TTFT and E2E latency or maximum throughput at affordable costs
- Sub-60-second pod autoscaling with predictive, traffic-aware scaling based on request rate trends, concurrency, and latency headroom
- Native integration with 15+ cloud providers (AWS, Azure, GCP) and support for private VPC or on-premises deployments
- SimpliTune: One-click fine-tuning with parallel training experiments
- SimpliDeploy: Streamlined model deployment with optimized performance and reduced costs
- SimpliObserve: Comprehensive monitoring of GPU utilization and node clusters
- Context-aware routing that dynamically directs requests based on prompt size, model availability, and fallback logic
- Kubernetes-native and BYOC friendly with Terraform-like deployment APIs for reproducibility and governance
- Enterprise-grade security with GDPR and ISO 27001 v2022 compliance