Build production ML pipelines faster
Open source, end-to-end ML infrastructure stack built for scale, speed, and simplicity. Integrate, deploy, and manage robust ML workflows with full reliability and control.
Adopted by data teams building at scale
Why BharatMLStack
The Real Barriers to Scaling Machine Learning
ML teams spend more time fighting infrastructure than building intelligence. BharatMLStack removes those barriers.
Platform Components
BharatMLStack Components
Purpose-built components for every stage of the ML lifecycle, from feature serving to model deployment.
Online Feature Store
BharatMLStack Online Feature Store delivers sub-10ms, high-throughput access to machine learning features for real-time inference. It seamlessly ingests batch and streaming data, validates schemas, and persists compact, versioned feature groups optimized for low latency and efficiency. With scalable storage backends, gRPC APIs, and binary-optimized formats, it ensures consistent, reliable feature serving across ML pipelines.
Learn more →Inferflow
Inferflow is BharatMLStack's intelligent inference gateway that dynamically retrieves and assembles features required by ML models using a graph-based configuration called Inferpipes. It automatically resolves entity relationships, fetches features from the Online Feature Store, and constructs feature vectors without custom code.
Learn more →Skye
Skye enables fast similarity retrieval by representing data as vectors and querying nearest matches in high-dimensional space. It supports pluggable vector databases, ensuring flexibility across infrastructure. The system provides tenant-level index isolation while allowing single embedding ingestion even when shared across tenants, reducing redundancy.
Learn more →Numerix
Numerix is a high-performance compute engine designed for ultra-fast element-wise matrix operations. Built in Rust and accelerated using SIMD, it delivers exceptional efficiency and predictable performance. Optimized for real-time inference workloads, it achieves strict sub-5ms p99 latency on matrices up to 1000×10.
Learn more →Predator
Predator streamlines infrastructure and model lifecycle management. It enables the creation of deployables with specific Triton Server versions and supports seamless model rollouts. Leveraging Helm charts and Argo CD, Predator automates Kubernetes-based deployments while integrating with KEDA for auto-scaling and performance tuning.
Learn more →Proven at scale
Scaling Numbers
Daily Orders
Daily orders processed via ML pipelines
QPS on FS
QPS on Feature Store with batch size of 100 id lookups
QPS Inference
QPS on Model Inference
QPS Embedding
QPS Embedding Search
See it in action
Demo Videos
Watch short demos of each BharatMLStack component in action.
Feature Store
Learn how to onboard and manage features using the self-serve UI for the Online Feature Store.
Embedding Platform
Walkthrough of onboarding and managing embedding models via the Skye self-serve UI.
Numerix
Step-by-step guide to configuring and running matrix operations through the Numerix self-serve UI.
Predator
How to deploy and manage ML models on Kubernetes using the Predator self-serve UI.
Inferflow
Setting up inferpipes and feature retrieval graphs through the Inferflow self-serve UI.
From our blog
View Our Blogs
Technical articles, architecture deep-dives, and the story behind BharatMLStack.
Building Meesho's ML Platform: From Chaos to Cutting-Edge (Part 1)
Building Meesho's ML Platform: Lessons from the First-Gen System (Part 2)
Cracking the Code: Scaling Model Inference & Real-Time Embedding Search
Designing a Production-Grade LLM Inference Platform: From Model Weights to Scalable GPU Serving
LLM Inference Optimization Techniques: Engineering Sub-Second Latency at Scale
Beyond Vector RAG: Building Agent Memory That Learns From Experience.
Deploy ML models with confidence
Comprehensive stack for business-ready ML. Integrates seamlessly with enterprise systems. Robust security and regulatory compliance.