BharatMLStack

What is BharatMLStack?

BharatMLStack is a production-ready, cloud-agnostic ML infrastructure platform that powers real-time feature serving, model inference, and embedding search at massive scale. Built and battle-tested at Meesho, it is designed to help organizations ship ML to production faster, cheaper, and more reliably.

Our Vision

BharatMLStack is built around four core tenets:

Workflow Integration & Productivity

Ship ML to production faster than ever.

• 3x faster experiment-to-deployment cycles
• 95% reduction in model onboarding time

Cloud-Agnostic & Lock-In Free

Run anywhere. Own your stack.

• Runs across public cloud, on-prem, and edge
• Kubernetes-native with zero vendor lock-in

Economic Efficiency

Do more with less.

• 60–70% lower infrastructure costs vs hyperscaler managed services
• Optimized resource utilization across CPU and GPU workloads

Availability & Scalability

Enterprise-grade reliability at internet scale.

• 99.99% uptime across clusters
• 1M+ QPS with low latency

Designed Truly for Bharat Scale

Built for the demands of one of the world's largest e-commerce platforms:

Metric	Performance
Feature Store	2.4M QPS (batch of 100 id lookups)
Model Inference	1M+ QPS
Embedding Search	500K QPS
Feature Retrieval Latency	Sub-10ms

Core Components

Component	Description	Version	Docs
TruffleBox UI	Web console for feature registry, cataloging, and approval workflows	v1.3.0	Docs
Online Feature Store	Sub-10ms feature retrieval at millions of QPS with streaming ingestion	v1.2.0	Docs
Inferflow	DAG-based real-time inference orchestration for composable ML pipelines	v1.0.0	Docs
Numerix	Rust-powered math compute engine for high-performance matrix ops	v1.0.0	Docs
Skye	Vector similarity search with pluggable backends	v1.0.0	Docs
Go SDK	Go client for Feature Store, Interaction Store, and logging	v1.3.0	Docs
Python SDK	Python client libraries for Feature Store and inference logging	v1.0.1	Docs
Interaction Store	ScyllaDB-backed store for user interaction signals at sub-10ms	—	—
Horizon	Control plane that orchestrates all services and powers TruffleBox UI	v1.3.0	—

Full documentation at meesho.github.io/BharatMLStack | Blogs

• All Blog Posts

Quick Start

git clone https://github.com/Meesho/BharatMLStack.git
cd BharatMLStack/quick-start
#Set versions
ONFS_VERSION=v1.2.0 HORIZON_VERSION=v1.3.0 TRUFFLEBOX_VERSION=v1.3.0 NUMERIX_VERSION=v1.0.0

./start.sh

For step-by-step setup, Docker Compose details, sample data, and health checks, see the full Quick Start Guide →.

Architecture

Use-Cases

BharatMLStack powers a wide range of ML-driven applications:

Use-Case	What BharatMLStack Enables
Personalized Candidate Generation	Retrieve and rank millions of candidates in real time using feature vectors and embedding similarity
Personalized Ranking	Serve user, item, and context features at ultra-low latency to power real-time ranking models
Fraud & Risk Detection	Stream interaction signals and features to detect anomalies and fraudulent patterns in milliseconds
Image Search	Run embedding search at 500K QPS to match visual queries against massive product catalogs
LLM Recommender Systems	Orchestrate LLM inference pipelines with feature enrichment for next-gen recommendation engines
DL & LLM Deployments at Scale	Deploy and scale deep learning and large language models across GPU clusters with Inferflow orchestration

Contributing

We welcome contributions from the community! Please see our Contributing Guide for details on how to get started.

Community & Support

• Discord: Join our community chat
• Issues: Report bugs and request features on GitHub Issues
• Email: Contact us at [email protected]

License

BharatMLStack is open-source software licensed under the BharatMLStack Business Source License 1.1.

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