Startups > Modular

Modular

Modular helps AI teams deploy models 100x faster across any hardware.

Seed $630M total Founded 2022 Palo Alto, California 144 employees

Modular is an AI infrastructure platform that unifies model serving and GenAI deployment through the Mojo programming language and MAX framework. It abstracts hardware complexity to run popular open models with 10-100x performance improvements over Python, without vendor lock-in. The platform addresses AI's fragmented infrastructure by rebuilding the entire stack from compilers up, enabling developers to deploy across CPUs, GPUs, and edge devices seamlessly.

Problem solved

AI infrastructure is fragmented and inefficient—teams struggle to run models consistently across different hardware platforms with acceptable performance without complex engineering work.

Target customer

AI/ML teams at enterprises and startups deploying large language models, inference workloads, and custom AI applications; hardware vendors and cloud providers seeking performance optimization.

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Founders

Chris Lattner

CEO & Co-Founder

Original architect of Swift and LLVM at Apple; led TensorFlow infrastructure at Google; VP of Engineering at SiFive; BS Computer Science University of Portland.

Tim Davis

President & Co-Founder

Former Google employee; met Lattner at Google and co-founded Modular in 2022 to rebuild ML infrastructure.

James Reynolds

Co-Founder

Limited public information available.

Funding history

Seed $30M June 2022 Led by Unknown · Unknown

Series A $100M 2023 Led by General Catalyst · Google Ventures, Greylock Partners

Series B Unknown 2025 Led by Unknown · Unknown

Series C $250M September 24, 2025 Led by Unknown · 5 investors participated (names not specified)

Total raised: $630M

Industries

Machine Learning Artificial Intelligence Software

Pricing

Mojo: Free community version available; commercial pricing not publicly detailed. MAX: Per-token pricing for managed cloud inference, per-minute pricing for reserved GPUs (NVIDIA/AMD), flexible deployment in managed cloud or customer VPC. No long-term commitments required.

Notable customers

Inworld, Qwerky

Integrations

NVIDIA GPUs, AMD GPUs, PyTorch-compatible APIs, custom GPU kernel deployment

Tech stack

GSAP (JavaScript frameworks) Swiper (JavaScript libraries) jQuery (JavaScript libraries) core-js (JavaScript libraries) animate.css (UI frameworks) Prism RSS Open Graph Linkedin Insight Tag (Analytics) Amplitude (Analytics) Google Analytics (Analytics) Apple iCloud Mail (Webmail) Google Workspace (Email) jsDelivr (CDN) cdnjs (CDN) Cloudflare (CDN) HubSpot (Marketing automation) Linkedin Ads (Advertising) Webflow (Page builders)

Website

modular.com/

Competitors

PyTorch

Established deep learning framework focused on research and development; Modular optimizes for production deployment and hardware abstraction across heterogeneous systems.

TensorFlow

General-purpose ML platform; Modular specializes in performance optimization and model serving with compiler-level infrastructure redesign.

vLLM

Open-source LLM inference engine; Modular offers broader platform including programming language, managed infrastructure, and enterprise support.

NVIDIA Triton

GPU-focused inference server; Modular provides hardware-agnostic optimization and Mojo language for custom kernel development.

Why this matters: Modular addresses a critical infrastructure gap in AI by rebuilding the entire ML stack from compiler level up, solving the fragmentation problem plaguing AI deployment. With $630M in funding and a $1.6B valuation achieved in 8 years, it's backed by top-tier VCs and led by Chris Lattner (Swift/LLVM architect), signaling serious execution on a fundamental infrastructure problem.

Best for: Organizations deploying large language models and custom AI workloads that need consistent high performance across diverse hardware without rewriting code.

Use cases

LLM Inference Optimization

Teams deploying models like DeepSeek and Kimi can run 1000+ models out-of-the-box with MAX framework, achieving 10-100x performance improvements over standard Python implementations. Per-token pricing and no long-term commitments enable cost-effective scaling.

Custom GPU Kernel Development

ML teams can use Mojo to define high-efficiency custom kernels (e.g., specialized silence-detection for voice AI) that run directly on GPUs without Python overhead. Inworld used this approach to create tailored kernels for their conversational AI platform.

Hardware-Agnostic Model Deployment

Companies need to deploy the same AI models across CPUs, GPUs, and edge devices without code changes. Modular's abstraction layer eliminates vendor lock-in and enables seamless scaling from on-premise to cloud infrastructure.

Alternatives

PyTorch + ONNX Runtime Open-source and free but requires manual optimization for different hardware; less integrated than Modular's end-to-end platform.

NVIDIA Triton Inference Server Strong for GPU inference but tightly coupled to NVIDIA hardware; Modular provides broader hardware support and compiler-level optimizations.

Hugging Face Inference API Managed inference as a service with easy API access; Modular offers more control and performance optimization for custom models and kernels.

FAQ

What does Modular do? +

Modular provides an AI infrastructure platform consisting of Mojo (a programming language unifying Python usability with C-level performance) and MAX (a framework for developing, optimizing, and deploying AI models). It abstracts hardware complexity to run AI models 10-100x faster across CPUs, GPUs, and edge devices without code changes or vendor lock-in.

How much does Modular cost? +

Mojo has a free community version; commercial pricing details are not public. MAX uses per-token pricing for cloud inference and per-minute pricing for reserved GPU capacity. No long-term commitments required. Contact Modular for custom enterprise pricing.

What are alternatives to Modular? +

PyTorch with ONNX Runtime (open-source but requires manual optimization), NVIDIA Triton Inference Server (GPU-focused), Hugging Face Inference API (managed service), and vLLM (open-source LLM inference). Each has trade-offs in flexibility, cost, and ease of use.

Who uses Modular? +

AI teams at enterprises and startups deploying large language models and custom AI workloads. Known public customers include Inworld (conversational AI) and Qwerky (memory-efficient model optimization). Target users are organizations prioritizing performance, hardware flexibility, and fast deployment cycles.

How does Modular compare to PyTorch? +

PyTorch excels at research and model development flexibility but requires manual optimization for production deployment. Modular is purpose-built for production inference and deployment, offering 10-100x performance improvements and hardware abstraction. Modular's MAX framework provides PyTorch-compatible APIs for easy model porting while Mojo enables custom low-level optimization.