Deploy and scale

Llama

Whisper

Flux

Deepseek

Llama

Inference that adapts to your needs

Deploy now

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Tailor-made inference for any model at scale

Model Library

Pick from 150+ open-source models or import custom weights

Get started quickly and pay-as-you-go

Import custom models from 10+ cloud repositories

Supports LLMs, VLMs, Diffusion and Speech models

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Deploy Now

Deploy in our cloud or yours

Deploy in any private VPC or on-prem setup 

One control plane to manage deployments across clouds

Native integration with 15+ clouds

B200s, H100s, A100s, L40S, A10G available across the globe

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Our paper on Autoscaling

Scale up in less than 500ms

Rapid auto-scaling for spiky traffic

Scale based on specific metrics to serve strict SLAs

Scale-to-zero based on traffic

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Modular inference stack

Built for performant runtime

Custom built CUDA kernels

Get lowest TTFT and E2E latency

or maximize throughput with most affordable costs

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The most common
mistake in inference

One size does not fit all
Your product deserves tailor-made inference, not generic APIs

Real-time multi-agent reasoning

Voice Agents

Real-time multi-agent reasoning

Streaming STT + TTS

Real-time multi-agent reasoning

TTFB <100ms

Real-time multi-agent reasoning

Scale based on latency threshold

Hardware

Frameworks

Model Backend

Quantisation

Model Backend

Optimisations

KV Caching

vLLM

Transformers

fp16

TP1

CUDA Kernels

Paged attention

A10G

Triton

vLLM Backend

FP8

TP2

Eagle

Static KV Cache

A100

LMDeploy

TensorRT

AWQ

TP4

FA2

ShadowKV

H100

Static KV Cache

ShadowKV

FB Cache

Real-time multi-agent reasoning

Document processing

Real-time multi-agent reasoning

Optimized for high throughput

Real-time multi-agent reasoning

VLM/LLM

Real-time multi-agent reasoning

Scale based on concurrency

Hardware

Frameworks

Model Backend

Quantisation

Model Backend

Optimisations

KV Caching

vLLM

Transformers

fp16

TP1

CUDA Kernels

Paged attention

A10G

Triton

vLLM Backend

FP8

TP2

Eagle

Static KV Cache

A100

LMDeploy

TensorRT

BF16

TP4

FA2

ShadowKV

H100

TensoRT LLM

ShadowKV

GPTQ

TP8

FA3

FB Cache

Real-time multi-agent reasoning

Content Generation

Real-time multi-agent reasoning

Optimised for Cost

Real-time multi-agent reasoning

Fine-tuned LLM

Real-time multi-agent reasoning

Scale based on usage

Hardware

Frameworks

Model Backend

Quantisation

Model Backend

Optimisations

KV Caching

vLLM

Transformers

fp16

TP1

CUDA Kernels

Paged attention

A10G

Triton

vLLM Backend

FP8

TP2

Eagle

Static KV Cache

A100

LMDeploy

TensorRT

BF16

TP4

FA2

ShadowKV

H100

TensoRT LLM

ShadowKV

GPTQ

TP8

FA3

FB Cache

Tested at Scale. Built for Production

Reliable deployments with 99.99% uptime, enterprise-grade security, and completely compliant with highest standards

GDPR

ISO 27001 v2022

AICPA SOC 2

Hear from our Partners

Don't take just our word for it, hear from companies that Simplismart has partnered with

"With Simplismart, we trained and deployed a vision model to process medical prescriptions at 93% accuracy. Their fine-tuning made inference fast, efficient, and effortlessly scalable"

Bhaskar Arun

Lead Data Scientist, Tata 1mg

"Running workloads at our scale demands both speed and adaptability. Simplismart delivered the fastest infrastructure we’ve used and stayed on top of every new development to keep us ahead.”

Ajay Dubey

Senior Engineering Manager, Mindtickle

"Simplismart’s optimizations cut our image generation costs from $30,000 to under $1,000 while halving inference time. Their solution integrated seamlessly, scaling effortlessly with our growing demand."

Shivam R.

Senior Director of Engineering, Invideo

"Simplismart’s solutioning helped us transition to custom models, and their fine-tuning expertise boosted our accuracy. The support quality has been outstanding and they handle all the MLOps heavy lifting so we can focus on building"

Elad Hirsch

Founding Research Scientist, Lica

"We had invested in GPUs, and Simplismart proved the best way to maximize them. Their optimizations cut our peak GPU usage from 15 to 6 while meeting latency targets, making our infrastructure faster and more cost-efficient"

Soumyadeep Mukherjee

Co-Founder & CTO, Dashtoon