Inceptron compiler, now open for early access. Auto-compile models for maximum efficiency. Join early access →

Inceptron compiler, now open for early access. Auto-compile models for maximum efficiency. Join early access →

Inceptron compiler, now open for early access. Auto-compile models for maximum efficiency. Join early access →

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The platform for scalable, reliable, and efficient inference

The platform for scalable, reliable, and efficient inference

Run open-source or fine-tuned models on infrastructure purpose-built for production.

Run open-source or fine-tuned models on infrastructure purpose-built for production.

Get Started

Get Started

Get Started

Contact us

Contact us

Contact us

Products

Hosted inference

Build with Model APIs

Test new workloads, prototype new products, or evaluate the latest models with production-grade performance — instantly.

Test new workloads, prototype new products, or evaluate the latest models with production-grade performance — instantly.

Explore the model library

Explore the model library

Explore the model library

Optimize

Supercharge your Models

Use our proprietary inference-optimization on models without restrictions or overhead, for the best possible performance in production.

Use our proprietary inference-optimization on models without restrictions or overhead, for the best possible performance in production.

Platform

Build on a powerful

foundation

From compiler to runtime, Inceptron powers low-latency, scalable inference without the busywork. 

Compiler-accelerated runtime

Compiler-accelerated

Compiler-accelerated

Our proprietary optimization compiler fuses graphs, tunes kernels, and manages memory for your target hardware—cutting latency and cost.

Fast endpoints, no cold starts

Fast endpoints

Fast endpoints

Launch model endpoints in one step. Pre-warmed replicas and cached weights keep p50/p95 low, with autoscaling that follows real traffic.

Launch model endpoints in one step. Pre-warmed replicas and cached weights keep p50/p95 low, with autoscaling that follows real traffic.

Launch model endpoints in one step. Pre-warmed replicas and cached weights keep p50/p95 low, with autoscaling that follows real traffic.

Connect to MLOps tools

Connect to MLOps

Connect to MLOps

Mount your cloud buckets, plug into CI/CD, and stream telemetry to your observability stack—without changing your workflow.

Multi-cloud capacity

Multi-cloud

Run across providers. We place replicas where GPUs are available and fail over automatically, so capacity is there when you need it.

Platform UI

Spin up endpoints and test in the built-in chat. Version models, manage keys, and go live in minutes.

Usage

Track requests, latency, and success rates by model and workspace. Get alerts and drill into traces to fix issues fast.

Spending

See total spend, tokens, and compute by project and model. Set budgets and alerts with workspace, endpoint, and time breakdowns.

Platform UI

Spin up endpoints and test in the built-in chat. Version models, manage keys, and go live in minutes.

Platform UI

Spin up endpoints and test in the built-in chat. Version models, manage keys, and go live in minutes.

Usage

Track requests, latency, and success rates by model and workspace. Get alerts and drill into traces to fix issues fast.

Spending

See total spend, tokens, and compute by project and model. Set budgets and alerts with workspace, endpoint, and time breakdowns.

Engineered from the ground up

Use your model or ours

Import custom checkpoints or start from our

curated library. Create versioned endpoints with

keys, access controls, and rollout policies in

minutes.

Import custom checkpoints or start from our

curated library. Create versioned endpoints with keys, access controls, and rollout policies in minutes.

Import custom checkpoints or start from our curated library. Create versioned endpoints with keys, access controls, and rollout policies in minutes.

fine-tuned model

53 tokens used

Optimize

Our proprietary compiler fuses graphs,

auto-tunes kernels, and plans memory for your

target hardware—cutting latency and cost.

Add performance-aware compression

(quantization, pruning) for even higher

throughput.

Our proprietary compiler fuses graphs, auto-tunes kernels, and plans memory for your target hardware—cutting latency and cost. Add performance-aware compression (quantization, pruning) for even higher throughput.

Autoscaled compute &
Batched inference

Maximize throughput with dynamic batching

that groups similar requests without spiking

latency. Serve millions of tokens per minute

while keeping costs predictable.

Maximize throughput with dynamic batching that groups similar requests without spiking latency. Serve millions of tokens per minute while keeping costs predictable.

Scheduled Inference

Scheduled Inference

Unified Observability

Integrated logging and full visibility into every

function, container, and workload. Correlate

metrics and traces to pinpoint issues fast.

Integrated logging and full visibility into every function, container, and workload. Correlate metrics and traces to pinpoint issues fast.

Live Usage

Live Usage

Time

Time

05:12am

05:12am

Containers

Containers

4

4

GPU Utilization

GPU Utilization

37%

37%

H100s

H100s

1028 GPUs

1028 GPUs

Connect to MLOps tools

Mount your cloud buckets, plug into CI/CD,

and stream telemetry to your observability

stack— without changing your workflow.

Mount your cloud buckets, plug into CI/CD, and stream telemetry to your observability stack— without changing your workflow.

import { infer } from '@inceptron/client';
import { readBucket, writeBucket } from '@cloud/storage';

const input = await readBucket('s3://customer-input/prompts.json');

const output = await infer({
  model: 'inceptron-hosted-model',
  input
});

await writeBucket('s3://customer-output/results.json', output);


import { infer } from '@inceptron/client';
import { readBucket, writeBucket } from '@cloud/storage';

const input = await readBucket('s3://customer-input/prompts.json');

const output = await infer({
  model: 'inceptron-hosted-model',
  input
});

await writeBucket('s3://customer-output/results.json', output);


import { infer } from '@inceptron/client';
import { readBucket, writeBucket } from '@cloud/storage';

const input = await readBucket('s3://customer-input/prompts.json');

const output = await infer({
  model: 'inceptron-hosted-model',
  input
});

await writeBucket('s3://customer-output/results.json', output);

Optimization

Compiler-driven performance

Auto-tuned kernels, graph fusion, and compression for lower latency and cost.

Agentic tuning

Finding the most efficient implementations for the algorithms needed to run inference is a hard problem, that depends not only on the model, but also on the hardware on which it runs. Inceptron leverages a combination of ML agents and Bayesian optimization to search for optimal solutions, a technique also known as auto-tuning. By aggregating and storing the results of the tuning, in databases and as model weights, Inceptron continuously improves the tuning efficiency.

Memory optimizations

Hardware-aware compilation

Graph level optimizations

Model compression

Agentic tuning

Finding the most efficient implementations for the algorithms needed to run inference is a hard problem, that depends not only on the model, but also on the hardware on which it runs. Inceptron leverages a combination of ML agents and Bayesian optimization to search for optimal solutions, a technique also known as auto-tuning. By aggregating and storing the results of the tuning, in databases and as model weights, Inceptron continuously improves the tuning efficiency.

Memory optimizations

Hardware-aware compilation

Graph level optimizations

Model compression

Agentic tuning

Finding the most efficient implementations for the algorithms needed to run inference is a hard problem, that depends not only on the model, but also on the hardware on which it runs. Inceptron leverages a combination of ML agents and Bayesian optimization to search for optimal solutions, a technique also known as auto-tuning. By aggregating and storing the results of the tuning, in databases and as model weights, Inceptron continuously improves the tuning efficiency.

Memory optimizations

Hardware-aware compilation

Graph level optimizations

Model compression

Enterprise-ready security

Your models. Your data. Fully protected.

Team controls

Team controls

Hardned isolation

Hardned isolation

ISO & GDPR

ISO & GDPR

Data residency controls

Data residency controls

Learn more about Inceptron

See all

See all

Green Fern
Green Fern

Aug 28, 2025

From Graph to GPU

Yellow Flower
Yellow Flower

Jul 23, 2025

Batched Inference

Orange Flower
Orange Flower

Jun 12, 2025

Compliance-First AI Serving

Run any model on the fastest endpoints

Use our API to deploy any model on one of the most cost-efficient inference stacks available.

Scale seamlessly to a dedicated deployment at any time for optimal throughput.



Curl

Python

JavaScript

curl https://api.inceptron.io/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $INCEPTRON_API_KEY" \
-d '{
  "model": "meta-llama/Llama-3.3-70B-Instruct",
  "messages": [
    {
      "role": "user",
      "content": "How many moons are there in the Solar System?"
    }
  ]
}'

Curl

Python

JavaScript

curl https://api.inceptron.io/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $INCEPTRON_API_KEY" \
-d '{
  "model": "meta-llama/Llama-3.3-70B-Instruct",
  "messages": [
    {
      "role": "user",
      "content": "How many moons are there in the Solar System?"
    }
  ]
}'

Curl

Python

JavaScript

curl https://api.inceptron.io/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $INCEPTRON_API_KEY" \
-d '{
  "model": "meta-llama/Llama-3.3-70B-Instruct",
  "messages": [
    {
      "role": "user",
      "content": "How many moons are there in the Solar System?"
    }
  ]
}'

Start building today

Get Started

Get Started