Inceptron provides a unified inference platform that compiles AI model graphs into optimized GPU binaries with automatic operator fusion and hardware‑aware code generation. The managed runtime offers serverless, autoscaling GPU replicas across multiple clouds, integrated MLOps hooks, and built‑in observability and security controls, enabling low‑latency, cost‑effective production inference. Usage is billed per token for serverless deployments or hourly for dedicated GPUs.
Funding
Funding not disclosed
Founders
Product
Problem
Deploying AI models at production scale often incurs high latency, unpredictable costs, and operational complexity. Engineers must manually tune kernels, manage GPU capacity across clouds, and integrate inference into existing MLOps pipelines, which slows time‑to‑value and limits reliability.
Solution
Inceptron delivers a unified inference platform that combines a proprietary compilation engine with managed runtime services. The compiler automatically fuses model graphs, auto‑tunes kernels, and performs hardware‑aware code generation, reducing latency and compute cost without hand‑written kernels. Optimized binaries run on pre‑warmed, autoscaling GPU replicas that can be provisioned serverlessly (pay‑per‑token) or on dedicated hardware (hourly rates). Multi‑cloud placement ensures capacity where GPUs are available, while built‑in observability, usage analytics, and budget alerts keep operations transparent. Integrated MLOps hooks (CI/CD, cloud‑bucket mounting) let teams deploy and version models through a simple API, preserving security and compliance with ISO‑27001/GDPR controls.
Target Audience
The platform targets ML engineers, data‑science teams, and DevOps groups building production AI services, as well as enterprises and SaaS providers that require scalable, low‑latency inference across multiple cloud environments.
Features
- Proprietary compiler that performs graph‑level IR normalization, operator fusion, and memory planning for target GPUs.
- Agentic auto‑tuning using Bayesian optimization to select optimal tile sizes, warps, and unroll factors, cached per‑op for faster recompiles.
- Hardware‑aware codegen targeting modern GPUs (including Blackwell‑ready tensor cores) with optional fp8 quantization and pruning.
- Dynamic batching engine with configurable windows and priority lanes to maximize tokens‑per‑second while respecting p95 latency budgets.
- Pre‑warmed replica pool and autoscaling controller that scales to zero when idle and fails over across cloud providers automatically.
- Unified API for model endpoint creation, versioning, key‑based access control, and rollout policies.
- Integrated observability dashboard reporting request latency, success rates, token usage, and spend; supports alerts and budget caps.
- Enterprise‑grade security: SSO (SAML/OIDC), RBAC, encrypted data in transit and at rest, region‑locked workloads, and audit‑trail logging.