LLM Inference Gateway

Unified Gateway for Multi-Provider LLM and On-Premise AI Inference

30% Faster Deployment Cycles
6+ Inference Backends
3x GPU Utilisation Gain
LLM Gateway
On-Prem Inference
Cloud Providers
GPU Infrastructure

Project Overview

Problem Statement

Enterprise teams faced fragmented LLM integrations — each team building bespoke clients for OpenAI, Anthropic, or self-hosted models separately. This created duplicated security controls, inconsistent rate limiting, zero cross-provider observability, and costly GPU underutilisation.

Solution

Built a unified LLM Inference Gateway that abstracts all model providers — cloud (Anthropic Claude, OpenAI GPT) and on-premise (Ollama, vLLM, LoRAX for LoRA adapter serving, KServe with ONNX Runtime for in-house model inference) — behind a single OpenAI-compatible API surface.

GPU infrastructure is centrally managed using NVIDIA MIG (Multi-Instance GPU) partitioning and time-slicing, enabling multiple workloads to share expensive GPU capacity efficiently without contention.

Key Capabilities

Multi-Provider Routing

Policy-based routing to Anthropic, OpenAI, Ollama, vLLM, LoRAX, and KServe by cost, latency, or model availability.

Dynamic failover Cost optimization

GPU Infrastructure Management

NVIDIA MIG partitioning and time-slicing for efficient GPU sharing across inference workloads.

MIG partitions Time-slicing

Centralised Security

Single control point for API key management, rate limiting, audit logging, and PII detection before prompts reach providers.

PII scanning Audit logs

Unified Observability

Latency, token usage, cost attribution, and error rates across all providers in a single dashboard.

Per-model costs SLO tracking