Cloud & Infrastructure Software

Services that expose GPU and accelerator hardware through APIs and web consoles, eliminating direct infrastructure management. Enables AI developers to rent rather than own compute. Hyperscalers capture enterprise lock-in; neoclouds compete on speed and price.

Software that distributes GPU and CPU tasks across server clusters to keep hardware busy. Poor scheduling wastes expensive silicon; good scheduling recoups GPU investment faster. Switching schedulers is costly, so controlling this layer matters.

Methods to split GPUs across users or workloads through time-slicing, partitioning or full virtualization. Sharing hardware makes inference affordable; without it, GPUs sit idle between traffic spikes. NVIDIA vGPU licensing generates direct revenue.

Platforms that track experiments, version models, orchestrate pipelines and manage the ML lifecycle. They ease handoffs from research to production. The market is fragmented with no clear winner; most tools are open-source or bundled into clouds.

Software that loads trained models to answer requests at scale, balancing throughput, latency and memory pressure. Compute becomes sellable output here: KV-cache efficiency and disaggregated prefill/decode separate good systems from bad. NVIDIA Triton, vLLM and proprietary stacks compete.

Tools converting model weights and compute graphs into hardware-specific binaries through quantisation, kernel fusion, and compilation. Inference speed and cost hinge on this step; suboptimal compilation can waste 5-10x compute. NVIDIA's CUDA/CUTLASS stack dominates, creating a durable gatekeeper position.

Software systems that store, move, and serve data to training pipelines, retrieval-augmented generation, and inference features. Bottlenecks here stall GPU clusters waiting on data; throughput directly affects training cost and iteration speed. Value fragments across cloud providers and specialised vendors with no single dominant player.

Tooling for measuring GPU efficiency, model behaviour and spending across AI infrastructure. Without it, clusters run underutilised and costs inflate opaquely. The market is fragmented with emerging leaders in each subsegment; enterprise demand is rising as AI spend grows.

Software guarding AI systems from prompt injection, model theft, training data poisoning and supply-chain tampering. Exposure surfaces expand as LLMs access external tools and sensitive data. Specialist vendors including HiddenLayer gain early enterprise adoption before incumbent security platforms absorb functions.