Problem

Wafer addresses the gap between the theoretical capability of AI hardware and the performance companies actually obtain from it. AI inference is often run on stock serving software such as SGLang or vLLM, leaving an estimated 50–80% of GPU performance unused. Closing that gap normally requires scarce performance engineers to profile workloads, inspect traces, rewrite kernels, and tune scheduling and memory behavior for each model and chip. The problem becomes more acute as new NVIDIA, AMD, TPU, and other architectures arrive faster than compilers and kernel libraries can be optimized for them.

The economics are particularly painful for companies operating large or latency-sensitive models: they pay for expensive accelerators while underusing capacity, and slow token generation makes interactive AI agents less effective. Wafer’s clearest use case is serving large open-source models inside agentic coding tools, where users face throttling, unpredictable per-token bills, and latency that compounds across an agent loop. The company has also framed the automation opportunity economically: a manual optimization might save $10,000 per month but require 40 hours of a $400-per-hour specialist’s time, whereas an AI agent could potentially find the optimization for roughly $600 in compute.

Product / Service

Wafer builds autonomous AI performance engineers. A customer points Wafer at an LLM or an existing inference workload, and its agents profile the workload, search across model, engine, kernel, and hardware combinations, then rewrite and validate the serving stack. The system can tune kernels, batching, scheduling, and memory layout, measuring candidate changes rather than relying on generated code that merely appears optimized. Wafer then ships the measured winner for the target environment.

The company delivers the resulting capability in two ways: serverless inference APIs for open models, with no infrastructure or deployment overhead, and dedicated endpoints for mission-critical workloads requiring predictable uptime and workload-specific optimization. Its APIs are designed to be compatible with existing AI tooling, including OpenAI-style integrations, while its routing layer can select among NVIDIA, AMD, TPUs, and other silicon. Wafer reports speedups such as 2.8x for Qwen3.5-397B-Turbo versus base SGLang and 2x for GLM5.1-Turbo and DeepSeek-V4-Pro versus vLLM baselines. It also offers Wafer Pass, a flat-rate subscription beginning at $10 per week, alongside usage-priced model APIs and an enterprise path for custom deployments.

Market

Wafer competes in AI inference infrastructure, specifically the performance-optimization and serving layer for open-weight large language models. Its alternatives include inference API and hosting providers such as Fireworks, Together AI, FriendliAI, CoreWeave, Nebius, and Eigen AI, as well as teams that operate their own optimized serving stacks. Wafer’s differentiation is not a proprietary foundation model; it is the combination of an autonomous optimization agent, hardware-aware tuning, and an inference service that aims to deliver better speed-to-price performance across changing chips. Public benchmark comparisons position Wafer against those providers on output speed, latency, and price, though results vary by model and workload.

The company is early but commercially active rather than merely a research project. It was founded in 2025, joined Y Combinator’s Summer 2025 batch, and has launched serverless and dedicated inference products, paid API plans, and a subscription product for coding-agent users. Wafer announced a $4 million seed round led by Fifty Years in April 2026, and its inference service has been integrated with TrueFoundry’s AI Gateway for routing, observability, streaming, tool calling, and zero-data-retention workflows. Public materials reviewed here do not disclose revenue, customer counts, or a definitive profitability status, so it is more accurate to describe Wafer as an early commercial company with funding and launched products than to label it definitively pre-revenue.