Weflayr
Weflayr is an AI gateway that routes each inference request to the model that performs best on a company's own workloads, based on benchmarks against replayed production calls. It lets customers define routing rules tied to business context—such as customer plan, country, or AI margin—to balance quality and cost. The platform includes a single API endpoint, automatic fallbacks, and observability dashboards tracking cost and margin per customer and feature.
- Artificial Intelligence
- Developer Tools
- Software Only
Funding
Raised to date
$2.5MRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
across 1 round
RVSF$2.5M · Pre-Seed
Announced Oct 2026
Disclosed cumulative: $2.5M
Founders
Founder details are not available yet.
Product
Problem
Hundreds of AI models now compete on quality, speed, and cost, yet most AI companies rely on just a handful of models because quality is non-negotiable. Traditional AI gateways can route requests between models, but without understanding how those models actually perform on a company's own workloads, routing decisions remain largely blind, forcing companies to choose between quality and cost.
Solution
Weflayr is an AI gateway that routes every request to the right model based on benchmarks validated against a company's own data. The platform benchmarks candidate models against replayed production calls, then dynamically matches each incoming request to similar previously benchmarked calls and selects the best-performing approved model within the customer's defined boundaries. Customers define routing strategies that incorporate business context such as customer plan, country, and AI margin, with rules like "same quality, best cost" or "best quality if high margin." The gateway handles fallbacks, rate limits, and provider selection automatically, while observability features track cost and margin per customer, feature, and workflow.
Target Audience
Primary customers are AI-native businesses and software companies that need to optimize inference costs and quality across multiple models, including those with high-volume production workloads and complex customer segmentation.
Features
- Benchmarking engine that replays 500 production calls per model against a company's own prompts, using automated judges to compare quality and cost
- Routing engine that matches each request to similar benchmarked calls and selects the best-performing approved model based on the customer's chosen quality/cost trade-off
- Custom routing rules tied to business context including customer plan, enterprise tier, country, and AI margin thresholds
- Single API endpoint supporting multiple AI providers with BYOK or managed keys, automatic fallbacks, and rate-limit handling
- Observability dashboard showing cost, usage, and profitability per customer, feature, and model, with optional integration of revenue data from billing providers
- Content logging on by default for benchmarking and routing, with the ability to disable per API key; all data stored in the EU