Frugal is an application cost engineering platform that links source code, cloud usage, and observability data to attribute cloud spend to specific code components. Using AI, it automatically detects inefficient patterns across AWS, GCP, Azure and major third‑party services and generates ready‑to‑merge code fixes that reduce waste in storage, logging, AI token usage, and other managed services. The solution runs in a secure, read‑only environment with optional private deployment, ensuring data protection while delivering actionable cost‑saving recommendations.
Funding
Funding not disclosed
Founders
Product
Problem
Developers and engineering teams often incur hidden cloud expenses because traditional cost optimization focuses on right‑sizing infrastructure rather than the application code and managed services that actually drive spend. This leads to wasted budgets, especially as AI and serverless services become a larger portion of cloud usage.
Solution
Frugal provides an application cost engineering platform that connects source code, cloud usage, and observability data to attribute spend directly to the code components that generate it. Using AI, the platform automatically identifies inefficient patterns—such as over‑provisioned logs, excessive AI token usage, or suboptimal storage formats—and generates ready‑to‑merge code fixes. The analysis runs in a read‑only, secure environment with encrypted data transit and at‑rest, and can be deployed privately within a customer’s cloud account. By surfacing cost hotspots early in the development lifecycle and delivering actionable patches, Frugal enables teams to reduce cloud bills without slowing feature delivery.
Target Audience
Primary users are software engineers, DevOps, and FinOps teams building cloud‑native or AI‑enabled applications who need to control operational spend while maintaining development velocity.
Features
- Automatic mapping of cloud spend to specific source files, functions, and managed‑service configurations across AWS, GCP, Azure, and major third‑party platforms
- AI‑driven recommendations that produce merge‑ready code changes to eliminate waste (e.g., prompt caching for AI APIs, log level adjustments, storage format changes)
- Support for a wide range of services including storage, logging/metrics, AI APIs, messaging, serverless functions, and databases
- Secure, read‑only access model with TLS 1.2+ and AES‑256 encryption, least‑privilege permissions, and optional private‑deployment inside the customer’s cloud
- Integration hooks for IDEs, GitHub, and AI coding assistants (e.g., Claude Code, Cursor) to deliver in‑context cost guidance during development
- Cost‑impact analysis dashboards that quantify projected savings for each recommended fix