Bootstrapping an Autonomous AI Agent with Zero Budget: Lessons from Building Otobot

Building autonomous AI applications often feels like an enterprise-only domain requiring massive compute budgets and complex server arrays. However, modern open-source toolchains make it entirely possible to deploy resilient, LLM-driven web agents on minimal infrastructure.
To prove this, I built Otobot—an autonomous browser agent running completely on Node.js, Playwright, and lightweight cloud pipelines.
Core Architectural Choices for Zero-Budget Builds
Headless Context Reuse: Instead of spinning up heavy browser instances for every micro-action, Otobot reuses headless Playwright contexts to drastically reduce memory usage.
Streamlined DOM Pruning: Raw HTML bloat is filtered instantly before entering the LLM context window, saving token overhead and response latency.
Serverless Pipeline Management: Deployed on cloud-hosted free-tier instances (Render) with automated fallback routines to handle traffic spikes smoothly.
Test the Live Build & Product Hunt
You can interact with the autonomous execution loop directly in your browser:
👉 Try the Live Agent: https://oto-bot-vyaz.onrender.com/
We are also live on Product Hunt! If you enjoy lightweight AI tooling and open architecture, feel free to check out the launch:
👉 Product Hunt Launch Page: https://www.producthunt.com/products/otobot?launch=otobot
What zero-budget optimization strategies do you use when deploying resource-heavy LLM applications? Share your stack in the comments!