One install to make every AI coding tool work with any LLM provider

Developers and vendor teams building AI coding tools today face a fragmented integration problem: each tool must separately support many LLM providers, which multiplies engineering and ops costs across IDE plugins, CI pipelines, and SaaS copilots. Across an addressable market of roughly 25 million developers and an estimated $15.0B in annual developer-facing AI tooling spend ($600 ARR per developer), this duplication materially slows product iteration and increases support overhead. You could build a "one install" abstraction — an SDK plus lightweight runtime/agent that exposes a stable, code-oriented API to tools while providing pluggable adapters for LLM providers, cost-aware routing, local hosting fallbacks, telemetry, and billing/key management. Distributed as an npm package, CLI and a VS Code extension, the product would let a single integration point map to many providers and enable tool vendors to add new models without changing their product code. Timing is favorable: rapid LLM proliferation and cheaper open-source models make switching and multi-provider strategies common, and developer-first distribution paths mean an SDK that saves integration effort can spread quickly. The market and revenue signals are strong (Market Score 94/100, Revenue Potential 88/100), so a successful technical integration product can capture platform fees and enterprise subscriptions in a $15B market. To stand out you must be obsessively developer-friendly (one-line install, solid docs), ship prebuilt adapters for the top ~10 providers, and offer enterprise-grade privacy, low-latency routing and certified performance, but be honest that sustaining dozens of adapters, handling secrets/compliance, and competing with open-source frameworks and vendor SDKs (competition = medium) will be ongoing challenges.