OneGlanseOneGlanse is currently built and maintained by a solo developer. The project's time and resources go toward making the open-source product reliable and useful, rather than operating proxy fleets, browser infrastructure, and anti-bot systems for a hosted service.
UI collection is harder to scale than API collection
A typical AI monitoring service can send requests to a model API from a server. OneGlanse takes a different path: its Agent opens ChatGPT, Perplexity, Gemini, Claude, and Google AI Overview in a browser, submits prompts through each product interface, and captures the rendered response and available citations.
That measures the product surface people use, but a hosted service would have to operate many authenticated browsers while managing:
- Residential proxy capacity and IP reputation
- Provider bot detection and verification challenges
- Browser fingerprints
- Authenticated sessions and session expiry
- Provider rate limits
- Retries and failed browser runs
- UI changes that break automation
- Concurrent browser workers and their compute cost
The cost and operational work grow with the number of prompts, users, regions, and providers. For a solo-maintained project, operating that infrastructure would take substantial time away from improving the product.
Why proxies matter
Browser automation on cloud servers usually comes from datacenter IP ranges. AI product interfaces can challenge, rate-limit, or block that traffic more aggressively than residential traffic. OneGlanse's self-hosted mode supports routing provider traffic through a residential proxy.
Running this centrally for every user would mean paying for proxy capacity and managing IP reputation, geography, sessions, and provider-specific failures. Self-hosted users instead control the proxy and infrastructure for their workload.
Authenticated sessions add another constraint
The supported AI products use authenticated user sessions. In local mode, you sign in through a browser on your machine. In self-hosted mode, you can transfer saved sessions to your own Agent server.
A hosted OneGlanse service would also need to securely operate third-party account sessions for many users. Keeping the runtime under your control avoids making OneGlanse a centralized custodian of those sessions.
Why not just use model APIs?
Model APIs would simplify collection infrastructure, but they would change what OneGlanse measures. Product interfaces can add retrieval, citations, source cards, recommendation ordering, and formatting that differ from the underlying model API. OneGlanse exists to observe that product surface.
The same design choice that makes UI collection useful also makes centralized collection more expensive and difficult to operate reliably.
Could OneGlanse offer a hosted version later?
Yes. Operating this infrastructure centrally is possible. It would require solving the cost and reliability of browser automation, proxy management, bot detection, session handling, and provider-specific failures at scale.
As a solo developer, that is not where I want to spend the project's resources today. The priority is keeping OneGlanse free, open source, and focused on collection and analysis. For now, the application runs locally or on infrastructure you control. A hosted version can make sense if the project grows enough to justify operating collection centrally.