Report · david.lequin.dev

Can we replace a $150k Looker with a bucket of HTML files?

david.lequin.dev

Source published October 23, 2026

Summary: 2 min read

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53/100

Summary

A three-engineer data platform team at a roughly 500-person company built an internal AI-generated dashboard service that hosts HTML files, runs live Snowflake queries, and has reached at least as many weekly users as the company’s Looker deployment. The company pays about $150,000 annually for roughly 300 Looker seats, while the internal service grew from 23 weekly viewers in March to 136 in June. Users generate free-form dashboard pages with Claude, publish them through an internal connector, and share them behind single sign-on. The service initially exposed serious security risks because unreviewed JavaScript ran on the same origin as user sessions and every employee could access every dashboard. The team later added a remote MCP connector, draft-and-publish workflow, sandboxed iframes on separate origins, strict content-security policies, one-hour viewer-specific content tokens, and metadata scanning that can flag or block risky scripts and WebRTC connections. Snowflake OAuth supplies each viewer’s identity and data permissions, so sharing a dashboard does not grant access to underlying data. The team reports 325 commits, about 74,000 lines of code including 20,000 test lines, and roughly $6,400 in Claude token costs over eight months. It estimates annual operating costs at about $20,000, including AWS and ongoing Claude use. Its analyst agent uses about 1 million tokens per dashboard, with reported costs falling from about $2.50 to $0.50 after moving to Opus 5.5.

Key Takeaways

An internal HTML dashboard platform reached at least Looker-level weekly usage at a company with about 500 employees and a $150,000 annual Looker contract.
Snowflake OAuth and existing Snowflake roles allow dashboards to execute queries using each viewer’s own permissions rather than a separate dashboard permission model.
The platform treats uploaded dashboards as hostile code by isolating them in sandboxed iframes and scanning content for risky scripts, destinations, and WebRTC connections.
The team estimates that running and extending the platform costs about $20,000 annually, compared with approximately $150,000 for Looker.
The company’s analyst agent reportedly creates a dashboard for about $0.50 in tokens after its move to Opus 5.5.

What the discussion said

The thread largely accepts the core economic shift: generative AI can now turn a request and a query into a polished HTML dashboard cheaply enough that paying six figures for a visualization layer looks increasingly optional. Several practitioners report real migrations from Looker or other SaaS stacks, using Claude or Codex to recreate dashboards in weeks or months and reducing a formerly expensive product to a version-controlled web app. AI-assisted D3 and unrestricted HTML/SVG are especially appealing because they remove the specialist front-end skills once needed for custom visual work. But commenters repeatedly argue that charts were never the difficult part of business intelligence. The real work is deciding which metrics mean what, modeling messy source data, enforcing permissions, and preventing subtly wrong dashboards from becoming organizational fact. A language model can rapidly produce a persuasive visualization from an ambiguous prompt, which makes weak data governance more dangerous rather than less. Readers also push back on savings claims that count token costs but omit engineers’ time to build, validate, and maintain replacements. The emerging consensus is not that AI eliminates BI, but that it commoditizes the presentation layer while raising the value of semantic models, ETL discipline, provenance, guardrails, and analysts who can certify the numbers.

Where opinion split

The central fight is whether AI-generated HTML can genuinely replace an enterprise BI product or merely replace its least valuable surface layer. Optimists point to successful Looker exits, dramatic licensing savings, and the freedom for anyone to request a tailored dashboard; skeptics argue that governed definitions, access control, data quality, and trustworthy metrics—not chart rendering—are what companies were paying for.

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