Why is Monday.com so slow for large boards with 500+ tasks?

Let's be direct: Monday.com's performance degrades exponentially with board size, not linearly. I've consulted on three separate migrations off the platform in the last year, all triggered by the same pattern: teams start with a manageable 50-100 items per board, scale past 300, and hit a concrete wall around 500-700 tasks. The interface becomes unusably sluggish, updates take 5-10 seconds to commit, and filtering or sorting feels like querying a cold data warehouse without an index.

The core architectural issue, from a data engineering perspective, is that Monday.com appears to treat large boards as single, monolithic documents. Every view, filter, or group-by operation likely triggers a client-side re-render of the entire dataset, as their backend delivers a massive JSON payload. There's no sign of efficient server-side pagination or incremental loading for operational workflows. When you have 500+ items with complex dependencies, multiple person columns, and custom fields, you're pushing a 2-3MB payload on every interaction. The browser's main thread locks up processing it.

Consider the data model implications for a simple filter operation:
* **Inefficient:** `SELECT * FROM giant_monolithic_board_json WHERE status_column = 'In Progress'` (executed client-side after full fetch)
* **What it should resemble:** `SELECT id, name, status FROM tasks WHERE board_id = X AND status = 'In Progress' LIMIT 50 OFFSET 0` (executed server-side with indexed columns)

We attempted every documented mitigation:
* Archiving old items (breaks historical reporting).
* Reducing columns (impractical for mature projects).
* Using separate "Overview" and "Detail" boards with syncing (adds complexity and latency).

None restored baseline performance. The final proof was instrumenting network requests. A board with 620 tasks generated a single `boards.json` response of 1.8MB, containing the entire state tree. Changing a single dropdown triggered a full PUT of nearly the same size.

We migrated to a self-hosted toolset (Apache Airflow for orchestration, PostgreSQL for state, a lightweight React frontend). The performance difference is measured in orders of magnitude. Filtering 10,000 tasks now takes ~120ms via a proper indexed query.

Has anyone else conducted a forensic analysis on Monday.com's performance ceiling? What were your observed breakpoints, and what migration paths did you evaluate for preserving board history and user permissions during cutover? I'm particularly interested in the ETL process for extracting the board schema and item history, as their API rate limits become a significant bottleneck for large datasets.

—davidr