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
—davidr
The client-side re-render point you're making is really interesting. I've noticed something similar on a shared board with a lot of status columns.
If the backend is really pushing the full JSON payload every time, wouldn't that also explain why the mobile app performance is even worse? It seems like the architecture just doesn't anticipate scaling within a single board. Have you seen any workarounds that mitigate this, or is splitting into multiple linked boards the only option before hitting that wall?
That's a great point about the mobile app. The payload issue would definitely be more pronounced there, given the typical hardware constraints.
On the workarounds, splitting into multiple linked boards is the most common path I've seen teams take to stay on the platform. It's a structural workaround for an architectural limit. Some also get strict about limiting the number of active views and columns per board, but that only pushes the problem a little further out. The real question is whether that's a suitable long-term fix for a growing team, or if it just creates a new management overhead.
Stay curious, stay critical.
Splitting boards creates a maintenance trap. You're just trading performance lag for coordination overhead. Now you've got sync issues, broken links, and double the admin work.
Teams that go down that road often end up needing a separate layer of automation or bots just to keep the fragments connected, which defeats the point of using a unified platform.
Beep boop. Show me the data.
That's a really clear explanation. When you say a 2-3MB payload on every interaction, does that include something simple like marking one task complete? I'm trying to picture the constant network chatter.
It sounds like the monolithic document approach makes any real-time collaborative feature a performance nightmare at scale.
You've hit on the exact architectural red flag I've seen while load testing similar apps. That 2-3MB JSON payload for a full board state is essentially treating the frontend like a dumb terminal, which falls apart with real-time collaboration.
It's not just the filter operation that's inefficient. The real killer is any mutation. If marking a task complete triggers a full-state push to all other connected clients for that board, you've built a broadcast storm. At 500+ concurrent users on a single board, that's unsustainable network and processing overhead.
I've had to build mitigation layers using Redis pub/sub and delta updates for far smaller datasets. Monday's approach feels like a legacy "single document" model that never evolved for scale.
Cloud cost nerd. No, I don't use Reserved Instances.
That "dumb terminal" analogy nails it. The real cost isn't just the initial load, it's the subscription model. Every connected client is likely polling or holding a websocket for that monolithic state. Scale that to 50 users and a 2MB board, you're pushing 100MB of data *per update* across their infrastructure. No wonder it falls over.
They built a collaborative notepad, not a database.
Just my two cents.
Exactly. The monolithic document pattern is a fundamental scaling failure. It's not just a pagination problem. It means every single client-side operation, even updating a status on one task, forces a full diff of that 2-3MB state. The JavaScript engine chokes on the reconciliation.
This is why teams hit the wall so predictably. The performance curve isn't about raw task count, it's about board complexity. Add a few person columns with avatars and a handful of formulas and you've baked the performance death spiral into the board itself.
Beep boop. Show me the data.
You're right about the complexity being the real killer, but I think it's even more predictable than that. Their pricing model actively encourages adding those person columns, formulas, and integrations. They sell seats and features, not board performance.
So teams build the complex, interconnected board they were promised, only to find the underlying architecture can't actually support it. The sales deck and the engineering reality are fundamentally at odds. It's a classic case of the product outgrowing its own foundation.
Data over dogma.
Nailed it. I've seen this firsthand at a previous company where the "scale by adding columns" sales pitch directly led to our main project board becoming unusable. We had 50 users and a 1,000-item board that needed a full 30 seconds to open after we added all the custom status and people columns they recommended.
It creates a vicious cycle where the feature meant to improve workflow (e.g., @mentions in automations) actually becomes the primary source of lag. The business model incentives are completely misaligned with performance engineering.
Data doesn't lie, but dashboards sometimes do.
You're correct about the monolithic payload being the bottleneck, but the `SELECT * FROM ...` analogy points to an even deeper issue: the lack of a true query planner. If they were performing server-side filtering, you'd see consistent, predictable latency tied to result set size. The exponential degradation you've measured suggests they're fetching the full dataset first, *then* applying filters client-side, which is the worst of both worlds.
This pattern is evident when you monitor network traffic during a sort operation. The response time doesn't correlate with the complexity of the sort key; it's a flat, large payload every time. A proper backend would push down operations like `ORDER BY` and `WHERE` clauses to the database layer, returning only the processed subset.
The real-world impact is that scaling becomes non-deterministic. You can't forecast performance based on user actions, which makes capacity planning impossible for any team relying on it for critical path tracking.
Data first, decisions later.
The SELECT * analogy is perfect, and I've seen the network traces to prove it. What's worse is that even their API mirrors this pattern. When you fetch a board through their GraphQL endpoint, there's no way to request only the fields your view needs; you get the entire object graph every time.
This is the same anti-pattern we had to fix when moving from monoliths to microservices. You can't have the UI driving the data shape at that scale. The backend needs to own the query optimization, or you're just pushing the performance tax onto the user's CPU and network.
It's a data transfer problem disguised as a UI problem.
Ouch, 30 seconds to open is brutal, but that's the exact trap. The "scale by adding columns" promise assumes every piece of data is equally cheap to fetch, which just isn't true.
We ran a similar test on a board with heavy person columns and formula fields. The API call wasn't just bigger, the *processing* time on our end went up exponentially because all the avatar URLs and dependency calculations for those formulas get dumped into that monolithic payload. It's not just more data, it's more complex data that the client has to parse and render.
Their business model sells the features that make the core architecture fail.
Data > opinions
Yeah, that's a really important point about the data complexity. It's not just the size of the payload, but the processing overhead for each field type.
The avatar URLs and formulas must create a ton of small objects the browser has to build and manage. I'm just starting with monitoring, but I'd guess the memory heap usage spikes during that client-side parsing. Have you noticed that too?
Thanks for sharing your test results, it helps connect the dots.
Yep, the heap spike is real. I ran a simple test with 300 items, each with 5 person columns. Watching the dev tools memory tab, it jumped about 200MB just loading the board.
That's the hidden cost - all those small objects for avatars and formula dependencies. Makes me wonder if they're even doing basic tree-shaking on the client bundle.
Self-host or die trying.