Lark vs Hex. The analyst your whole team uses.
Hex waits for the question. Lark already answered it.
Every other tool on this page is reactive: someone has to know a question is worth asking, then go ask it. Lark watches the metrics and accounts that matter to each person and posts the issue, the fix, and the opportunity before anyone thinks to look.
Hex is a place you go to find things out. Someone has to suspect a number moved, open a notebook, write the query, and read the result. Nothing surfaces until a person starts looking.
Lark is already looking. It watches your pipeline, revenue, and product metrics around the clock and posts the anomaly, the likely cause, and the fix in your channel, with the SQL attached, before anyone opens a notebook.
- You want a polished notebook environment for SQL + Python data science.
- You are building published, parameterized data apps the analyst team owns.
- Branching, version history, and code review on the analysis itself matter.
- The team is already deep in Hex’s notebook surface.
- You want an analyst that watches your metrics around the clock and posts the moment something breaks, before anyone has to ask. Hex waits for you to open a notebook.
- Most questions span more than just the warehouse. You need CRM, billing, product analytics, and uploaded docs joined in one answer.
- Slack is where the business actually asks questions, and you want a Slack-native experience, not a notification surface bolted onto a notebook.
- You want benchmark-verified accuracy on real schemas: 75.2% on BIRD-Interact for Lark, ~44% for Hex Magic.
What each tool actually does, side by side.
Green check = first-class feature. Orange dash = partial / possible with effort. Gray X = not the job this tool is built for.
| Feature | Lark | Hex |
|---|---|---|
Wide integration catalog (warehouse + CRM + billing + product + docs + more) Snowflake, BigQuery, Salesforce, HubSpot, Stripe, Segment, Amplitude, Notion, plus the long tail joined at query time. | ||
Proactive metric monitoring Lark watches a metric continuously and posts in the channel when it breaks. Hex does not ship this. | ||
Slack-native experience Hex now supports Slack. Lark is built Slack-first, ground up, not as a notification surface. | ||
Plain-English questions for non-technical users Hex has moved toward this with Magic. Lark was built for it from day one. | ||
Returns the SQL and source rows with every answer Every Lark reply is auditable. The analyst can verify it, and the next person can build on it. | ||
Cites uploaded docs and metric definitions inline Upload your "what counts as MRR" doc. Lark retrieves the right one per question and cites it in the answer. | ||
Collaborative Python + SQL notebooks Cell-based exploration, branching, version history. Hex’s core surface. | ||
Published parameterized data apps Hex’s app builder is still the strongest option for that artifact. | ||
Audited on a public text-to-SQL benchmark BIRD-Interact (ICLR 2026). See accuracy section below. |
Lark scores 75% on BIRD-Interact. Hex Magic lands around 44%.
BIRD-Interact is the University of Hong Kong + Google Cloud benchmark of 600 deliberately ambiguous business questions against 22 ugly real-world Postgres schemas, where a question like “find underperforming assets” has no matching column. Claude Opus 4.6, the underlying frontier model, scores 33% on its own. Hex’s Magic AI lands around 44%, a real lift over the bare model, which is what wrapping a frontier LLM in a thin retrieval layer typically buys you. Lark scores 75.2% on top of the same model. The gap is grounding: schema retrieval, metric definitions, and clarification before generating SQL.
Read the benchmark write-upHex is excellent at notebooks. Lark is the analyst the team relies on.
Hex remains best-in-class for analyst-driven notebook work and published data apps. The reason Lark wins for most buyers: the bottleneck on real analytics teams is not "we need a better notebook." It is "we need to join the warehouse with the CRM and a doc, in Slack, with proactive monitoring on the key metrics, and we need to trust the SQL." That is the job Lark is built for.
A user opens a notebook (or now, asks in Slack), writes SQL or Python, iterates on a chart, and optionally publishes the result as a parameterized app. The output is durable and versioned, which is Hex’s real strength.
Someone in #revenue-ops asks "which expansion accounts need exec attention today?" Lark joins Salesforce, the warehouse, and your usage events, posts a sourced answer with the SQL attached. Separately, Lark is already watching your top metrics; if any of them break overnight, the channel sees it before anyone has to ask. Action gets taken without a ticket and without a notebook getting opened.
Questions buyers ask us about Hex.
Hex has Slack now. Why pick Lark?
Hex’s Slack support is recent and reads more as a notification surface than the primary interface. The deeper differences are integration breadth (Lark joins the warehouse with CRM, billing, product analytics, and uploaded docs in one query), proactive metric monitoring (Lark pings when a metric breaks rather than waiting to be asked), and accuracy on real schemas: 75.2% on BIRD-Interact for Lark vs Hex Magic around 44%.
What does "proactive monitoring" actually do?
You point Lark at a metric: pipeline coverage, MRR, refund rate, anything from your warehouse and business systems. Lark watches it continuously. When the metric breaks a threshold or moves anomalously, Lark investigates and posts the root cause in the channel with the SQL attached. No one has to remember to check the dashboard. Hex does not ship this.
How are the integrations different?
Hex centers on the warehouse plus a handful of native connectors. Lark is built around joining the warehouse with the rest of the business stack — CRM, billing, product analytics, ticketing, docs — in a single query, on top of an integrations catalog with much wider coverage. If a real question requires joining Snowflake usage with Salesforce pipeline and a Notion runbook, that is one prompt in Lark.
Can I ask Lark the same kinds of questions I would ask Hex Magic?
Yes, and Lark is noticeably more accurate on messy real schemas. Lark scores 75.2% on BIRD-Interact; Hex Magic lands around 44%. For multi-step exploratory data science where you want to branch, plot, and iterate inside a notebook, Hex is still the right tool.
What about price?
Hex has a free Community tier capped at 5 notebooks with small compute, plus self-serve paid plans ($36/editor for Professional, $75/editor for Team) and Enterprise (sales). Lark is sales-led, priced for broader org access since the whole team is the user.
30 minutes. Bring a real question your team has been waiting on, and watch Lark surface three you hadn’t thought to ask.