Alternatives

We built Sidequery because we kept running into the same problems with existing AI data tools: confusing pricing, locked-down runtimes, half-finished features, and platforms that look good in a demo but fall apart in daily use.

Here's what we found when we looked at the landscape.

TextQL

TextQL markets heavily around their "ontology," a semantic layer that maps business concepts to your database. Their own team acknowledges it covers roughly 80% of queries. The remaining 20%, usually the hardest and most important questions, fall through. Pricing uses "Agent Compute Units" where idle sandboxes burn compute for four hours after your last query, and heavy model usage adds up fast. Despite enterprise positioning, there's almost no public social proof: near-zero reviews on G2, nothing substantive on Reddit or HN. It's a 16-person team spreading across healthcare, finance, insurance, and retail simultaneously.

Julius AI

Julius AI connects to databases like Postgres, Snowflake, and BigQuery, but the product was built file-first and it shows. Features like workflows and notebooks exist but use a proprietary format with no Jupyter compatibility. You can't export a notebook as .ipynb or run it anywhere else: your work is locked inside Julius. There's no semantic layer, so the same question can produce different SQL against different columns on different days. Their Slack agent requires a Team plan at $70/month per member, so a 10-person team pays $700/month just to ask data questions in Slack. The free tier gives you 15 messages a month, barely enough to evaluate the product.

Camel AI

Camel AI is a YC-backed data analyst platform from a three-person team that recently pivoted from QA testing into analytics. It can answer one-off questions against your database, but there's no native way to schedule or rerun analyses from the UI. Every question is a fresh start. The free tier caps you at 10 messages per week, and with only two public reviews to its name, it's hard to know what you're getting.

Hex

Hex is a capable notebook, but it gets expensive and restrictive. Scheduled runs, a basic production feature, require the $75/seat/month Team plan. Per-minute compute charges stack on top, making costs hard to predict. The runtime locks you to old package versions: no Python 3.12 or 3.13, and DuckDB, numpy, pandas, and pyarrow are all pinned and can't be upgraded. Published apps re-execute every cell on each page view. Large notebooks slow to a crawl. Scheduled runs cap at 60 minutes.