Bigeye alternatives for teams watching one production database, not a warehouse estate

Our Dagster instance broke while our founder was on holiday, and nothing told anyone for a day. The app stayed up, the dashboards kept loading, and every check we had lived inside the system that had stopped — so when it stopped, they went quiet with it. A user noticed before we did. That's the outage that led us to evaluate the enterprise data observability platforms, Bigeye among them, and it's why most people land on this page: a call that started promising and turned into scheduling a sales conversation for a warehouse estate you don't actually have.

Bigeye is shaped like Monte Carlo, and it's sold the same way — automated anomaly detection and lineage across your tables, quote-based pricing, aimed up-market at data organizations with a budget line for observability tooling. For a team of two to twenty engineers with one production Postgres or ClickHouse database and a handful of outcomes you already know should hold, that's a lot of platform, and a lot of sales process, for a job that's smaller than the category implies.

Alertee is one of the alternatives below and it's ours. The rest is an honest read on the tools we evaluated against the same problem.

Where Bigeye is the right answer

Bigeye is the right tool when you have a warehouse estate too large to hand-write checks for and a budget for enterprise data tooling — hundreds or thousands of tables in a cloud warehouse, automated metrics, anomaly detection, and lineage doing work nobody is going to do by hand at that scale. If that's your actual shape, Bigeye (evaluated in the same bracket as Monte Carlo) is the right aisle, and the sales process and quote-based pricing are the cost of that automation, not a red flag. This page is for the other case: when you priced that automation against "alert me when the import stops writing rows," and the two didn't match.

The alternatives, ranked by what your data needs

We're ranking by the job, not by team size — a two-person team taking payments through Postgres has more riding on its data than a fifty-person team with one internal dashboard.

1. Alertee — SQL checks on the production database itself

This is the opposite starting point from Bigeye's. Instead of a platform that learns your warehouse tables, Alertee runs the SQL checks you already know you need, against the production Postgres or ClickHouse database your application writes to directly — not a downstream warehouse.

You get a check two ways. Write it yourself — plain SQL plus a condition, and Alertee runs it exactly as written, never editing it. Or describe what should be true in plain English and let Alertee's agent inspect your schema, draft a baseline-aware query and threshold, and keep it tuned on a review cadence; the SQL stays visible and editable, and you can take a managed check back to manual control at any time.

-- Did today's shipment export actually write rows?
SELECT COUNT(*) AS rows_today
FROM daily_shipments_export
WHERE loaded_at >= CURRENT_DATE;
-- Alert if 0 after 07:00

For the failures a single number papers over — one region, store, or tenant going quiet while the total looks normal — a check can watch many dimensions at once instead of one. Return name/value rows and pick a condition that evaluates every row:

SELECT region AS name, COUNT(*) AS value
FROM orders
WHERE created_at > NOW() - INTERVAL '15 minutes'
GROUP BY region
-- Condition: every dimension must clear greater_than 0

What replaces Bigeye's lineage graph and anomaly model: nothing, deliberately. Every check — hand-written or agent-authored — is a query you review before it ever runs, and read when it fires. Nothing runs until you enable it.

What replaces the incident workflow you'd otherwise have to build around a warehouse platform's alerts: a failing check opens an incident in the Inbox, routed to an alert group of your choosing — email, Slack, or webhook. Resolving it means classifying the outcome as real, transient, expected, or noisy, which feeds back into tuning the check instead of training the team to skim past a channel.

Connections are read-only, and credentials are encrypted per organization — Alertee can't write to your database. There's a CLI and an MCP server, so checks and alert routing can be driven from a terminal or from inside an AI coding session, not just the dashboard.

What it deliberately doesn't do: no lineage graph, no anomaly model trained across a warehouse, and it connects to Postgres and ClickHouse — not Snowflake, BigQuery, or Redshift. If that's the shape of your problem, Bigeye or Monte Carlo is the right aisle, not this page.

2. Metaplane — the lighter automated option, if you want to stay in that lane

If what actually drew you to Bigeye is the automated, learn-your-tables approach rather than the enterprise sales process, Metaplane — now "Metaplane by Datadog" after a 2025 acquisition — is a lighter version of the same idea. It connects in minutes, applies anomaly detection to row counts, freshness, and distributions, and includes column-level lineage. Its center of gravity is the warehouse stack: Snowflake, BigQuery, Redshift, Databricks. Postgres connections exist but aren't the focus, and it doesn't reach ClickHouse.

One thing worth knowing before a trial: a learned monitor needs history before it can call anything abnormal — Metaplane's own site says alerts arrive "in as soon as 3 days." A SQL check you write yourself fires on its first run, because it's asserting a rule you already know rather than waiting to learn what's normal.

3. A cron job and a handful of SQL scripts

The honest zero-cost option, and where most teams reading this already are: a script on a cron that runs a query and posts to Slack when the number looks wrong. It's free and it's entirely yours, and for the first handful of checks it's the right call. What erodes is everything around the query — checks scatter across repos until nobody can list what's monitored, there's no history of past failures, and the alert lands in a channel that belongs to everyone, which means no one. That's the gap Alertee closes, without asking you to adopt a warehouse observability platform to close it.

Bigeye vs Alertee, side by side

How to choose

Finish one sentence and the choice mostly makes itself. If it's "we have hundreds of tables across a warehouse and nobody's going to hand-write a check for each one" — that's Bigeye's ground, in the same bracket as Monte Carlo, and the quote-based pricing buys real automation at that scale. If it's "I want the automated, learn-your-tables approach without an enterprise sales process" — trial Metaplane, especially if Datadog already runs your infrastructure monitoring. And if it's "I already know the handful of outcomes that need to hold on my production database — orders keep arriving, payments keep recording, no region's gone quiet" — that's what we built Alertee for: connect a database and turn one check on, free to start, no sales call.

For the full field — cron scripts through every platform on this page — the data quality tools comparison scores eight of them side by side on cost growth, portability, and whether you can read the query behind an alert. And if your actual shortlist is Monte Carlo itself rather than Bigeye, the Monte Carlo alternatives post goes deeper on that specific evaluation. For what continuous, SQL-based monitoring actually looks like once it's running, the continuous monitoring walkthrough covers where to put checks and how to read the queries behind them.