Lighthouse vs Soda

You need both. Lighthouse does both.

Q: Can Lighthouse actually do data quality monitoring?

Yes. Lighthouse runs any SQL query against your warehouse on a schedule and alerts when a threshold is crossed. That means freshness checks, null rate monitoring, volume anomalies, duplicate detection: all of it. You write the SQL once, Lighthouse monitors continuously.

Q: Where does Soda have an edge?

Soda's automated observability is more comprehensive out of the box: it can detect schema drift, column-level anomalies, and pipeline freshness across hundreds of tables with minimal configuration, and it supports more connectors. If zero-config data quality across your entire platform is the primary requirement, Soda has more coverage today.

Q: My team needs both data quality and business metrics. Is Lighthouse the right call?

Usually yes. Lighthouse gives you both in one tool, without YAML files or separate platforms. Business stakeholders use the visual builder. Engineers write SQL for custom quality checks. One system, one bill.

Q: Does Lighthouse need code to set up data quality checks?

For custom checks, yes: you write SQL. That's a one-time effort per check, not an ongoing YAML project. The visual metric builder handles most business metrics without any code at all.

Q: What warehouses does Lighthouse support?

Snowflake, BigQuery, Postgres, and Microsoft SQL Server. Redshift is supported through the Postgres connection. Lighthouse generates dialect-correct SQL per engine, not a generic query pasted across systems.

Soda specializes in data quality. Lighthouse covers business metric monitoring and data quality checks, in one tool, no code required, accessible to your whole team.

Start for free

Business metrics + data quality
No code required
Free forever tier

Lighthouse

Business user access: Full UI on every plan, including free, production-ready from day one
Setup time: < 10 minutes, UI-only
Pricing: Transparent per-user plans: solo teams from ~$50/mo

Soda

Business user access: No-code UI requires $750/mo Team plan; free tier is evaluation only
Setup time: Hours: YAML + pipeline wiring
Pricing: Team plan from $750/mo · Enterprise custom

How Lighthouse covers data quality

Every data quality check is a SQL query on a schedule. The trick is writing it once for all your tables, using your warehouse metadata or your own audit table as the source, and Lighthouse's segment feature to fan out one metric into per-table alerts.

Freshnessmonitoring

Query your warehouse metadata once. Segment by table_name. Lighthouse fires a separate alert per table that goes stale.

1 metric · every table in your DB

SELECT table_name,
  DATEDIFF('hour', last_altered,
    CURRENT_TIMESTAMP) AS hours_stale
FROM information_schema.tables
WHERE table_schema = 'analytics'

Segment by table_nameorders · not updated for 8h

Volumemonitoring

Pull row counts from your warehouse metadata. Lighthouse compares each table to its own 7-day baseline automatically.

1 metric · every table in your DB

SELECT table_name, row_count
FROM information_schema.tables
WHERE table_schema = 'analytics'

Segment by table_nameevents · row count down 43%

Completenessmonitoring

Write a stats view once that calculates null rates per column. Segment by column_name: one alert per column that spikes.

1 metric · every column you care about

SELECT column_name,
  100.0 * null_count / total_rows
    AS null_pct
FROM your_column_stats_view

Segment by column_nameusers.email · 12% nulls (was 0.3%)

Duplicatesmonitoring

Maintain a dedup stats view or query your audit table. Segment by table. Lighthouse alerts per table when duplicates appear.

1 metric · every table you track

SELECT table_name,
  total_rows - unique_key_count
    AS duplicate_count
FROM your_dedup_stats_view

Segment by table_nameorders · 3,241 duplicate keys

Pipeline delaysmonitoring

Query your pipeline log table. Segment by pipeline_name: one metric covers your entire ETL stack, one alert per job that runs late.

1 metric · every pipeline or job

SELECT pipeline_name,
  DATEDIFF('min', last_run_at,
    CURRENT_TIMESTAMP) AS mins_overdue
FROM your_pipeline_log
WHERE expected_run_at < CURRENT_TIMESTAMP

Segment by pipeline_nameetl_orders · 47 min overdue

Value validitymonitoring

Store daily aggregates in a stats table. Segment by metric_name. Lighthouse monitors all of them and alerts on any anomalous value.

1 metric · every business stat

SELECT metric_name, metric_value
FROM your_daily_stats
WHERE stat_date = CURRENT_DATE

Segment by metric_nameavg_transaction_amount · negative value

Works across Snowflake, BigQuery, Postgres, and MS SQL Server, each with dialect-correct SQL.

Side by side

Where each tool wins, and where the honest tradeoffs are.

Business KPI monitoring: Lighthouse
Native: revenue, users, trends, segments; Soda
Not purpose-built for business metrics
Data quality checks: Lighthouse
SQL metrics on any table or column; Soda
Automated across 20+ connectors
Business user access: Lighthouse
Full UI on every plan, including free, production-ready from day one; Soda
No-code UI requires $750/mo Team plan; free tier is evaluation only
Setup time: Lighthouse
< 10 minutes, UI-only; Soda
Hours: YAML + pipeline wiring
Creating a metric or check: Lighthouse
Visual builder or SQL, no engineering ticket; Soda
SodaCL YAML by default; AI Copilot + no-code UI on $750/mo Team plan only
AI threshold optimization: Lighthouse
AI suggests optimal alert thresholds based on your data patterns, no manual tuning; Soda
Manual threshold or ML anomaly detection on paid plans
Metric suggestions: Lighthouse
After connecting a dataset, Lighthouse suggests which metrics to create: AI-powered discovery; Soda
Basic profiling, no AI-powered metric discovery
Alert noise reduction: Lighthouse
Snooze · consecutive triggers · adaptive baselines · smart business trend comparison; Soda
Static thresholds (ML on paid plans)
Notification channels: Lighthouse
Slack, email, and more; Soda
Slack, email, and more
Connector coverage: Lighthouse
4 warehouses and databases; Soda
20+ incl. MySQL · Redshift · Oracle
Free tier: Lighthouse
5 metrics · no time limit; Soda
3 datasets · 25 checks · 1 user
Pricing: Lighthouse
Transparent per-user plans: solo teams from ~$50/mo; Soda
Team plan from $750/mo · Enterprise custom

Lighthouse compared with Soda
Feature	Lighthouse	Soda
Business KPI monitoring	Native: revenue, users, trends, segments	Not purpose-built for business metrics
Data quality checks	SQL metrics on any table or column	Automated across 20+ connectors
Business user access	Full UI on every plan, including free, production-ready from day one	No-code UI requires $750/mo Team plan; free tier is evaluation only
Setup time	< 10 minutes, UI-only	Hours: YAML + pipeline wiring
Creating a metric or check	Visual builder or SQL, no engineering ticket	SodaCL YAML by default; AI Copilot + no-code UI on $750/mo Team plan only
AI threshold optimization	AI suggests optimal alert thresholds based on your data patterns, no manual tuning	Manual threshold or ML anomaly detection on paid plans
Metric suggestions	After connecting a dataset, Lighthouse suggests which metrics to create: AI-powered discovery	Basic profiling, no AI-powered metric discovery
Alert noise reduction	Snooze · consecutive triggers · adaptive baselines · smart business trend comparison	Static thresholds (ML on paid plans)
Notification channels	Slack, email, and more	Slack, email, and more
Connector coverage	4 warehouses and databases	20+ incl. MySQL · Redshift · Oracle
Free tier	5 metrics · no time limit	3 datasets · 25 checks · 1 user
Pricing	Transparent per-user plans: solo teams from ~$50/mo	Team plan from $750/mo · Enterprise custom

Comparison based on publicly available information as of May 2026. Soda product details sourced from soda.io. If you spot an inaccuracy, let us know and we'll correct it promptly.

When to choose which

An honest guide. Both tools are good; the right answer depends on your situation.

Choose Lighthouse when…

You need business metric monitoring alongside data quality: revenue, users, conversions, product KPIs
You have analysts or business stakeholders who need to manage their own metrics without code
Setup speed matters: you want alerts running in minutes, not a YAML project to maintain
You want one tool and one bill instead of two specialized platforms
Alert noise is a problem: you need adaptive baselines, not manual threshold tuning

Choose Soda when…

Your primary need is automated data quality across a large number of tables with minimal configuration
Your team is all-technical and code-based workflows (YAML, CLI, dbt, Airflow) are the norm
You need deep pipeline integration: checks that run inside your Airflow or Dagster jobs
Schema drift detection and automated column-level profiling are must-haves out of the box

Where Soda genuinely has more depth

Soda's automated observability is more comprehensive out of the box, and it works across more connectors. It can detect schema drift, column-level anomalies, and pipeline freshness across hundreds of tables with minimal configuration, whether you're on Snowflake, Redshift, Databricks, or other engines. If automated zero-config data quality across your entire data platform is the primary requirement, Soda has more coverage today.

Lighthouse currently supports Snowflake, BigQuery, Postgres, Redshift (through the Postgres connection), and MS SQL Server. You write the SQL for each custom quality check: a one-time effort, not an ongoing project. Pre-built quality packs for more connectors are on the roadmap.

The tradeoff: most Soda checks are written in SodaCL YAML, meaning every check goes through an engineer, unless you're on the $750/mo Team plan. And business metric monitoring isn't in scope. If your team includes non-technical users, or if KPI alerting matters too, that's a real constraint.

What Lighthouse brings to the table

The things Soda doesn't do, or makes significantly harder.

Business metrics and data quality in one place

Revenue drops, user activity spikes, and pipeline freshness failures all live in the same alert feed. One tool to configure, one Slack integration, one system your team actually checks.

Non-technical users can self-serve, on every plan

Any analyst on your team can create and manage their own metrics using the visual builder: no code, no YAML, no engineering ticket. Free tier included.

Visual builder + raw SQL, no forced choice

Simple business metrics use the visual builder. Advanced or custom quality checks use raw SQL. Both go through the same scheduling, alerting, and audit system.

Adaptive alerting that learns your patterns

Snoozing, consecutive-trigger gating, and trend-based thresholds mean alerts fire when something genuinely changed, not every time the metric wavers slightly.

Full audit trail and workflow

Every run is logged: values, SQL, who acknowledged what. Severity levels, ownership, and access controls per metric. Your team can act on alerts, not just receive them.

Transparent pricing, generous free tier

Start for free with up to 5 metrics. Upgrade when you need more. No enterprise contract required to access the UI or onboard non-technical team members.

Common questions

Can Lighthouse actually do data quality monitoring?