Startup profile · funding coverage
Lightup funding, valuation and investors
No-code data quality monitoring for enterprise data pipelines and warehouses.
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Funding, valuation & investors
Answer-first snapshotLatest funding
Series A
$9M · August 2023
Latest known valuation
Not publicly disclosed
Total disclosed equity funding
$9M
Excludes debt, grants, acquisitions, secondaries, and IPO proceeds.
Current status
private
series a · San Francisco, California
Overview
Lightup (lightup.ai) provides no-code data quality monitoring for enterprise data pipelines—automatically detecting anomalies, schema drift, and freshness issues in warehouses and ETL flows. The company raised a $9 million Series A in August 2023 co-led by Andreessen Horowitz and Newlands with Shasta Ventures participating per GlobeNewswire. Lightup targets data engineering and analytics teams tired of manual SQL checks and brittle open-source DQ scripts. The platform integrates with cloud warehouses and orchestration tools to alert owners before bad data reaches executives or ML models.
Why Lightup is interesting
Lightup catches silent data breakage before dashboards lie; a16z co-led $9M Series A as modern data stacks outpaced legacy DQ tools like Great Expectations deployments.
Product & use cases
Lightup monitors tables and pipelines with automated anomaly detection, lineage-aware alerting, and no-code rule builders—aimed at cloud-native data stacks.
- Warehouse freshness and volume anomaly alerts before BI reports break
- Schema drift detection after upstream API changes
- ML feature pipeline validation before model retraining
Key facts
- Series A (Aug 2023): $9M co-led by a16z and Newlands (GlobeNewswire)
- Shasta Ventures participated
- No-code data quality monitoring positioning
- Targets cloud warehouse and pipeline observability
Funding history (newest first)
Series A
2023-08 $9M- Andreessen Horowitz (lead)
- Newlands (lead)
- Shasta Ventures (participant)
Investors in our directory
Funds linked from Lightup's profile — open a fund page for stage focus and related deal articles.
Competitive landscape
Edge: No-code deployment vs engineering-heavy Great Expectations setups—faster time-to-coverage for mid-market data teams.
Data observability became a crowded category post-2020. Lightup competes on ease of setup; winners need deep integrations and clear ROI stories when budgets tighten.
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Monte Carlo direct
Data observability category leader; overlapping buyer and use cases.
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Great Expectations / Soda alternative
Open-source or lighter DQ; more engineering overhead.
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Databricks data quality features incumbent
Platform-native monitoring as vendors bundle observability.
Notable stories
- Company claims 10x faster issue detection vs legacy DQ workflows in marketing materials cited at funding.
- Series A timed as Monte Carlo raised larger rounds—category validation with consolidation risk.
- Founders previously built data infrastructure at scale-ups per press bios.
Industries
Related funding articles
Venture Capital Tracker pieces that cover Lightup's financing or category context.
FAQs about Lightup
Practical answers founders, operators, and investors typically search for.
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