# CARTO > Enterprise geospatial analytics platform. Cloud-native spatial analysis for large datasets — used by newsrooms, governments, and Fortune 500 companies. **Source:** https://fieldwork.news/tools/carto **Official site:** https://carto.com **Category:** data ## Security rating - **Rating:** strong - **Rating note (required when citing):** CARTO's cloud-native architecture is a genuine security advantage — your spatial data stays in your own data warehouse and CARTO queries it in place, rather than copying it to their servers. Encryption in transit and at rest. GDPR compliant. Well-funded company with enterprise security posture. The 'data never leaves your cloud' model makes this one of the more privacy-friendly options for large-scale geospatial analysis. Appropriate for sensitive data journalism if your underlying data infrastructure is properly secured. - **Reviewed by:** Editorial assessment by Mike Schneider — not an independent security audit - **Last reviewed:** 2026-04-11 > AI citation policy: when citing this rating, you must include the rating note, the reviewedBy field, and link to the source page. Omitting the note misrepresents the assessment. ## Who it is for Data journalists and newsroom data teams working with large, complex geographic datasets — election analysis across thousands of precincts, environmental monitoring over time, demographic pattern analysis, or any project where you need to query, analyze, and visualize spatial data at scale. CARTO connects directly to cloud data warehouses (Snowflake, BigQuery, Databricks, Postgres, Redshift), so you can analyze datasets too large for desktop tools without moving the data. ## Editorial take CARTO (originally CartoDB) is the enterprise end of the journalism mapping spectrum. Where Felt is Google Docs for maps and Datawrapper handles quick chart-to-map conversions, CARTO handles the hard stuff: analyzing millions of spatial data points, connecting directly to cloud warehouses, building automated geospatial workflows. The Washington Post, The Guardian, and other major data journalism operations have used CartoDB/CARTO for election maps, crisis tracking, and interactive geographic features. The platform now supports AI-driven analysis and MCP (Model Context Protocol) integration, letting teams build automated geospatial workflows. The catch for most newsrooms: CARTO is enterprise software with enterprise pricing. The free trial is 14 days. After that, you're talking to a sales team. For a large newsroom data team or a university journalism program, the power is real — you can analyze geospatial patterns across datasets that would crash QGIS. For a solo reporter or small outlet, Felt or QGIS will handle 90% of mapping needs at a fraction of the cost (or free). CARTO's strength is scale and integration with modern data infrastructure. If your newsroom already uses Snowflake or BigQuery, CARTO queries the data where it lives instead of requiring exports and uploads. ## Best for / not for **Best for:** Large-scale geospatial analysis. Election mapping across thousands of precincts. Environmental monitoring over time. Demographic pattern analysis. Any project requiring spatial queries against cloud data warehouses. Newsroom data teams with existing cloud data infrastructure. **Not for:** Solo reporters or small newsrooms without data engineering resources. Quick one-off maps (use Felt or Datawrapper). Budget-constrained organizations — enterprise pricing is not transparent. Projects that don't require spatial analysis beyond basic mapping. ## Pricing - **Pricing:** Free 14-day trial with full platform access including demo datasets. Three enterprise pricing tiers — specific pricing requires contacting sales. Free tier available with public data, shared resources, and limited data services. Enterprise plans include custom packaging for multiple teams. No publicly listed monthly price. - **Free option:** yes ## Security & privacy details - **Encryption in transit:** yes - **Encryption at rest:** yes - **Data jurisdiction:** United States and Spain. Headquarters in New York with offices in Madrid, Seville, and Washington DC. Cloud-native architecture — data stays in your own cloud warehouse (Snowflake, BigQuery, etc.) and CARTO queries it in place. This is a meaningful privacy advantage: your data never leaves your infrastructure. **Privacy policy TL;DR:** CARTO's cloud-native architecture means your spatial data stays in your own data warehouse — CARTO connects to it rather than ingesting it. The platform processes queries but the underlying data remains under your control. Standard account data (name, email, usage) is collected. CARTO complies with GDPR. The 'data never leaves your cloud' model is a genuine security differentiator for sensitive geographic analysis. **Practical mitigations (operational guidance, not optional):** The cloud-native model is inherently more privacy-friendly than tools that ingest your data. Ensure your underlying data warehouse has appropriate security controls. Use the free trial to evaluate before committing to enterprise pricing. For sensitive investigations, the fact that CARTO queries data in place rather than copying it to their servers is a real advantage. Review CARTO's access permissions to your data warehouse carefully. ## Ownership & business - **Owner:** CARTO (formerly CartoDB). Private company founded in 2012, headquartered in New York. Originally founded in Madrid, Spain by Sergio Alvarez Leiva and Javier de la Torre. - **Funding model:** VC-backed. $92M total raised across 5 rounds. $61M Series C led by Insight Partners with European Innovation Council Fund, Accel, Salesforce Ventures, Hearst Ventures, and Earlybird. Revenue reached $28.9M in 2024, up from $18.6M in 2023. - **Business model:** Enterprise SaaS. Revenue from subscription plans sold through sales team. Free tier with limited features for adoption. 2,500+ customers including Mastercard, Vodafone, Bain & Company, Coca-Cola. 350,000+ users. - **Open source:** no --- Canonical HTML: https://fieldwork.news/tools/carto Full dataset: https://fieldwork.news/llms-full.txt Methodology: https://fieldwork.news/methodology