Steep vs Acceldata
Side-by-side comparison of AI visibility scores, market position, and capabilities
Copenhagen Denmark BI platform for modern data teams connecting to Snowflake and BigQuery; metric-centric analytics with fast warehouse-native query execution and clean opinionated UI designed as an alternative to legacy BI paradigms for analytics...
About
Steep is a business intelligence and analytics platform founded in 2021 and headquartered in Copenhagen, Denmark. The company was founded by former product and engineering leaders to build a BI tool optimized for the modern data team workflow — fast, warehouse-native query execution, a clean and opinionated UI, and first-class support for the metric-centric analytics workflows that analytics engineering teams are building. Steep positions itself as an alternative to legacy BI tools that carry the weight of decade-old UI paradigms and to overly complex enterprise platforms.\n\nSteep has raised pre-seed funding and operates as a lean, product-focused startup primarily targeting analytics engineering teams in Europe and growing technology companies. Its platform connects directly to Snowflake, BigQuery, and Redshift as the query engine, ensuring that all analysis runs against live warehouse data without intermediate caching layers that can serve inconsistent results. Steep's metric layer allows teams to define business metrics centrally and build dashboards around those metrics rather than one-off SQL queries, promoting consistency in how the company measures performance.\n\nSteep's dashboard experience is designed for both analysts building data products and business stakeholders consuming them, with a clean viewer mode that removes technical noise for non-technical audiences. The platform supports scheduled email and Slack delivery of dashboard snapshots, data alerting for metric threshold monitoring, and embedding for sharing dashboards in internal tools. Steep's European roots and GDPR-compliant data architecture make it a strong fit for European organizations with data residency requirements.
San Jose CA data observability platform raised $55M+; monitors data pipeline health, quality, and compute cost across multi-cloud environments; founded by Hortonworks veterans covering four observability pillars for enterprise data engineering teams.
About
Acceldata is a data observability and data pipeline monitoring company founded in 2018 and headquartered in San Jose, California, with engineering operations in Bengaluru, India. The company was founded by Rohit Choudhary and Achal Agarwal, data infrastructure veterans from Hortonworks and other enterprise data companies, to provide deep operational visibility into modern data environments. As data stacks became more complex with multiple data platforms, streaming pipelines, and warehouse compute, data engineering teams lacked a unified view of pipeline health, data quality, and infrastructure cost — problems Acceldata was built to solve.\n\nAcceldata raised $55 million across two funding rounds led by March Capital and Insight Partners. Its platform covers four pillars of data observability: data reliability monitoring for detecting anomalies in data freshness, completeness, and distribution; pipeline observability for tracking job health, latency, and failure rates across Spark, Airflow, dbt, and other orchestration tools; compute intelligence for analyzing and optimizing cloud warehouse and data platform costs; and data quality testing for defining and validating data quality rules. This breadth distinguishes Acceldata from narrower data observability tools that focus primarily on data quality checks.\n\nAcceldata supports complex enterprise data environments including multi-cluster Hadoop, Spark, Databricks, Snowflake, BigQuery, Redshift, and Kafka, reflecting its roots in large-scale enterprise data platforms. Its compute intelligence capability is a differentiator, providing cost attribution down to the team, job, and user level so data platform owners can identify waste and enforce cost governance in cloud warehouse environments where runaway compute costs are a common problem.
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