Steep vs Modal
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 engineering teams.
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.
Serverless GPU cloud platform for AI/ML with Python-native deployment and per-second billing; developer-favorite scaling from zero competing with Replicate and Beam for AI compute.
About
Modal is a serverless cloud computing platform purpose-built for AI and machine learning workloads — providing on-demand GPU compute that scales instantly from zero with per-second billing, container management, distributed training support, and a Python-native developer experience that makes running ML workloads in the cloud feel as simple as running code locally. Founded in 2021 in New York City and backed by Redpoint Ventures and other investors, Modal has grown rapidly as AI development has accelerated demand for flexible, developer-friendly GPU infrastructure.\n\nModal's developer experience is its primary differentiator — engineers write Python functions decorated with @modal.function() and deploy them to the cloud with a single command, with Modal handling container building, GPU provisioning, auto-scaling, and execution. The platform supports training jobs that need distributed compute across multiple GPUs, model serving endpoints that scale to zero when unused (eliminating idle GPU costs), and batch inference jobs that process large datasets. The per-second billing model means developers pay only for actual compute time, not provisioned instances.\n\nIn 2025, Modal competes in the AI infrastructure market with Replicate, Beam, Banana, and major cloud providers' managed ML services (AWS SageMaker, Google Vertex AI, Azure ML) for serverless GPU compute. The market for AI-specific cloud infrastructure has grown dramatically as the number of ML engineers deploying models to production has expanded — traditional cloud providers require significant DevOps expertise to use GPU instances effectively, while Modal's Python-native approach reduces the barrier to entry. Modal has attracted a strong developer following among AI researchers and ML engineers building production AI applications. The 2025 strategy focuses on growing the developer community, adding enterprise features (dedicated GPU capacity, private networking, compliance), and expanding the hardware options available (H100 GPUs, custom accelerators).
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