y42 vs Modal
Side-by-side comparison of AI visibility scores, market position, and capabilities
Berlin Germany full-stack data platform; raised $31M+; combines ELT pipeline, dbt-based transformation, and BI in a single no-code/low-code environment.
About
y42 is a full-stack data platform founded in 2020 and headquartered in Berlin, Germany. The company was founded by Hung Dang and Fabian Schuh to build a unified platform that covers the entire modern data stack — ELT data ingestion, dbt-based SQL transformation, and business intelligence visualization — in a single integrated product. y42's thesis is that the fragmentation of the modern data stack, while enabling best-of-breed component selection, also creates significant operational overhead from maintaining multiple tools with separate authentication, monitoring, and support relationships. y42 integrates these layers into a single, cloud-hosted environment.\n\ny42 raised $31 million in funding from investors including Sequoia Capital, La Famiglia, and Creandum. The platform's ELT component provides pre-built connectors to more than 200 data sources, with the data delivered directly into the customer's own cloud data warehouse — Snowflake, BigQuery, or Redshift — ensuring data ownership and compliance. The transformation layer is powered by dbt under the hood, allowing analytics engineers familiar with dbt to work in their existing paradigm while benefiting from y42's visual interface and managed execution. The BI layer provides a drag-and-drop dashboard builder that connects to the transformed data models in the warehouse.\n\ny42 is particularly popular in the European market among data teams at growing technology companies and scale-ups that want the full modern data stack without the complexity of managing and integrating three or four separate tools. Its single-vendor support model and GDPR-compliant European data infrastructure make it a strong fit for EU-based organizations with compliance 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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