Side-by-side comparison of AI visibility scores, market position, and capabilities
Paris France self-service analytics and data activation platform; enables operations teams to explore warehouse data and sync insights into business tools.
Whaly is a self-service analytics and data activation platform founded in 2020 and headquartered in Paris, France. The company was founded by Julien Lemaire and Pierre Tondereau to make warehouse data accessible to operations teams — sales, marketing, customer success, and finance — without requiring them to write SQL or depend on data analysts for every reporting request. Whaly provides a business-user-friendly exploration interface connected directly to cloud data warehouses, combined with reverse ETL capabilities for syncing warehouse data back into the operational tools where business teams work.\n\nWhaly is venture-backed with early-stage funding from French and European investors and is primarily focused on the European market, where it serves growing technology companies and scale-ups with data-driven operations teams. Its platform combines a no-code metric exploration interface — where business users can filter, segment, and drill into pre-defined metrics without SQL — with a data sync engine that pushes computed metrics and audience segments from the warehouse into Salesforce, HubSpot, Intercom, and other business applications. This combination of BI access and data activation in one platform distinguishes Whaly from tools that cover only one side of this workflow.\n\nWhaly's governed exploration model ensures that business users only access metrics that data teams have explicitly published and documented, preventing the ungoverned self-service that leads to metric fragmentation. Data teams build a curated catalog of metrics and datasets in Whaly, and business users explore and activate those curated assets. This producer-consumer model enables both data governance and operational self-service at growing companies where the data team cannot fulfill every analytics request manually.
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.
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).
Monitor how your brand performs across ChatGPT, Gemini, Perplexity, Claude, and Grok daily.