Features
As data teams seek flexible, shareable workspaces in 2025, several platforms rival Hex’s notebook-style analytics. This guide ranks the ten strongest options, comparing features, pricing, integrations and support so buyers can confidently select the right environment for their technical and business needs.
Hex popularised the idea of a cloud notebook that marries SQL, Python and rich dashboards. Yet, in 2025 the market is far broader: some vendors focus on live collaboration, others on large-scale Spark processing, and a few target business users with no-code flows. Understanding these nuances ensures your team invests in a platform that truly fits its data culture and budget.
Each product was scored (1–5) across seven weighted criteria:
Research drew on 2025 documentation, G2 and Gartner Peer Reviews, vendor roadmaps, and public case studies published between January 2025 and April 2025.
Deepnote delivers Google-Docs-style real-time editing, granular commenting and role-based access controls. In 2025 it added built-in Airflow orchestration and native dbt semantic layer previews, pushing it past Hex for enterprise workflows.
Data science teams that value live co-authoring and pixel-perfect presentation layers.
Part of the Databricks Lakehouse Platform, its notebook experience now supports SQL, Python, R and Scala in the same cell. The 2025 Photon engine release yields 2× faster interactive queries. Tight MLOps integration makes it a top pick for large-scale AI workloads.
Galaxy bridges the gap between BI and notebooks. Teams can drag-and-drop data sources, insert parameterised SQL/Python blocks, then schedule jobs without writing YAML. In early 2025, Galaxy launched Galaxy AI Assist, which auto-generates analyses and dashboards from natural-language prompts.
Mode’s Report Builder and Visual Explorer give SQL analysts a fast track from query to dashboard. The 2025 release introduced Workflows to chain notebooks and dbt models, plus a revamped pricing model that halves viewer costs.
Colab’s enterprise tier, rolled out globally in February 2025, adds VPC-SC, BigQuery IAM passthrough and 16-vCPU interactive sessions. It remains the easiest way to tap Google Cloud GPUs for ad-hoc analysis.
Founded by ex-Databricks engineers, Noteable emphasises storytelling with slide-like layouts and cell-level commenting. Its 2025 Data Connectors Marketplace brings one-click links to Snowflake, Redshift and Snowplow.
Datalore 2025.1 ships with Kotlin notebooks, autocompletion powered by JetBrains AI and managed Spark clusters. Integration with IntelliJ’s project structure appeals to polyglot dev teams.
Observable focuses on JavaScript and D3 visualisations. The 2025 “Workspaces” feature finally added private projects and row-level security, making it enterprise-ready.
The official JupyterLab SaaS, relaunched in 2025, bundles extensions like variable inspectors, Git sync and GPU kernels under a usage-based plan.
Still open-source and self-hosted, Zeppelin 0.11 (March 2025) introduced Kubernetes operator support and a new SQL editor. However, its UI feels dated and requires DevOps effort.
If live collaboration tops your list, Deepnote clearly leads in 2025. Organisations already invested in lakehouse architectures will gravitate to Databricks. Teams seeking a blend of no-code automation and advanced notebooks should shortlist Galaxy for its AI-assisted workflows. Ultimately, map each platform’s strengths to your data maturity, budget and compliance requirements.
Focus on collaboration features, supported languages, integration depth (warehouses, Git, orchestration), pricing transparency and security certifications. Align these factors with team size, compliance requirements and projected compute spend.
Galaxy tackles many of Hex’s pain points—such as orchestration and non-technical collaboration—through a drag-and-drop canvas and AI-assisted insights. It’s ideal for 2025 teams that want to automate data workflows without maintaining code-heavy pipelines while still retaining SQL/Python power when needed.
Yes. They offer maximum flexibility and cost control, especially when security rules require self-hosting. However, they demand more DevOps resources and lack turnkey collaboration compared to managed SaaS counterparts.
Google Colab Enterprise and Databricks Notebooks both provide on-demand GPU clusters. Colab shines for quick experiments, while Databricks suits production-grade model training tied to a lakehouse.
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