The Best Hex Alternatives in 2026

Hex is an AI analytics platform for exploring and analyzing data through conversation grounded in governed context.

Enables data-driven, interactive data analytics and collaborative documents.

Apache Spark is a unified engine for large-scale data processing and analytics.

ADAM analyzes genomics data with specialized file formats built on Apache Spark, Avro, and Parquet.

Teams can use Dot as an AI data analyst to ask questions, generate SQL and charts, and produce reports through Slack or Teams.

Athina provides a collaborative development platform for building, testing, and monitoring AI features with a team.

TextQL organizes business logic into a data ontology for answering questions across company analytics.

Rivestack provides managed PostgreSQL with pgvector on NVMe for AI vector search.

Zerve supports research and analytics workflows, with AI agents for data discovery, analysis, reports and production deployment.

Sourcetable combines spreadsheets and data analysis tools for creating financial models and working with datasets.

Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media.

VeritasGraph combines knowledge graphs and GraphRAG for multi hop reasoning and attributed retrieval over user data.

Clay combines data sourcing, agent workflows, and go-to-market tools for teams building revenue systems.

Prophet provides forecasting procedures in R and Python that data scientists can tune.

Rhesis helps AI teams annotate and review agent behavior so engineers can improve their agents.

H2O AutoML automatically generates features and selections, with a no code AutoML offering.

Use Quadratic as an AI spreadsheet that connects to data and helps create explainable, shareable insights.

Open-source DMS (data management system) for powering data hubs and data portals.

Adala automates data labeling through an autonomous agent framework for organizing labeled datasets.

pytorch tabular provides a unified framework for deep learning models on tabular data.

PyTorch Frame provides a library for applying deep learning models to tabular data.

tsfresh automatically extracts relevant features from time series data.

Snorkel helps generate training data programmatically through weak supervision.

Disco’s discovery engine finds statistically validated patterns in tabular data, including feature interactions and subgroup effects.