Data platforms and AI

Data platforms and AI follow a company's data from the moment it is recorded to the moment a model learns from it. This Domain of the product-companies collection holds the companies that store data, move it between systems, turn it into dashboards and answers, sell data a company cannot collect for itself, and build the AI models and the platforms that train and run them. Their buyers are engineers, data teams and analysts in every industry, so their products sit underneath apps that people use every day without ever seeing them. Much of this engineering is done in India, above all in Bengaluru.

Some of the best-known names in this Domain are told separately, such as the cloud warehouses and the most widely used AI models of the largest technology companies. (More on them in Microsoft, Google and Meta, in the big-tech collection.)

The Domain's three Industry Verticals follow that trail, data kept in Database and storage companies, moved and read in Analytics and data providers, and learned from in AI labs and model platforms. Every company named in this Domain has posted software engineering jobs in India.

Database and storage companies

Database and storage companies are where the trail begins. Its companies record every transaction and keep every byte safe, like MongoDB, Snowflake, Databricks, NetApp and Cohesity, and the whole picture is on the Database and storage companies page.

Analytics and data providers

Analytics and data providers take the data out of storage and make it useful. Its companies move it, show it on dashboards and sell the outside data a business cannot gather itself, like Fivetran, ThoughtSpot, NielsenIQ, AlphaSense and HERE Technologies, and more is on the Analytics and data providers page.

AI labs and model platforms

AI labs and model platforms are where the data is turned into models. Its companies build language, voice and robot models and the plumbing that trains, serves and judges them, like Sarvam, SoundHound AI, Anyscale, TrueFoundry and Surge AI, and more is on the AI labs and model platforms page.

Where the verticals meet

The three Industry Verticals are links in one chain, and each depends on the next. Pipeline companies like Fivetran and Matillion exist to fill the warehouses and lakehouses that Snowflake and Databricks sell, and business intelligence tools like ThoughtSpot and Domo read their answers out of those same warehouses. Databricks has moved furthest along the chain. It began with the lakehouse and now also sells the tools to build AI models on the same data, so it belongs as much to the model platforms as to the databases.

AI is pulling the whole Domain towards it. Database and warehouse companies like MongoDB, Teradata and Cloudera are adding vector search, which finds data by meaning rather than by exact words, and features that let people question their data in plain language. Business intelligence is moving the same way, from the dashboard to the question typed in plain language, at companies like ThoughtSpot and WisdomAI, which was founded by a team from the backup company Rubrik. At the bottom of the stack, storage companies like DDN and WEKA now build storage fast enough to feed the GPUs that train the models.

Each part of the chain hires for its own kind of work. Database and storage companies hire C and C++ systems engineers and cloud engineers who build managed services. Analytics and data providers make the data engineer their signature role. AI labs and model platforms hire GenAI engineers above all, mostly to build with models rather than to train them from scratch.

That is Domain III, Data platforms and AI, the trail data follows from the database where it is kept to the model that learns from it.