The Best AI Tools for Data Science in 2026: Two Different Buys, Not One List

Databricks closed a funding round on July 16, 2026 that valued the company at $188 billion, the same week a much smaller rival filed under the same category, Rows AI, finished shutting down for good.

Both get grouped as "AI for data science" in roundup after roundup, but they solve different problems. One runs code against your data. The other turned a typed question into a chart. Conflating them is why so many "best data science tools" lists read the same, and why the split matters more than any individual review score for a small team picking a tool this quarter.

Why data science and AI analytics keep getting sold as one category

HokAI's own data-science-and-ML directory lists 14 active tools, and only two of them, Runcell and Databricks, write and execute code against a dataset the way a data scientist actually works: opening a notebook, writing a function, running it, reading the output, iterating. The other twelve turn a written or spoken question into a chart, a formula, or a SQL query, without ever exposing a line of code to the user.

That is a real distinction with a real budget consequence. A tool that writes SQL for you assumes the schema, the warehouse, and the access controls already exist. A tool like Runcell assumes you have raw data and no fixed answer yet. The practical test is one question: are you exploring data you do not yet understand, or reporting on data you already do?

Tools like ThoughtSpot and Qlik are built for the second case, letting a non-technical user type a question in English and get a governed dashboard back. Neither one will build a predictive model from scratch, and neither claims to. A team that buys one expecting the other loses weeks of budget cycle finding that out.

Picture a five-person analytics team at a seed-stage startup that just closed a round. They already have clean revenue data sitting in a warehouse and want a dashboard the founders can query themselves before board meetings. That team needs an analytics platform, not a notebook agent.

A two-person research team at the same company needs the opposite. They are trying to figure out why churn spikes on a specific cohort, with no existing model to explain it, which calls for something that writes and runs exploratory code. Both teams work at the same startup. Neither one should buy what the other needs.

Four questions that actually separate these tools

Do you need to write and run code, or just ask questions of data that is already clean? If the answer is code, Runcell or Databricks are the only real candidates here. Everything else is a dashboard with an AI front end bolted on, however capable that front end has become.

Is your data warehouse already decided? Databricks only makes sense once you have committed to a lakehouse. If you are still deciding where data lives, a lighter analytics layer is the lower-commitment move, and switching later costs a migration, not a rebuild.

Does the vendor publish a real price, or does everything route to a sales call? Akkio keeps list pricing off its site entirely. If your team cannot get a number without a meeting, budget for a multi-week procurement cycle, not a same-day signup.

Who actually needs to touch the tool? A single analyst wants something they can expense on a card, like AI2sql's $9-a-month plan. A company-wide rollout needs a vendor with named account management, which rules out most of the narrower tools in this category outright. Ask that question before the demo, not after.

What Runcell actually does inside a notebook

Runcell is a Jupyter-native AI agent, not a chat window bolted onto a notebook. It writes Python inside a JupyterLab session you already have open, runs the cell, reads whatever comes back, whether that is a table, a chart, or a stack trace, and debugs its own errors without a human copying an exception message into a prompt.

It also keeps memory across a session: earlier decisions about a dataset, like which columns were dropped or which join was used, persist so a later question does not restart the analysis from zero. Runcell gives access to several underlying models, including GPT, Claude and Gemini variants, rather than locking a user into one. Its Hobby plan is free with a set of monthly credits and no API key required to start.

What Runcell has not done, as of August 2026, is publish exact dollar figures for its paid tiers on its own marketing site. That is worth knowing before you plan a budget around it rather than after, since "free Hobby plan, undisclosed paid tier" is a different commitment than a listed price you can compare against AI2sql or Akkio.

Databricks: the platform for teams that already committed

Databricks sits at the other end of that spectrum: a lakehouse platform built for petabyte-scale data engineering and machine learning, billed per second with no flat published list price. The $188 billion valuation from its July round, led by existing investor Coatue, reflects an enterprise-scale product, not a starter tool a two-person team signs up for on a Tuesday.

The choice between Runcell and Databricks comes down to scale, not preference. A team that already runs a lakehouse and needs governed, production-grade pipelines across petabytes should pick Databricks outright; nothing else in this category handles that scale. One or two people exploring a dataset that fits on a laptop, who want an agent to write and debug notebook code without standing up infrastructure, should pick Runcell, test it on the free Hobby plan, and decide from there once the workflow proves out.

AI2sql and Akkio: the narrow tools worth knowing about

AI2sql turns an English question into a SQL query. The Start plan is $9 a month for 100 queries and 10 tables, Pro is $24 a month for 300 queries and 50 tables and adds Oracle, MongoDB and BigQuery support, and Business is $39 a month for 1,000 queries with unlimited tables, all backed by a 7-day free trial, according to AI2sql's own pricing page. The query caps make it a personal tool for one analyst, not a team-wide subscription, and that is exactly who it is built for.

Akkio does the opposite job: no-code predictive modeling, aimed at a marketing or ops team that wants a forecast without writing a line of code. Its own site quotes a Horizon Media executive crediting the platform with cutting an audience-building workflow from six weeks to about ten minutes. Akkio publishes no tier pricing at all, not a single starting number, only a form that routes to a sales call.

Both sit closer to the analytics side of this category than to Runcell or Databricks, but neither shows up on the generic "best data science" roundups, because neither is trying to be a general-purpose platform. A single analyst with a recurring SQL question does not need a lakehouse. A marketer forecasting campaign response does not need a notebook. That narrowness is what makes them worth naming here instead of skipped for something more famous.

Where ThoughtSpot and Qlik fit

ThoughtSpot and Qlik round out this category on the dashboard side. Both let someone type a question in plain English and get a chart back, with governance and row-level security built for a company-wide rollout rather than one analyst's laptop.

Both entry tiers start in roughly the same range as AI2sql's top plan, and both push anything past the entry tier behind a sales call. If dashboards, not code, are the actual job, treat this pair as its own separate shopping decision with its own comparison, not an extension of this one.

The two differ most on who is expected to own the setup. ThoughtSpot and Qlik both assume someone will model the underlying data before the AI agent gets to answer anything usefully, which is a real project, not a weekend task. A team with no data model decided yet is better served picking one of the code-first or narrow tools above, getting a first answer fast, and revisiting a full analytics platform once the same questions keep repeating.

Who should skip this entire category

Anyone doing pure model training and evaluation, not data exploration or reporting, should skip this whole list. Surge AI sits adjacent to this category but does a third job entirely: producing the human-labeled and RLHF training data that frontier AI labs use to train models in the first place, not analyzing data that already exists.

Surge AI reported $1.2 billion in annualized revenue in 2024, according to a company profile published by the research firm Sacra, built on a workforce Sacra puts at roughly 50,000 contract annotators and 130 full-time staff, with Meta alone reportedly spending more than $150 million a year on the service. None of that has anything to do with picking a dashboard or a notebook agent. If labeled training data is the actual need, none of the tools above are the right conversation.

Skipping the categorization homework is fine, until money moves

A small team without a dedicated data hire genuinely does not have a spare afternoon to map out a whole category before picking a tool, and that is a fair complaint against everything above. It also does not need one yet. Runcell's free Hobby plan costs nothing to try. Testing it alongside a quick look at AI2sql loses you an hour, not a quarter.

The categorization only starts to matter once a contract gets signed. Committing to Akkio's sales-only pricing, or a Databricks committed-use agreement, before confirming which side of this split you actually need is the expensive version of the same mistake, and those are exactly the contracts that are hardest to unwind once signed.

The vendor risk sitting underneath the smaller names

Rows AI was acquired by Superhuman in an announcement posted February 23, 2026. Portugal Startup News reported the product's full shutdown for May 31, 2026, closing out a run the outlet put at 2.2 million users over nine years, during which the platform executed more than 17 billion spreadsheet functions and over 800,000 AI Analyst prompts.

It never belonged on this list as a data-science tool, but its disappearance is the risk sitting underneath every narrower name here. Runcell, AI2sql and Akkio are all smaller, venture-backed companies with far less capital behind them than Databricks or Qlik.

AI-native analytics is consolidating fast, and a startup vendor's free tier can vanish inside a single fiscal year, folded into whichever larger company decided the workflow was worth owning instead of competing against.

Before you build a real workflow around any of the smaller tools in this guide, ask the vendor directly whether they have been approached about an acquisition. Most will not answer that question. The ones that do are worth the extra ten minutes it takes to ask, and the answer tells you more about next year's roadmap than any feature list on the pricing page.

Frequently asked questions

What's the difference between an AI data-science tool and an AI analytics tool?

A data-science tool like Runcell or Databricks writes and runs code against raw data, producing a model or pipeline you can inspect and rerun. An analytics tool like ThoughtSpot or Qlik turns a typed question into a dashboard or chart without exposing any code. Most "best AI tools for data science" lists mix the two together, which is why the picks rarely match what a specific team actually needs.

Is Databricks worth it for a small team?

Only if you already have, or are actively building, a lakehouse architecture. Databricks bills per second with no flat published price, and it was valued at $188 billion in a funding round that closed July 16, 2026, which reflects an enterprise-scale product, not a starter tool. A team without existing data infrastructure gets more immediate value from a narrower tool like Runcell or AI2sql.

What's a cheaper alternative to Databricks for someone who just needs SQL written for them?

AI2sql turns an English question into a SQL query starting at $9 a month for 100 queries and 10 tables, with a 7-day free trial. It caps out fast, so it suits a single analyst rather than a whole team, but it needs no infrastructure commitment at all.

What happened to Rows AI?

Superhuman announced its acquisition of Rows on February 23, 2026, and the tracking site AlternativeTo logged the product's full shutdown for May 31, 2026. It is a concrete example of the acquisition risk that comes with picking a smaller, venture-backed AI tool in this category over an established one.

Do I need a different tool if I'm training AI models rather than analyzing existing data?

Yes. Surge AI, which reported $1.2 billion in annualized revenue in 2024 according to the research firm Sacra, supplies the human-labeled and RLHF training data that frontier AI labs use to train models, a different job from analyzing data you already have. None of the tools in this guide replace that function.

Covered in this guide

• Databricks: Unified Lakehouse platform for data engineering, analytics, and AI, founded 2013, valued at $134B, with consumption pricing from $0.07 per DBU.
• Runcell: AI agent for Jupyter notebooks that writes, runs, and debugs code from natural language prompts; free tier includes 50 credits/month, Pro is $20/month.
• Qlik: Data analytics and integration platform used by 40,000+ organizations worldwide.
• AI2sql: AI2sql is an AI SQL generator that converts natural language descriptions into optimized SQL queries for 10+ database types, supporting PostgreSQL, MySQL, BigQuery, Snowflake with 90% accuracy.
• Akkio: Akkio is a no-code AI analytics platform that trains predictive models up to 100x faster than AutoML, with natural language data queries. Plans start at $49/user/month.
• Rows AI: AI spreadsheet scoring 89% first-try accuracy vs Excel's 53%, with GPT formula functions and 50+ live integrations. Acquired by Superhuman; sunset May 31, 2026.
• Surge AI: Surge AI powers RLHF for Anthropic, OpenAI, and Google, with $1.2B revenue in 2024 and 100K+ expert annotators. Custom enterprise pricing; no free tier.
• ThoughtSpot: AI-Powered Agentic Analytics Platform for Self-Service Business Intelligence

Sources

• Databricks Raising a Strategic Round of Funding at a $188 Billion Valuation
• Databricks Pricing
• Runcell
• ThoughtSpot Pricing
• Qlik Cloud Analytics Pricing
• Akkio Pricing
• AI2sql Pricing
• Superhuman to Acquire Rows
• Superhuman to acquire Portuguese spreadsheet company Rows to bolster AI productivity suite
• Surge AI revenue, funding & news