ThoughtSpot Review 2026

Hex

AI-assisted notebooks and data apps for analytics teams

Hex is a collaborative data workspace where SQL, Python and no-code cells live in one reactive notebook. Magic AI writes queries and transformation code from a plain-language question, using your warehouse schema and column descriptions as context so the generated SQL references real tables rather than plausible-sounding guesses. The reactive execution model means changing an upstream cell recomputes everything downstream, which keeps long analyses honest. When the work is done, publishing turns the same notebook into a polished interactive app with filters and parameters for stakeholders who will never open a notebook. Analytics engineers appreciate the dbt and warehouse integrations, version control and review workflow, while analysts get an AI pair that handles the boilerplate joins and chart code.

Akkio

No-code predictive AI, now repositioned specifically for media agencies

Akkio lets non-technical users build machine learning models — churn prediction, lead scoring, revenue forecasting — through a drag-and-drop interface and conversational Chat Explore feature, connecting to live data sources like HubSpot, Salesforce, Google Sheets and Snowflake without writing code. A user uploads a CSV or connects a live source, and Akkio's guided templates walk through building a working predictive model in minutes rather than requiring a dedicated data science team. A significant repositioning happened through 2025-2026: Akkio has moved from a general-purpose no-code AI platform toward a focused offering for media agencies specifically, announcing a partnership with Havas in January 2026 as part of Havas's €400 million agentic AI investment, alongside existing relationships with agencies like Horizon Media. Its current feature set leans into audience-building, media planning, and marketing mix modeling (MMM) — conversational campaign analytics and automated reporting workflows purpose-built for agency use, rather than the broader general-business predictive analytics positioning it held previously. Pricing has become considerably less transparent alongside this shift: as of July 2026, Akkio gates virtually all pricing behind a "Contact Sales" form, with historical figures ($49/user/month Starter, scaling to $999/month tiers) no longer reflecting current, published rates. If you're an individual marketer or analyst outside the agency-specific use case Akkio has moved toward, tools like Julius AI or Hex may now be a more directly accessible fit; for media agencies specifically needing audience analytics and MMM with genuine partnership backing, Akkio's newer focus is worth a direct sales conversation.

Julius AI

Trending

Chat with your data & spreadsheets

Julius AI turns a spreadsheet, CSV or database connection into a conversation. Upload a file or paste in messy data and ask questions in plain English — Julius writes and runs the underlying Python analysis itself, then hands back a chart, a table or a written answer instead of a wall of code. What sets Julius apart from a generic chatbot is that it treats analysis as a process, not a single prompt: it plans the steps, executes them, checks the output makes sense and iterates if a chart looks wrong or a query returns something unexpected. That loop is what lets non-technical users get correct pivot tables, regressions and visualisations without ever opening a notebook. Founders, analysts and ops teams use it as a faster substitute for pestering a data analyst with one-off questions — connect the data once and keep asking follow-ups the way you would in a conversation with a colleague who happens to know Python.

Formula Bot

Excel & Sheets formulas from text

Formula Bot solves a narrower but universally annoying problem: writing the right Excel or Google Sheets formula. Describe what you want in plain language — "sum column B where column A is 2025" — and it returns a working formula with an explanation of what each part does, so you are not just copy-pasting something you don't understand. Beyond formulas, it extends into a broader AI-for-spreadsheets toolkit: explaining an existing formula someone else wrote, generating VBA or Apps Script, and a chat-with-your-data mode for quick summaries directly inside a spreadsheet. It installs as an add-on for Excel and Google Sheets, so the workflow stays inside the tool people already use rather than pulling data into a separate app. It is aimed squarely at the person who knows what result they need but doesn't want to relearn spreadsheet syntax — marketers, ops staff and analysts who live in spreadsheets but aren't formula specialists.

Power BI Copilot

AI-generated DAX and visuals — but enabling it costs $5,258/month minimum

Power BI Copilot generates DAX formulas, builds visuals, summarizes data and writes natural-language narratives from your Power BI reports, using Azure OpenAI Service to translate plain-English prompts into actual Power BI actions — available in both Power BI Desktop and the Power BI Service. For an organization already invested in Power BI as its business intelligence layer, having an AI assistant that can write the DAX measure you're struggling to phrase correctly, rather than searching Stack Overflow for the syntax, is a genuinely significant productivity unlock for analysts who aren't DAX specialists. Here's the detail that catches a lot of people off guard, and it's worth understanding clearly before assuming Copilot is a simple per-user add-on the way Microsoft 365 Copilot is: enabling Power BI Copilot requires either Fabric capacity at the F64 tier or higher — which runs $5,258.88 per month — or Premium Per User (PPU) licensing at $20/user/month with a Fabric trial enabled and a Fabric administrator required to toggle on the Copilot tenant setting. There is no lower-cost, small-team entry point; this is fundamentally an enterprise-infrastructure decision, not a simple checkbox upgrade to an existing individual Power BI license. Once enabled through either path, Copilot itself carries no additional per-query charge — the cost is entirely in unlocking access via Fabric capacity or PPU licensing, not in metered usage afterward. That structure makes the real total cost heavily dependent on organization size: for a large enterprise already running substantial Fabric capacity for other workloads, the F64 tier may already be justified regardless of Copilot; for a smaller team evaluating Power BI Copilot specifically, PPU at $20/user/month is the more realistic entry point, and it's worth modeling total licensing cost carefully before assuming this is a lightweight add-on to an existing Power BI Pro subscription.

Coefficient

Auto-refreshing live data from 60+ business tools, straight into your existing Google Sheets or Excel

Coefficient takes a deliberately different approach from a new spreadsheet tool like [Rows](/tools/rows) in our [AI Data Analysis category](/category/ai-data-analysis): instead of asking you to switch tools, it's an add-on that brings live data from Salesforce, HubSpot, Google Analytics, QuickBooks and 60+ other business systems directly into the Google Sheets or Excel file you already use, with automatic refresh on a schedule you set. Its AI layer, Coefficient AI, can build pivot tables, generate charts, and answer questions about your connected data in plain language, but the core value proposition is really about eliminating the manual export-and-paste cycle that eats hours of many analysts' and marketers' weeks. Automated Slack or email alerts can also notify your team when a connected metric crosses a threshold, turning a static spreadsheet into something closer to a lightweight monitoring tool. A free tier covers a limited number of data connections and refreshes, aimed at individuals testing the workflow; paid plans scale by number of connected data sources and refresh frequency, positioning Coefficient as the practical choice for teams who've already built their reporting around spreadsheets and don't want to rebuild it in a new platform.

Rows

A spreadsheet with AI and live data built in, so formulas can pull from the web without an API

Rows rebuilds the spreadsheet around one idea: your data rarely lives only in the sheet itself, so why should pulling in outside information require an API or a separate integration tool? It connects directly to live sources like Google Analytics, HubSpot, Instagram, Stripe and financial market data, letting a formula reference "current follower count" or "this month's revenue" the same way it would reference a normal cell. On top of that, Rows AI can write formulas from a plain-English description, summarize a column of data, or build an entire report from a prompt — genuinely useful for people who know what they want a spreadsheet to show but not the exact formula syntax to get there. Compared to a pure dashboard-generator like [Polymer Search](/tools/polymer-search) in our [AI Data Analysis category](/category/ai-data-analysis), Rows stays closer to a familiar spreadsheet interface rather than an auto-built dashboard, which matters if your team already thinks in rows and columns. A genuinely usable free plan covers personal use and small teams; paid plans, starting around $10/month per editor, add more live data connections, larger team workspaces and priority support.

MindsDB

Query your databases with AI directly in SQL, without exporting data to a separate ML platform

MindsDB, founded in Berkeley in 2017, takes an approach most AI data tools don't: instead of exporting your data into a separate machine-learning platform, it brings AI and predictive modeling directly into your existing databases, letting you train and query ML models using familiar SQL syntax right where your data already lives. Its core open-source product is genuinely free and self-hostable, connecting to dozens of data sources (PostgreSQL, MySQL, Snowflake, MongoDB and more) and letting you create a predictive model with a SQL `CREATE MODEL` statement, then query predictions the same way you'd query a normal table. That "AI inside your database" approach removes a lot of the data-pipeline plumbing a tool like [DataRobot](/tools/datarobot) in our [AI Data Analysis category](/category/ai-data-analysis) still requires for enterprise deployments. Its newer MindsHub Router product adds a layer for routing requests across multiple LLMs from inside the same SQL-based workflow. The honest trade-off: this is a developer-facing, SQL-comfortable tool, not a point-and-click dashboard for a non-technical analyst. The open-source core is free; hosted and enterprise plans run from roughly $9.95/month for individual use up to custom enterprise pricing for larger deployments.

DataRobot

The enterprise AutoML platform that turns spreadsheets into deployed prediction models

DataRobot is one of the oldest names in automated machine learning — founded in Boston back in 2012, well before "AI platform" became a crowded label. It automates the unglamorous 80% of a data science project: cleaning data, testing dozens of algorithms against each other, picking the best-performing model, and deploying it as a live API, all without writing training code by hand. What sets it apart from lighter tools like [Julius AI](/tools/julius-ai) or [Formula Bot](/tools/formula-bot) in our [AI Data Analysis category](/category/ai-data-analysis) is scale: DataRobot is built for enterprise teams running hundreds of models in production, with MLOps monitoring that flags when a live model's accuracy starts drifting — a real problem most lightweight AI-analysis tools don't even attempt to solve. It has expanded into generative AI agent-building on the same platform, letting teams combine predictive models and LLM agents in one pipeline. Pricing is enterprise-quoted rather than published, which is the honest trade-off here: it's overkill and out of budget for a solo analyst, but for a company that needs governed, monitored, audit-ready models running continuously, it remains a category benchmark. A free trial is available for teams who want to test it against their own data before committing to a contract.

Obviously AI

No-code predictive machine learning — upload a spreadsheet, get a working prediction model in minutes

Obviously AI targets a specific gap between a pure dashboard tool and a full data-science platform like [MindsDB](/tools/mindsdb) in our [AI Data Analysis category](/category/ai-data-analysis): it lets a non-technical user upload a spreadsheet and build a working predictive model — forecasting churn, sales, demand or any other outcome in your historical data — entirely through a point-and-click interface, with no SQL or code required at any step. Its AI automatically selects an appropriate algorithm, handles feature engineering, and reports model accuracy in plain language rather than raw statistical output, specifically aimed at marketers, operators and small business owners who need a real prediction, not a data-science degree. A "What-If" simulator lets you adjust input variables and immediately see how the model's prediction changes, which is genuinely useful for scenario planning without rebuilding the model each time. That accessibility is also its honest limit: it won't handle the scale, governance or custom-model flexibility that DataRobot or MindsDB offer for larger, more technical teams. Pricing starts around $20-75/month depending on data volume and prediction frequency, with a free trial available to test a model against your own data before committing.

Julius AI

Trending

Chat with your data & spreadsheets

Julius AI turns a spreadsheet, CSV or database connection into a conversation. Upload a file or paste in messy data and ask questions in plain English — Julius writes and runs the underlying Python analysis itself, then hands back a chart, a table or a written answer instead of a wall of code. What sets Julius apart from a generic chatbot is that it treats analysis as a process, not a single prompt: it plans the steps, executes them, checks the output makes sense and iterates if a chart looks wrong or a query returns something unexpected. That loop is what lets non-technical users get correct pivot tables, regressions and visualisations without ever opening a notebook. Founders, analysts and ops teams use it as a faster substitute for pestering a data analyst with one-off questions — connect the data once and keep asking follow-ups the way you would in a conversation with a colleague who happens to know Python.