The best AI agents for data analysis in 2026

1. 1

   H

   HexPublished prices

   The Notebook Agent writes, edits and runs SQL and Python cells against Snowflake, BigQuery, Databricks or Redshift, and asks for approval before destructive edits. Business users ask questions in Threads, which answer from endorsed dbt semantic models, while versioned eval suites rerun the team's test questions on a schedule to catch regressions.

2. 2

   T

   TelliusPriced by quote

   Kaiya, the AI analyst, takes a described outcome, plans the steps for the team to shape, then runs specialist agents over a governed semantic layer of the company's metrics and access rules, showing every SQL and Python step with full lineage. Missions run on schedules or fire when a KPI breaks pattern.

3. 3

   T

   ThoughtSpotPriced by quote

   Spotter answers business questions in plain language over verified definitions in the semantic layer, checking its own work, inside Salesforce, Slack or the mobile app. SpotterModel drafts semantic models from raw data for the data team to approve, SpotterViz builds dashboards from a prompt and SpotterCode writes embedded analytics code in the IDE.

4. 4

   D

   DatafoldPriced by quote

   The Migration Agent translates legacy SQL, stored procedures and Informatica or SSIS jobs into dbt, then checks each translation against the source with value level data diffs and corrects itself until the numbers match. Datafold's team supervises the work and hands over a report that links every diff as proof of parity.

5. 5

   R

   RedbirdPriced by quote

   A routing agent turns a plain language question into work for a Data Engineering Agent, a Data Science Agent and a PowerPoint Reporting Agent, which return a finished analysis and deck. Every step is logged and editable without code, and self healing agents repair steps that break when a source changes.

6. 6

   W

   Wisdom AIPriced by quote

   Analytics agents built in prompt mode or on a visual canvas run on a schedule or when a metric crosses a threshold, then deliver findings to Slack, email or cloud storage. Answers come from a governed context layer of metrics, synonyms and reviewed queries, and each run keeps its generated SQL for review.

7. 7

   Z

   ZenlyticPriced by quote

   Zoë reads the warehouse schema and existing LookML, dbt or DAX context to build a governed semantic layer, then answers questions in Slack, Teams, Claude or ChatGPT with the SQL behind each number. The data team promotes the fields she creates to verified, and Proactive Agents run her tasks on a schedule.

8. 8

   D

   DefinitePriced by quote

   Fi searches the semantic layer for governed definitions before it writes a query, then returns the answer as a data app or dashboard built over data synced from more than 500 connectors. Monitoring agents probe the lakehouse on a schedule and post to Slack when a metric crosses a threshold.

9. 9

   O

   OpenseePriced by quote

   Agensee plans a multi step analysis over live risk and trade data, declares the plan, then runs SQL and analytics in a sandbox permissioned to each user. A market risk team can ask why VaR moved since yesterday and get a report on the drivers, with every query readable and every action reversible.

10. 10

    A

    Actian AI AnalystPublished prices

    Business users ask questions in Slack, Teams or the web app, and the analyst queries Snowflake, BigQuery, Databricks and other warehouses through a governed semantic layer. A Steward agent maintains that layer and proposes fixes an admin approves, Plan Mode waits for sign off before running, and Scheduled Insights flag anomalies by email.

See the full ranking with scores

Broader platforms that also cover data analysis

These 34 vendors sell a wider product and name work for data analysis as one of its uses. They suit a company that already runs on the platform or wants one agent layer across several teams.

A

Appian

Process agents query records and run SQL over the data fabric, which reads connected systems in place without migration, and chat agents answer questions on that data inside the apps people use. Process Intelligence turns event data into process views that show where workflows stall, with AI driven recommendations for broken sequences.

D

Dataiku

Visual agents for analysts and Python code agents for data scientists run on the same governed data preparation and machine learning pipelines. Knowledge Bank search grounds answers in documents with document level security, and agents run unattended in automation scenarios on a schedule or when the data changes.

G

GPTBots

Analysts ask questions of their databases in plain language and get Text2SQL queries and charts back, and MultiAgent breaks a research request into a task list the analyst confirms before Browser Use and coding agents gather data and build tables. The planner reviews each result against the analyst's standards before compiling the report.

C

Coworker

A Data Analyst agent queries connected sources such as Snowflake and Salesforce and reports on the results, using only facts the asker is allowed to see, each traced to its source. Agents also build decks and dashboards on a schedule or when a record changes.

P

PostHog

The in product agent reads events, sessions, cohorts and warehouse tables, writes the SQL and summarizes session replays to answer product questions in the web app or Slack. It saves investigations as notebooks, builds dashboards and alerts, and works from a semantic layer that fixes how each metric is defined.

O

Owkin

K Pro routes each research question to specialized agents for multi omics, pathology, plotting and code execution, running generated code in a sandbox over Owkin's patient datasets joined to the customer's own data. It reaches Benchling, Snowflake and Databricks through MCP and writes results to Spotfire or Tableau.

Picks by what you need most

Best for complex multi step work

T

Tellius

Documents the most on orchestration, memory, and knowledge grounding among the specialists.

Best for regulated and governed deployments

H

Hex

Documents the most on security, auditability, and human oversight among the specialists.

Best for self hosted and sovereign deployment

T

ThoughtSpot

Documents the most on deployment control and model flexibility among the specialists.

Best for developer extensibility

D

Datafold

Documents the most on APIs, MCP support, and tool calling among the specialists.

How they charge

Published prices: 18 of 66

Plans and prices are on the website, and a team can start without a sales call. Examples:

G

GPTBots,

H

Hex,

C

Coworker and

P

PostHog.

Priced by quote: 47 of 66

Sold through a sales team, with pricing set per customer. Examples:

A

Appian,

D

Dataiku,

T

Tellius and

T

ThoughtSpot.

Pricing not published: 1 of 66

No pricing model could be confirmed from public sources.

Compare pricing across the whole index

Where they run

Private cloud: 11 of 66

Runs inside the customer's own cloud account. Examples:

T

Tellius,

C

Coworker,

D

Datafold and

R

Redbird.

On premises or self hosted: 13 of 66

Runs on infrastructure the customer operates. Examples:

A

Appian,

D

Dataiku,

G

GPTBots and

T

Tellius.

The rest run as a cloud service operated by the vendor.

What to check before a shortlist

How it learns your definitions

Revenue, active customer and churn mean different things at different companies. Check where those definitions live: a semantic layer, a set of verified queries, or notes the team writes for the agent.

Whether it shows its work

An answer is easier to trust when the query behind it is visible. Look for the SQL or the steps beside every result, so an analyst can check a number before it reaches a meeting.

Where the data stays

Some products query the warehouse in place. Others copy data into their own store. Teams with strict rules on customer data should check which, and whether rows are sent to a model provider.

Who it is built for

Some products serve analysts and speed up their own work. Others serve everyone else and aim to take questions off the data team's queue. The two need different controls.

Head to head

DataikuvsThoughtSpotHexvsThoughtSpotDatafoldvsDataikuTelliusvsThoughtSpotRedbirdvsZenlyticWisdom AIvsZenlyticAnswerRocketvsTelliusQueriovsZenlyticDefinitevsScoop AnalyticsOpenseevsVantedgeAIScoop AnalyticsvsActian AI AnalystBlazeSQLvsJulius AI

All 67 AI agents for data analysis

Actian AI AnalystAera TechnologyAlloy.aiAnaplanAnswerRocketAppianArchimetisAskEarthAskYourDatabaseAthenic AIBlazeSQLBrewitChordConnecty AICorpusIQCoworkerDatafoldDataikuDatapadDefiniteDefog.aiDomoEmergence AIFabi.aiFinOpslyFirmbaseFlauntGenspark AI WorkspaceGPTBotsHexJet AdminJulius AIKaaj AIKenmeiketteQKyrokLumi AIMoBagelMOGOPLUSNewton ResearchOnetabOpenEvidenceOpenseeOwkinPangeAIParashiftPluto7PluvoPolymrPostHogPotatoPowerdrillProphetQuerioRedbirdScienceMachineScoop AnalyticsShopify SidekickSmart BricksSnowplowSuplariTelliusThoughtSpotVantedgeAIVivox AIWisdom AIZenlytic

Common questions

What are the best AI agents for data analysis?

Hex, Tellius, and ThoughtSpot lead the 33 products built for data analysis tracked by Agentic Index, and Appian, Dataiku, and GPTBots lead the 34 broader platforms that also cover it. The list is ordered by how many of the same 14 capabilities each vendor documents in public sources, and no vendor pays for placement.

How much do AI agents for data analysis cost?

18 of the 66 researched vendors publish their prices, so a team can see plans and start without a sales call. 47 sell by quote through a sales team. Each vendor profile on Agentic Index lists the pricing model and any published prices.

Can AI agents for data analysis run in a private cloud or on premises?

Most run as a cloud service. 11 of the 66 researched vendors document a private cloud option and 13 document an on premises or self hosted option.

How many AI agents for data analysis are there?

Agentic Index tracks 67 AI agents for data analysis, 66 of them with completed capability research. Every one is researched from public sources and listed on this page.