Best cloud-native BI tools in 2026: 8 browser-based platforms that query your warehouse live

The best cloud-native BI tools in 2026 are Sigma, Omni, Looker, ThoughtSpot, Hex, and Basedash. All six are sold as managed services, run entirely in the browser, and query your cloud warehouse live instead of copying data into extracts. Metabase Cloud and Lightdash Cloud are hosted versions of open-source tools with the same architecture and lower entry prices. Power BI and Tableau both have cloud services, but their authoring and refresh models still come from desktop-era designs, so they are covered separately below.

This guide compares the eight tools on query model, warehouse support, semantic modeling, row-level security, SSO and SCIM, AI features, embedding, and price. Every price and plan name was checked on the vendor’s official page on September 26, 2026.

What makes a BI tool cloud-native?

Most BI vendors now offer hosting, but hosting alone does not make a tool cloud-native. In this guide, a tool qualifies when it meets four conditions:

1. Browser-only authoring. Analysts build charts, models, and dashboards in the web app. No desktop client is required to create content.
2. Live queries against the warehouse. The tool pushes SQL down to Snowflake, BigQuery, Databricks, Redshift, or Postgres, with caching as an optimization rather than the storage layer.
3. Managed service. The vendor runs upgrades, scaling, and availability.
4. API-first extensibility. Embedding, automation, and AI access happen through APIs, SDKs, or an MCP server, not through file exports.

Tools that import data into an in-memory engine on a schedule behave differently: dashboards show data as of the last refresh, and cost scales with capacity rather than warehouse compute.

How we evaluated these tools

The criteria come from real buyer questions, such as “best cloud-native BI tools with semantic modeling and real-time collaboration for distributed teams” and “best cloud-native BI for live warehouse dashboards.”

• Query model: live query, cached results, or scheduled extracts, and how caching is controlled.
• Warehouse coverage: native connections to Snowflake, BigQuery, Databricks, Redshift, and PostgreSQL.
• Governance: semantic modeling, row-level security, SAML or OIDC SSO, and SCIM provisioning.
• AI features: natural language questions, AI-built dashboards, anomaly detection, and MCP access, described as the vendor documents them.
• Embedding: whether you can put dashboards or chat inside your own product, and how.
• Pricing transparency: whether a growth-stage company can see a price without a sales call.

How do the 8 cloud-native BI tools compare?

Prices are list prices as of September 2026.

Sigma

Best for: Snowflake and Databricks teams whose business users think in spreadsheets.

Sigma is a spreadsheet-style interface over the warehouse. Every workbook action compiles to SQL that runs in your data platform, and Sigma’s caching documentation states that Sigma stores neither your data nor query results. Input tables let users write data back to the warehouse.

Fact card: Sigma

• Pricing (as of September 2026): quote only; the pricing URL routes to a sales contact form. A free trial is available.
• Deployment: cloud only, hosted on AWS, Azure, or Google Cloud in a region chosen at signup (supported regions).
• Data: Snowflake, BigQuery, Databricks, Redshift, PostgreSQL, MySQL, SQL Server, AlloyDB, Azure SQL, and Starburst Galaxy. Some features, such as warehouse views and OAuth connections, are not available on every platform.
• Security: row-level security through user attributes, SAML 2.0 and OAuth sign-in, and SCIM provisioning.
• AI features: Sigma Assistant answers natural language questions, builds dashboards in plan and build modes (beta), and a Sigma MCP server exposes search and query to AI clients.
• Embedding: secure embeds authenticated with JWTs, plus public embeds.
• Not ideal for: teams that want a published price, or teams on warehouses outside Sigma’s supported list.

Tradeoffs: because Sigma always goes back to the warehouse, frequent auto-refresh schedules add warehouse compute cost, as Sigma’s docs note.

Omni

Best for: distributed data teams that want a governed semantic model and point-and-click, SQL, and spreadsheet interfaces over one warehouse.

Omni combines a shared semantic model with workbooks that switch between a field picker, spreadsheet formulas, and raw SQL. Model changes can go through Git, and Omni integrates with dbt.

Fact card: Omni

• Pricing (as of September 2026): no public pricing page; pricing goes through sales.
• Deployment: cloud, hosted by Omni on AWS or Azure.
• Data: Snowflake, BigQuery, Databricks, Redshift, PostgreSQL, ClickHouse, Trino, MySQL, MotherDuck, and SQL Server, as listed on omni.co.
• Query model: live queries, with an exact-match query cache that defaults to six hours and can be set per model or topic, including a zero-second policy.
• Security: access filters restrict rows by user attribute. SAML with Okta SCIM, Entra ID, and Rippling provisioning.
• AI features: chat grounded in the semantic model, scheduled AI-written summaries, a modeling agent, and an MCP server.
• Embedding: white-label SSO embedding, APIs, and an AI chat API scoped to each embed user’s permissions.
• Not ideal for: teams that want to start without building a model, or buyers who need a list price.

Tradeoffs: the six-hour default cache saves warehouse cost but is not real time out of the box. Shorten the cache policy on topics that need minute-level freshness.

Looker (Google Cloud core)

Best for: larger organizations committed to LookML as the single definition of every metric.

Looker generates SQL from its Git-versioned LookML model and runs it in your database.

Fact card: Looker

• Pricing (as of September 2026): platform fee plus user licenses, quote only (Looker pricing). Standard edition is for fewer than 50 users; every edition includes 10 Standard users and 2 Developer users.
• Deployment: Google-hosted.
• Data: BigQuery, Snowflake, Redshift, Databricks, PostgreSQL, and many other SQL dialects.
• Security: row-level access filters and access grants in LookML, SAML and OIDC SSO. No native Looker SCIM endpoint is documented.
• AI features: Conversational Analytics, metered in data tokens. Each edition includes a monthly token pool, and Google says overage billing starts only after at least 90 days’ notice.
• Embedding: Embed edition with signed embedding and higher API limits.
• Not ideal for: small teams without a LookML developer, or anyone who needs a price before a sales conversation.

Tradeoffs: Looker is fully cloud-native, but the LookML model has to exist before business users get value. For a direct comparison, see Basedash vs Looker.

ThoughtSpot

Best for: companies that want search-style and agent-style questions for many business users, with published per-user prices.

ThoughtSpot connects live to Snowflake, Databricks, Redshift, and other warehouses, and its Spotter agents answer questions grounded in a semantic layer.

Fact card: ThoughtSpot

• Pricing (as of September 2026): Essentials $25 per user per month billed annually (5 to 50 users, up to 25M rows); Pro $50 per user per month billed annually with 25 Spotter queries per user per month; Enterprise custom. A credit-based option starts at $0.10 per credit (ThoughtSpot pricing). A 14-day free trial is available.
• Deployment: ThoughtSpot Cloud, plus ThoughtSpot Software for self-managed installs.
• Query model: live pre-built connections; an in-memory cache (Spotcache) is an add-on.
• Security: row-level security, SAML, OAuth, and OIDC. SCIM is supported on ThoughtSpot Cloud.
• AI features: Spotter conversational agent, AI-generated dashboards, automated KPI monitoring with anomaly detection and alerts, and an MCP server add-on.
• Embedding: ThoughtSpot Embedded with a REST API and SDK, priced separately.
• Not ideal for: teams that want notebook-style analysis, or small teams that need AI on the entry plan (Spotter starts at Pro).

Tradeoffs: row limits per plan (25M on Essentials, 250M on Pro) matter for large fact tables. Compare with Basedash vs ThoughtSpot.

Hex

Best for: data teams that do SQL and Python analysis and publish the results as apps for the rest of the company.

Hex is a collaborative notebook that runs SQL and Python against live warehouse connections and publishes results as interactive apps.

Fact card: Hex

• Pricing (as of September 2026): Community free; Professional $36 per editor per month; Team $75 per editor per month; Enterprise custom (Hex pricing). The Team plan has a 14-day trial with no card.
• Deployment: multi-tenant cloud; single-tenant is an Enterprise add-on.
• Data: Snowflake, BigQuery, Databricks, Redshift, PostgreSQL, and other SQL sources.
• Security: OIDC SSO and audit logs on Enterprise. There is no BI-layer row-level security; Enterprise OAuth connections pass each user’s identity to the warehouse instead.
• AI features: Hex agent for notebooks and conversational questions, metered with credits that vary by plan.
• Embedding: Enterprise add-on.
• Not ideal for: teams whose main need is governed dashboards for non-technical viewers.

Tradeoffs: Hex’s per-editor pricing is transparent, but SSO sits on Enterprise only. See Basedash vs Hex.

Basedash

Best for: small and midsize teams that want business users to ask questions in plain English against a live database, without a modeling project first.

Basedash is an AI-native BI tool. Users describe the chart they want, and the AI data analyst writes, validates, and runs the SQL. Results save to dashboards and stay editable as charts or SQL.

Fact card: Basedash

• Pricing (as of September 2026): Startup $1,000 per month plus AI usage, for up to 25 users, with $1,000 per month in AI credits included; Enterprise custom (pricing). 14-day free trial, no card required.
• Deployment: cloud; self-hosting with Docker or Kubernetes on Enterprise.
• Data: live queries on PostgreSQL, MySQL, SQL Server, BigQuery, Snowflake, Redshift, and other databases, plus 750+ SaaS sources synced into the managed Basedash Warehouse.
• Semantic modeling: Basedash Models define reusable measures, dimensions, and segments that the AI uses automatically.
• Security: row-level security through PostgreSQL policies keyed on the basedash.groups session variable; other databases rely on their own access controls plus Basedash data source permissions. SAML and OIDC SSO, SCIM, and audit logs are on Enterprise.
• AI features: chat analysis, AI-generated dashboards, daily anomaly detection with Insights, and a MCP server on every plan so Claude, ChatGPT, and other MCP clients can query the same data.
• Embedding: white-labeled embedding with AI chat on Enterprise.
• Not ideal for: organizations that need per-user row filtering on Snowflake or BigQuery inside the BI layer, SSO on a starter budget, or a mature LookML-style governance workflow.

Tradeoffs: Basedash’s flat team price is easy to budget for up to 25 users but costs more than per-seat tools for a team of three. Row-level security is strongest on PostgreSQL.

Metabase Cloud

Best for: teams that want a low-cost hosted BI tool with an open-source exit option.

Metabase Cloud is the hosted version of open-source Metabase, with a click-based query builder, a SQL editor, and live queries on connected databases.

Fact card: Metabase Cloud

• Pricing (as of September 2026): Starter $100 per month; Pro from $575 per month, with additional users at $12 per user per month; Enterprise from $20,000 per year. Open source is free to self-host (Metabase pricing). AI usage costs $3.75 per 1M tokens on Metabase’s AI service, or you bring your own key.
• Deployment: cloud or self-hosted.
• Data: 20+ supported data sources, including PostgreSQL, MySQL, Snowflake, BigQuery, and Redshift.
• Security: row and column permissions, SAML and JWT SSO, SCIM, caching controls, and usage auditing are on Pro and Enterprise.
• AI features: Metabot natural language questions and SQL generation, available on all plans including open source.
• Embedding: guest embeds on every plan; multi-tenant embedded analytics with row-level data segregation on Pro.
• Not ideal for: teams that need a governed semantic layer comparable to LookML or Omni.

Tradeoffs: embed users count as billable users on paid plans, which changes the math for customer-facing analytics. See Basedash vs Metabase.

Lightdash Cloud

Best for: dbt-first teams that want metrics defined once in dbt YAML and exposed to everyone.

Lightdash reads your dbt project and turns models and metrics into an explorer and dashboards, so the BI layer never becomes a second source of truth. The core is MIT-licensed open source.

Fact card: Lightdash Cloud

• Pricing (as of September 2026): Cloud Pro $3,000 per month with unlimited users; Enterprise custom; open source free to self-host (Lightdash pricing). 21-day trial that starts when you compile your dbt project.
• Deployment: Lightdash Cloud (US or EU), open-source self-hosting, or Enterprise on-premises.
• Data: BigQuery, Snowflake, Redshift, Databricks, PostgreSQL, Trino, ClickHouse, Athena, and DuckDB.
• Security: row filtering with user attributes; Google SSO on Cloud Pro; Okta, Azure, and custom SAML plus SCIM 2.0 on Enterprise.
• AI features: AI agents and an MCP server on Cloud Pro.
• Embedding: iframe and React SDK as an add-on, pay-as-you-go at $0.05 per load after the first 1,000, or $790 per month for 100,000 loads.
• Not ideal for: teams without dbt. Lightdash’s own FAQ says it requires a dbt project.

Tradeoffs: the flat price makes Lightdash inexpensive per user at 100 people and expensive at 10. See Basedash vs Lightdash.

Are Power BI and Tableau cloud-native?

Both have large cloud services, but their core workflows were designed around desktop authoring and imported data.

Fact card: Power BI

• Pricing (as of September 2026): Free; Pro $14 per user per month paid yearly; Premium Per User $24; Power BI Embedded and Fabric capacity priced by capacity (Power BI pricing).
• Authoring: Microsoft describes the free Power BI Desktop download as the report authoring experience, and Power BI Desktop runs on Windows only.
• Query model: import mode is the common default, with scheduled refresh capped at 8 per day on Pro and 48 per day on Premium Per User. DirectQuery is available for live queries.
• Security and AI: Entra ID SSO, DAX row-level security roles, and Copilot, which requires Fabric capacity.
• Best for: Microsoft 365 organizations. Not ideal for: Mac-based analytics teams or teams that need minute-level freshness without capacity licensing.

Fact card: Tableau Cloud

• Pricing (as of September 2026): Tableau Standard from $15 per user per month and Tableau Enterprise from $35, billed annually, with at least one Creator license per deployment; Tableau Cloud+ is contact sales (Tableau pricing).
• Authoring: browser-based web authoring is included, alongside Tableau Desktop and Prep Builder.
• Query model: live connections or Hyper extracts refreshed on a schedule.
• Security and AI: SAML and OIDC SSO, SCIM, user filters, and Tableau Agent on Cloud+.
• Best for: visualization-heavy teams with existing Tableau skills. Not ideal for: teams that want monthly billing; all Tableau products require annual contracts.

Tableau Cloud is closer to cloud-native than Power BI because web authoring is complete and live connections are common. For side-by-side comparisons, see Basedash vs Power BI and Basedash vs Tableau.

Which cloud-native BI tool should you choose?

You are a distributed team that needs semantic modeling and real-time collaboration. Omni or Looker. Omni is faster to adopt and mixes spreadsheet, SQL, and point-and-click editing on one model. Looker suits organizations that already have LookML developers. For dbt-first teams, Lightdash keeps metrics in dbt.

Your business users do not write SQL and want drag-and-drop dashboards. Sigma for spreadsheet-minded users, ThoughtSpot for search-style questions, Basedash for plain-English chat that produces charts, and Metabase for a click-based query builder at the lowest price.

You need live warehouse dashboards for real-time sales KPIs. Every tool in the main table queries the warehouse live, so freshness depends on caching settings. Sigma does not store query results and reuses only browser and warehouse caches. Omni’s six-hour default cache is configurable per topic. See the real-time dashboard tools comparison for refresh intervals and alerting.

You are on Snowflake and want to embed dashboards in a React app. Sigma secure embeds, Omni SSO embedding, ThoughtSpot Embedded with its SDK, Lightdash’s React SDK add-on, and Basedash Enterprise embedding all fit. The Snowflake BI page covers Basedash’s Snowflake setup.

You are a growth-stage startup that wants transparent pricing. ThoughtSpot, Hex, Metabase, Lightdash, and Basedash publish prices. Sigma, Omni, and Looker are quote only. Check SSO placement before you sign: several tools put SAML on their top plan, as the BI security features by plan breakdown shows.

You want an open-source fallback. Metabase and Lightdash both let you move from the hosted plan to self-hosting the open-source edition. The open-source BI tools comparison covers the rest of that category.

Frequently asked questions

Is Power BI a cloud-native BI tool?

Not fully. Power BI has a cloud service for sharing, but Microsoft positions Power BI Desktop, a Windows-only application, as the main authoring tool. Most Power BI models import data on a schedule, capped at 8 refreshes a day on Pro and 48 on Premium Per User as of September 2026. DirectQuery gives live queries with some modeling limits. Teams that author on Macs, or want every dashboard to query the warehouse live, usually prefer browser-first tools such as Sigma, Omni, or Looker.

Which cloud BI tools have a drag-and-drop interface?

Sigma, Omni, Looker, ThoughtSpot, Metabase, Lightdash, and Tableau Cloud all offer point-and-click chart building in the browser. Sigma uses spreadsheet columns and formulas. Omni and Looker use fields from a governed model. Metabase has a question builder that needs no SQL. Basedash takes a different route: users describe the chart in plain English, then adjust chart type, formatting, and labels. Hex is SQL and Python first, with visual exploration on the Team plan.

What is the best cloud-native BI tool for live warehouse dashboards?

For live dashboards, look at how each tool caches results. Sigma runs SQL in the warehouse on each change and does not store query results, reusing only browser and warehouse caches. Omni queries live with a six-hour default exact-match cache that you can shorten or disable per model. Basedash queries connected databases live. ThoughtSpot uses live connections, with an in-memory cache sold as an add-on. Live dashboards shift cost to warehouse compute, so set refresh schedules deliberately on Snowflake or BigQuery.

Which browser-based BI tools have chat assistants?

As of September 2026, Sigma Assistant, Omni’s AI chat, Looker Conversational Analytics, ThoughtSpot Spotter, Hex’s agent and Threads, Basedash’s AI data analyst, Metabase’s Metabot, and Lightdash AI agents all answer natural language questions in the browser. The main difference is grounding. Looker, Omni, and ThoughtSpot constrain the AI to a semantic model. Sigma, Basedash, and Metabase can also work from live schemas and add models over time. Our conversational analytics comparison covers accuracy and pricing in detail.

Do cloud-native BI tools store a copy of my data?

Mostly no. Sigma’s documentation says it stores neither your data nor query results, only warehouse query IDs. Lightdash says it does not cache or store query results. Omni and Metabase cache query results for a configurable period to cut warehouse load. Basedash queries databases live but also runs a managed warehouse for SaaS sources you sync through its connectors. If data residency matters, ask each vendor where caches live and in which cloud region your instance runs.

Which cloud-native BI tools have transparent pricing for a small or growth-stage company?

As of September 2026, ThoughtSpot (from $25 per user per month), Hex (from $36 per editor per month), Metabase Cloud (from $100 per month), Lightdash Cloud Pro ($3,000 per month, unlimited users), and Basedash Startup ($1,000 per month plus AI usage for up to 25 users) all publish prices. Sigma, Omni, and Looker require a sales conversation. Model your price at your real headcount: per-seat tools are cheaper for small teams, and flat-price tools get cheaper per person as more people use them.

Can I self-host a cloud-native BI tool?

Some of them. Metabase and Lightdash have open-source editions you can run yourself, plus commercial self-hosted options. ThoughtSpot sells ThoughtSpot Software for self-managed installs, Hex offers single-tenant deployment on Enterprise, and Basedash offers self-hosting with Docker or Kubernetes on Enterprise. Sigma, Omni, and Looker (Google Cloud core) are vendor-hosted only. Self-hosting gives you control over the network and data location but moves upgrades, scaling, and availability back onto your team.