We used Oden to analyze public data from vendor websites, G2 reviews, third‑party pricing benchmarks, and real‑world Reddit discussions so you don’t have to piece this together yourself. If you’re trying to pick between Sigmacomputing (Sigma), Looker, Tableau, and Mode for cloud analytics, you’re probably wrestling with unclear pricing, overlapping feature sets, and conflicting opinions from your data team vs. business users. This guide pulls those sources into one place and focuses on what actually matters: where each platform is strong, where it’s weak, and how to match it to your stack and skills.

Which cloud analytics platform has the best rating?

Based on G2 ratings as of late 2025

All four tools land in a similar band (4.4–4.5), but sample sizes differ a lot: Tableau and Looker have 3k+ and 1.5k+ reviews respectively, versus ~500 for Sigma and ~330 for Mode, so small rating differences may not be statistically meaningful on their own. Source: G2 – Sigma, G2 – Looker, G2 – Tableau, G2 – Mode.

Takeaways

• Mode has the highest average rating (4.5) but the smallest sample, so treat it as a signal that its niche (data‑team‑centric orgs) is very happy, not that it’s categorically “better” than Tableau or Looker. Source: G2 – Mode.
• Tableau and Looker have the most mature review bases, which makes their 4.4 ratings more statistically robust—especially if you’re a large enterprise. Source: G2 – Looker, G2 – Tableau.
• Sigma’s 4.4 over ~500 reviews suggests solid satisfaction despite being newer than Tableau/Looker, particularly around ease-of-use and support. Source: G2 – Sigma.
• Differences of 0.1–0.2 stars are small compared with implementation quality, data modeling, and training, which show up repeatedly as bigger drivers of satisfaction in G2 and Reddit feedback across all four tools. Source: Reddit – Has anyone tried Sigma?, Reddit – Looker v Tableau.

How much do cloud analytics platforms really cost?

Cloud analytics pricing is notoriously opaque. Below is a simplified view using public price lists plus buyer benchmarks. Real deals vary widely by region, usage, contract length, and negotiation.

Cost patterns

• Sigma and Looker both follow the “platform fee + user licenses” model, and real‑world benchmarks show six‑figure ACVs are common once you factor in platform plus dozens of standard/developer or creator seats. Source: PeerSpot – Sigma pricing, PriceLevel – Looker pricing.
• Tableau is the only one with fully transparent, role‑based pricing, which makes budgeting easier but can become expensive for large “Viewer‑heavy” audiences unless you tightly right‑size roles. Source: Tableau pricing page, Explo – Tableau pricing.
• Mode is usually cheaper than a full Looker/Tableau deployment at similar scale, but not “cheap”—median ACVs near $50k/year put it in serious‑tool territory, not “sidecar” spend. Source: Vendr – Mode.
• Warehouse and AI usage are a hidden but material cost driver for Sigma and Looker especially, since both push queries into your cloud data warehouse and Looker now charges for AI data tokens beyond included quotas. Source: Sigma homepage, Looker pricing page.

Always double-check current prices with each vendor's calculator or sales team.

What are the key features of each platform?

Sigma (Sigmacomputing)

Core positioning: Cloud-native BI that lets business users work directly on warehouse data through a familiar spreadsheet, with writeback and AI on top. Source: Sigma homepage, Sigma Computing – Wikipedia.

Key Features:

• Spreadsheet-style interface that reads and writes directly to cloud data warehouses (Snowflake, BigQuery, Redshift), so users analyze live data without exports. Source: Sigma homepage, Sigma – next‑gen analytics announcement.
• Input Tables that create Sigma-managed tables in your warehouse, enabling scenario modeling and operational writeback without code. Source: BusinessWire – Sigma Input Tables.
• AI layer (“Ask Sigma” and AI Query) that invokes LLMs hosted in your warehouse for natural language exploration and AI-powered data apps. Source: Sigma homepage.
• Multi‑modal workflows: spreadsheet UI, SQL, and Python support in one governed environment, with collaborative workbooks and dashboards. Source: Sigma homepage.
• Embedded analytics for putting interactive workbooks and dashboards inside products with iframe-based embedding and permission controls. Source: Sigma embedded trial page.
• Cloud-native architecture where data never leaves the warehouse; Sigma inherits CDW security and governance. Source: Sigma homepage.

Best For:

• Teams standardized on Snowflake/BigQuery/Redshift that want true warehouse‑native BI for business users.
• Organizations trying to move spreadsheet‑heavy workflows (forecasts, plans, operational inputs) into governed cloud analytics.
• Modern SaaS products that need embedded, self‑serve customer analytics without building custom UIs from scratch.
• Data teams who want to empower semi‑technical business users without giving them direct SQL access.

Looker

Core positioning: Semantic‑layer‑driven BI on Google Cloud that centralizes metrics, governance, and embedded analytics for large-scale deployments. Source: Looker for BI, Looker product page.

Key Features:

• LookML semantic modeling layer where analysts define metrics, joins, and permissions once and reuse across dashboards and tools. Source: Looker for BI.
• Strong integration with BigQuery and Google Cloud IAM, plus connectivity to other warehouses and transactional DBs. Source: BigQuery + Looker docs.
• Conversational Analytics powered by Gemini models, letting users ask questions in natural language and receive narrative + visual answers. Source: Looker product page.
• Rich embedded analytics and APIs for building full custom data applications, not just iframed dashboards. Source: Looker product page.
• Multi‑cloud and hybrid support, with Looker hosted on Google Cloud but connecting to data across clouds. Source: Looker docs.

Best For:

• Enterprises on Google Cloud that want a governed semantic layer and tight BigQuery integration.
• Organizations with dedicated data engineering resources to own LookML and centralized data modeling.
• Product and platform teams building custom analytic and AI‑infused applications on top of governed metrics.
• Companies with strict governance/audit requirements across many departments and regions.

Tableau (Cloud)

Core positioning: Fully hosted visual analytics platform with AI‑augmented insights and strong ecosystem, aimed at broad business adoption. Source: Tableau Cloud product page, Tableau product overview.

Key Features:

• Tableau Cloud: fully hosted analytics platform for authoring, sharing, and governing dashboards without managing servers. Source: Tableau Cloud product page.
• Drag‑and‑drop visual analytics with a wide variety of chart types, advanced mapping, and highly customizable dashboards. Source: Tableau product overview, G2 – Tableau.
• Tableau Pulse and Tableau Agent: AI features that surface personalized insights in natural language and assist with calculations and viz creation. Source: What is Tableau Cloud?, Tableau Pulse product page.
• Broad connector ecosystem (cloud data warehouses, databases, SaaS apps) and support for hybrid data via Tableau Bridge and Private Connect. Source: What is Tableau Cloud?.
• Multiple deployment options (Cloud, Server, Next) so organizations can mix fully hosted and self‑hosted depending on compliance and control needs. Source: Tableau product overview.

Best For:

• Organizations that want best‑in‑class visual storytelling and interactive dashboards for business users.
• Teams with existing Tableau skills or a desire to tap into a large talent pool and training ecosystem.
• Companies that don’t want to manage BI infrastructure but still need enterprise‑grade governance and security.
• Use cases where visual polish and stakeholder‑facing storytelling are a top priority.

Mode

Core positioning: Modern BI built around data teams, combining SQL, Python, R, notebooks, and self‑serve dashboards in one platform. Source: Mode homepage, Mode platform page.

Key Features:

• Integrated SQL editor, Python and R notebooks, and visualization layer so analysts can do advanced analysis and publish dashboards in one tool. Source: Mode platform page.
• Support for ad hoc analysis, reusable Datasets, and governed self‑service reporting side‑by‑side. Source: Mode platform page, Mode PR – modern BI.
• Focus on being the “intelligence layer” for the modern data stack—connecting to major cloud warehouses and integrating with dbt’s semantic layer. Source: Mode platform page.
• Custom data apps with HTML/CSS/JS and robust embedding for internal and external analytics experiences. Source: Mode platform page, AWS Marketplace – Mode.
• Free Studio tier for individual analysts and full Business/Enterprise editions for teams with SSO, governance, and support. Source: G2 – Mode.

Best For:

• Data teams that live in SQL/Python/R and want notebooks, charts, and dashboards tightly integrated.
• Organizations already on a modern stack (Snowflake/BigQuery/Redshift + dbt) seeking a collaborative BI layer.
• Mid‑market companies that need strong ad hoc capabilities plus governed dashboards without a massive Looker‑style modeling layer.
• Products or internal tools that benefit from embedded, highly customized analytics experiences.

What are the strengths and weaknesses of each platform?

Sigma (Sigmacomputing)

Strengths:

• Intuitive for spreadsheet users. Multiple G2 reviews and Reddit threads highlight that non‑technical users can quickly become productive because Sigma “feels like Excel” on live warehouse data. Source: G2 – Sigma, Reddit – Has anyone tried Sigma?.
• Fast time-to-value and strong support. Users frequently mention going from zero to embedded dashboards in weeks and praise responsive live chat and onboarding. Source: G2 – Sigma.
• Great fit for Snowflake/BigQuery‑centric stacks. Reviews call out tight Snowflake integration and the ability to leverage warehouse features (e.g., Snowflake Cortex) directly from Sigma. Source: G2 – Sigma, Sigma homepage.
• Democratization vs. curated BI. Reddit users who’ve used Tableau, Looker, and Power BI describe Sigma as better for “democratization” of data access, giving stakeholders governed datasets they can explore themselves. Source: Reddit – Has anyone tried Sigma?.

Weaknesses:

• Performance on very large and complex datasets. G2 reviewers note slow loading and latency issues on big tables or snapshot-style balance data unless heavy modeling/aggregation happens upstream. Source: G2 – Sigma.
• Less sophisticated visualization than Tableau/Domo. Several users say Sigma lacks some advanced chart types, axis tricks, and formatting options they’re used to in more mature viz tools. Source: G2 – Sigma.
• Creator/admin licenses can be pricey. Enterprise buyers report platform fees around $30k/year and ~$1,000 per creator per year, which can limit how many true author seats you provision. Source: PeerSpot – Sigma pricing, Row Zero – Sigma pricing review.

Looker

Strengths:

• Powerful semantic modeling and governance. LookML lets you centralize metrics, joins, and access controls, which large orgs value for consistency and auditability. Source: Looker for BI, BigQuery + Looker docs.
• Excellent for self-service “Explores” on wide datasets. Reddit and G2 users note that compared with Tableau, Looker handles large explores with many attributes better, enabling ad hoc analysis for many business users once models are ready. Source: Reddit – How does Looker compare to Power BI and Tableau?, G2 – Looker.
• Deep Google Cloud integration. Native BigQuery connectivity, IAM integration, and being offered as a managed Google Cloud service make Looker attractive if you’re already standardized on GCP. Source: Looker product page, Looker docs.
• Strong embedded and API story. Looker is widely used for embedded analytics and custom data apps, and Google emphasizes this in product marketing. Source: Looker product page.

Weaknesses:

• Steep learning curve and developer dependency. G2 reviewers and Reddit threads repeatedly mention LookML complexity and the need for specialized data engineers, which slows iteration. Source: G2 – Looker, Reddit – Looker v Tableau.
• Weak visual layer vs. Tableau. Many users complain about limited visualization and dashboard layout options, rigid tiling, and dated UI compared to Tableau and others. Source: Reddit – How does anyone actually use Looker?, Reddit – How does Looker compare to Power BI and Tableau?, Mode blog – Looker vs Tableau vs Mode.
• Performance and stability issues in poorly modeled instances. Some users report slow dashboards and even crashes with relatively simple sales dashboards when modeling or infrastructure isn’t tuned. Source: Reddit – How does anyone actually use Looker?, G2 – Looker.
• High total cost of ownership. Platform plus user licenses plus warehouse and now AI token usage means Looker is often among the more expensive options, especially for smaller orgs. Source: Explo – Looker pricing, PriceLevel – Looker pricing, BetterBuys – Looker pricing.

Tableau

Strengths:

• Best-in-class visualizations and UX. G2 reviews and third‑party comparisons consistently cite Tableau as the strongest for drag‑and‑drop visual analytics, custom layouts, and storytelling. Source: G2 – Tableau, Mode blog – Looker vs Tableau vs Mode.
• Broad adoption and talent pool. Tableau’s long history and ecosystem mean it’s relatively easy to hire experienced developers and find training, templates, and community content. Source: Tableau product overview.
• Clear, published pricing and SKUs. Compared with Looker and Sigma, Tableau’s role‑based per‑user pricing is straightforward and well‑documented. Source: Tableau pricing page.
• Evolving AI features (Pulse, Agent, Tableau Next). Salesforce is investing heavily in AI‑assisted analytics, especially in Tableau Cloud. Source: What is Tableau Cloud?, Tableau Next overview.

Weaknesses:

• Limited built‑in data modeling and ETL. G2 reviewers frequently note that serious data prep must happen upstream; Tableau Prep helps but is not a full semantic layer or transformation engine. Source: G2 – Tableau.
• Performance challenges at scale. Users report dashboards slowing down on very large datasets or complex calculations, especially with live connections. Source: G2 – Tableau, Reddit – Looker to Tableau questions.
• Self-service at very large user counts can be tricky. Reddit discussions describe difficulties maintaining performant, truly self‑serve analytics for thousands of users compared with Looker’s explore model. Source: Reddit – Looker to Tableau questions.
• Per‑user pricing can add up quickly. Even with transparent pricing, a wide Viewer base (hundreds or thousands of users) can make Tableau Cloud expensive without careful license planning. Source: Tableau pricing page, Upsolve – Tableau pricing.

Mode

Strengths:

• Analyst-first workflow. G2 reviews and Mode’s own messaging emphasize that analysts can work in SQL, Python, and R in one environment and then ship dashboards without context switching. Source: Mode platform page, G2 – Mode.
• Combines ad hoc and self-service. Features like Datasets, Visual Explorer, and governed dashboards let Mode serve both one‑off analysis and recurring reporting. Source: Mode platform page, Mode PR – modern BI.
• Strong support reputation. Users frequently praise Mode’s fast, hands‑on support as a differentiator compared with larger vendors. Source: G2 – Mode.
• Flexible embedding and customization. Support for HTML/CSS/JS, parameters, and embedding in internal and external apps is a key selling point. Source: Mode platform page, AWS Marketplace – Mode.

Weaknesses:

• Less friendly for non-technical, drag‑and‑drop‑only users. G2 reviewers note onboarding non‑technical users can be challenging and that Mode is best when there’s a strong analyst team. Source: G2 – Mode.
• Performance issues on very large datasets. Several users report slow chart updates and long load times for user‑level or large‑volume data. Source: G2 – Mode.
• Visualization breadth lags Tableau. Some advanced chart types and fine‑grained formatting options are missing, which power users used to Tableau notice. Source: G2 – Mode.
• Pricing still enterprise-grade. Vendor benchmarks show median ACVs near $49k/year, so Mode is not a “lightweight” or purely budget option. Source: Vendr – Mode.

How do these platforms position themselves?

Sigma (Sigmacomputing). Sigma markets itself as an “Enterprise Intelligence Platform” that lets everyone explore and act on live warehouse data through spreadsheets, dashboards, reports, and AI‑powered applications, with security and performance inherited from the cloud data warehouse. Source: Sigma homepage, Sigma Computing – Wikipedia.

Looker. Google positions Looker as the semantic and governance layer for your business data—“Google for your business data”—emphasizing trusted metrics, AI‑driven Conversational Analytics, and embedded experiences across clouds, with a strong focus on BigQuery and Google Cloud integration. Source: Looker for BI, Looker product page.

Tableau. Tableau describes Tableau Cloud as a “fully-hosted, enterprise-grade analytics platform powered by AI,” highlighting visual analytics, Tableau Pulse for proactive insights, and flexible deployment options to “help people see and understand data” across the organization. Source: Tableau Cloud product page, Tableau product overview.

Mode. Mode brands itself as “modern business intelligence built around data teams” and the “central hub for your organization’s analysis,” focusing on clearing the path from data to insights by uniting data teams and business users in one multimodal platform. Source: Mode homepage, Mode platform page, AWS Marketplace – Mode.

Which platform should you choose?

Below are data-driven, scenario-based recommendations. Assume you have a modern cloud warehouse (Snowflake/BigQuery/Redshift) unless noted.

Choose Sigma If:

1. Your business users live in spreadsheets but your data lives in the warehouse. You want to kill Excel exports without forcing everyone to learn SQL or a new GUI. Source: Sigma homepage, G2 – Sigma.
2. You care more about governed self-service than pixel-perfect dashboards. Stakeholders need to explore data safely; “perfect” visuals are secondary. Source: Reddit – Has anyone tried Sigma?.
3. You’re already invested in Snowflake/BigQuery and want to keep all computation there. Sigma’s live-query model aligns compute and security with your warehouse. Source: Sigma homepage.
4. You have spreadsheet-based operational workflows (quotas, plans, approvals) you’d like to turn into data apps. Input Tables and writeback let you replace fragile Excel models with governed apps. Source: BusinessWire – Sigma Input Tables.
5. You’re comfortable with enterprise pricing if it materially reduces ad‑hoc report backlog. Sigma’s ACVs are similar to other enterprise BI tools, but can free data teams from many reporting requests. Source: PeerSpot – Sigma pricing, Vendr – Sigma.

Choose Looker If:

1. You need a strong semantic layer with tight governance across many teams. Centralized metrics and permissions in LookML are a must-have for your risk/compliance posture. Source: Looker for BI.
2. You’re a Google Cloud/BigQuery shop and want first-class integration. Consolidating on GCP for data, analytics, and identity simplifies your architecture. Source: Looker product page, BigQuery + Looker docs.
3. You plan to build custom data or AI apps, not just dashboards. Looker’s APIs, extensions, and Conversational Analytics make sense if you’re investing in data products. Source: Looker product page.
4. You have (or will hire) a dedicated data modeling team. You’re willing to invest in LookML skills and disciplined modeling in exchange for strong governance and reusable explores. Source: G2 – Looker, Reddit – Looker v Tableau.
5. Budget is less constrained than standardization and scale. You’re okay with higher annual spend if it gives you a single, governed analytics layer across the org. Source: PriceLevel – Looker pricing, Explo – Looker pricing.

Choose Tableau If:

1. Visual storytelling and stakeholder presentations are critical. You need the richest chart library, design control, and pixel-perfect dashboards for execs and clients. Source: G2 – Tableau, Mode blog – Looker vs Tableau vs Mode.
2. You want predictable, transparent per‑user pricing. Role‑based pricing makes planning easier, even if you still negotiate discounts. Source: Tableau pricing page.
3. You’re rolling out BI to a wide audience with mixed technical skills. Tableau Cloud plus Pulse and Agent can support everyone from analysts to execs. Source: Tableau Cloud product page, Tableau Pulse product page.
4. You have or can build basic upstream data models. Your data team can deliver cleaned, modeled tables; you don’t need Looker‑style semantic governance inside the BI tool. Source: G2 – Tableau.
5. You can tolerate some performance tuning effort for large data. You accept that very heavy, live dashboards may need extraction strategies and optimization. Source: G2 – Tableau, Reddit – Looker to Tableau questions.

Choose Mode If:

1. Your analytics team is highly technical and already works in SQL/Python/R. You want a single environment for notebooks, queries, and dashboards. Source: Mode platform page, G2 – Mode.
2. You need fast, flexible ad hoc analysis plus self‑serve dashboards. Analysts do deep work in notebooks and then publish reusable, governed Datasets and reports to business users. Source: Mode platform page, Mode PR – modern BI.
3. You’re on a modern data stack and want a lightweight semantic/gov layer. Mode integrates with dbt’s semantic layer and modern warehouses without requiring a LookML‑style modeling commitment. Source: Mode platform page.
4. Support quality matters more than vendor size. You value responsive, “white‑glove” support over a massive community forum. Source: G2 – Mode.
5. You want strong embedding and internal tooling. You plan to embed analytics in products or internal apps and need customization beyond what iframe‑only tools offer. Source: Mode platform page, AWS Marketplace – Mode.

If you’re still torn, a practical pattern we see is:

• Sigma vs. Tableau: choose Sigma when democratization and warehouse‑native workflows matter more than advanced visual polish; choose Tableau when presentation and visual storytelling are paramount.
• Looker vs. Mode: choose Looker when you need a heavyweight semantic layer and tight GCP alignment; choose Mode when you want analyst speed and flexibility with lighter governance overhead.

Sources & links

Company Websites

• Sigma – Business Intelligence and Analytics Solution
• Looker – Google Cloud product page
• Tableau – Product overview
• Mode – Modern Business Intelligence

Pricing Pages

• Sigma – Free trial and contact
• Looker – Pricing
• Tableau – Pricing
• Mode – Vendr pricing benchmark
• Sigma – Vendr pricing benchmark
• Explo – Looker pricing breakdown

Documentation

• Looker for Business Intelligence
• BigQuery – Analyze data with BI Engine and Looker
• What is Tableau Cloud?
• Tableau Next overview
• Mode platform overview

G2 Review Pages

• G2 – Sigma
• G2 – Looker
• G2 – Tableau
• G2 – Mode

Reddit Discussions

• Has anyone tried Sigma?
• How does Looker compare to Power BI and Tableau?
• Looker v Tableau
• How does anyone actually use Looker?
• Looker to Tableau questions

Additional Resources

• Sigma Computing – Wikipedia
• Mode – Modern BI press release
• Row Zero vs Sigma – Pricing review
• BetterBuys – Looker pricing
• PriceLevel – Looker pricing from actual buyers
• Upsolve – Tableau pricing overview