5 Best Reporting Tools for 2025

In the era of data-driven decision making, reporting tools have become essential for businesses of all sizes. These platforms turn raw data into actionable insights, helping companies track performance, identify trends, and make informed decisions quickly.

From small startups monitoring key metrics to large enterprises consolidating global data, a good reporting tool enables teams to visualize information and share it in real time.

In fact, modern organizations rely on BI (Business Intelligence) tools to such an extent that over 350,000 businesses now use Microsoft Power BI alone.

The right reporting solution can improve efficiency by reducing manual data work, increasing transparency, and focusing discussions on solutions rather than debating numbers.

What to Look for in a Reporting Tool

Choosing a reporting tool in 2025 means evaluating several critical factors. Not all BI platforms are the same, so consider the following when making your decision:

• Data Source Connectivity: Ensure the tool supports the data sources you use – from spreadsheets and databases to cloud services.

  Leading platforms like Power BI and Tableau boast 100+ native connectors to databases (SQL Server, Oracle, Snowflake, etc.), cloud apps, and files.

  A broader range of connectors means you can easily bring all your data together without complex IT work.

• Visualizations & Dashboards: Look at the visualization capabilities. The best tools offer interactive charts, graphs, maps, and tables with rich formatting.

  Features like drag-and-drop dashboard design and drill-down interactivity help turn data into clear insights.

  For example, Tableau is renowned for its advanced, dynamic visualizations and rich chart library, while newer tools like Daymark automatically choose chart types (bar, line, table, etc.) to best answer your question.

• AI & Automation Features: Modern reporting tools increasingly incorporate AI-powered analytics. This can include natural language query (ask questions in plain English), automated insight generation, and predictive analytics.

  Microsoft’s Power BI has AI features like Q&A and the Copilot assistant to generate visuals from queries, and Tableau includes AI-driven insights via its Tableau GPT/Next initiatives.

  AI can save time by automatically surfacing trends or creating reports with minimal effort.

• Ease of Use: The ideal tool balances power with user-friendliness. Consider the skill level required – is it aimed at business users or does it need technical expertise?

  A tool like Daymark focuses on non-technical users, letting anyone type questions to get charts without knowing SQL.

  Metabase offers a simple GUI and even a no-code query builder, whereas Looker requires some technical knowledge (LookML modeling) for setup.

  If your team has limited analytics expertise, prioritize a tool known for a gentle learning curve and strong UI.

• Community & Support: A large user community and robust support resources can be invaluable.

  Established tools (Power BI, Tableau) have extensive online forums, tutorials, and third-party content, making it easier to find help.

  Open-source tools like Metabase have active community forums.

  Also consider official support options – enterprise-focused platforms might offer dedicated account managers or support packages (as Domo does for paid plans).

• Scalability and Pricing: Finally, fit the tool to your budget and scale requirements.

  Pricing models vary widely – from per-user subscriptions (e.g. Tableau at $75+/month for a Creator license, or Power BI Pro at $14 per user/month) to enterprise contracts (Looker can start around $60k/year) or usage-based pricing (Domo’s pay-for-what-you-use model).

  Also consider if the tool can scale with your data and users: many offer cloud hosting for virtually unlimited scale, while others also allow on-premises deployments (Tableau, Power BI Report Server, etc.).

  The goal is to find a solution that provides value for its cost and can grow with your business.

Keeping these factors in mind will help you select a reporting tool that aligns with your data sources, team skillset, and business goals. Next, we’ll explore the five best reporting tools for 2025 and see how they stack up.

5 Best Reporting Tools for 2025

Based on features, user feedback, and industry trends, here are five top reporting platforms to consider in 2025. Each tool is described with its key features, pricing model, and why it might be the right choice for certain users or organizations.

1. Microsoft Power BI

Power BI is a leading BI platform from Microsoft that offers both self-service analytics for business users and robust tools for data professionals. It integrates tightly with the Microsoft ecosystem (Excel, Azure, Teams, etc.) and is known for its affordable pricing and ease of use.

• Key Features: A comprehensive suite for creating and sharing interactive dashboards. Power BI provides a drag-and-drop report designer and over 100 secure data connectors (to databases, cloud services, files) for advanced data connectivity.

  It has powerful data modeling with DAX (useful for complex calculations), and supports collaboration through cloud workspaces.

  Notably, Power BI includes AI-driven features like the Q&A visual (type a question and get an answer visualized) and the new Copilot which can generate reports or DAX formulas via natural language.

  It also offers mobile apps and can embed visuals in other applications easily.

• Pricing: Power BI stands out for its low cost. Power BI Desktop is free for individual analysis.

  For sharing and collaboration, a Power BI Pro license is ~$14 per user/month (recently increased from $10), which allows creating and sharing dashboards with others.

  There’s also Premium options: Premium Per User (PPU) at ~$24 per user/month for advanced features (larger data capacity, AI, etc.), and Premium per capacity (starting around $4,995/month for dedicated infrastructure) for enterprise-scale deployments.

  Microsoft’s pricing is very competitive; even with the 2025 price hike, Power BI remains one of the most cost-effective BI tools.

• Why It’s a Good Choice: Power BI delivers a balance of power and affordability.

  It’s praised for its user-friendly interface (ideal for beginners) and tight integration with Excel and Office 365 (great for organizations already using Microsoft products).

  The community is massive, offering plenty of tutorials and plug-in visuals. It’s also continually updated monthly with new features.

  Analysts frequently rank Power BI as a leader in the BI space, citing its innovation and the strength of its user community.

  The downside can be its Windows-centric nature (Desktop is Windows-only) and that advanced use (large datasets, AI features) may require the Premium tier.

• Who It’s Good For: Power BI is a solid choice for almost any size team.

  Small businesses and startups appreciate that they can start with the free Desktop and then pay just ~$14 per user for Pro to share reports – a very low entry cost.

  It’s also ideal for departments or mid-size companies that want quick analytics wins without a big BI budget.

  Enterprises benefit if they are already on Microsoft 365/Azure, as Power BI fits naturally with their infrastructure.

  Essentially, if you need an affordable, easy-to-learn tool that still offers advanced capabilities, Power BI should be on your shortlist.

2. Tableau

Tableau is often synonymous with interactive data visualization. Now part of Salesforce, Tableau has long been a top choice for analysts due to its ability to create beautiful, interactive visualizations and its flexibility in handling data. It’s a mature platform with options from individual use to enterprise deployments.

• Key Features: Tableau provides a rich, intuitive interface to explore data.

  Users can connect to over 100 data sources (spreadsheets, SQL databases, big data platforms, cloud apps) with native connectors, then create dashboards by dragging and dropping fields to see instant visual feedback.

  It excels at visual analytics – you can filter, drill-down, and highlight data directly on charts.

  Tableau’s visuals are highly customizable and renowned for their quality (great for storytelling with data).

  It supports geographical mapping, sophisticated calculations, and has recently integrated AI-driven insights (Tableau GPT/Einstein) that automatically explain trends and make predictions.

  Tableau can be deployed either on Tableau Cloud (hosted) or on-premises (Tableau Server), giving flexibility for different IT needs.

  It also has a separate data prep tool (Tableau Prep) included with certain licenses.

• Pricing: Tableau is sold via subscription licenses.

  For Tableau Cloud (hosted version), the main roles are: Creator at $75 per user/month (billed annually) which includes full authoring capabilities (and a Tableau Desktop license), Explorer at ~$42/user/month (for users who can edit existing reports and do some authoring), and Viewer at $15/user/month (for read-only access).

  On-premises Tableau Server licensing is similar. There's a minimum of one Creator required per deployment.

  Enterprise plans (Tableau Data Management, advanced governance) can raise costs further (e.g. an Enterprise Creator license ~$115/user/month with extras).

  In summary, Tableau's licenses are significantly pricier than Power BI's – a small team of 5 Creators could spend ~$4,500/year.

  However, there is a free public version (Tableau Public) for non-confidential use, and a trial is available.

• Why It's a Good Choice: Tableau's strength is cutting-edge visualization and analytics depth.

  It's often the tool of choice when deep data exploration and interactive dashboards are a priority.

  Users love how quickly you can generate insights by just visualizing data in different ways.

  Tableau is also highly flexible in deployment – you can run it fully on-premises if data security requires, or in cloud, or even fully offline for individual analysts.

  The platform includes robust governance features, making it suitable for enterprise-wide implementations with many users and strict data control.

  The trade-offs are that it has a steeper learning curve than some tools (mastering all its features can take time), and cost can become high as you scale users.

• Who It's Good For: Medium to large organizations that need powerful analytics and have the budget for a premium tool.

  It's ideal for companies that prioritize data visualization quality – e.g. analytics teams, data scientists, and business intelligence departments in finance, marketing, etc., who want to dive deep into data relationships.

  Many Fortune 500 enterprises standardize on Tableau for its enterprise features and support.

  Smaller businesses or teams can use Tableau too (even a single analyst can harness it), but the higher cost per user might be overkill if you only need basic reporting.

  If you have a dedicated analytics team and want to empower them with one of the most robust BI tools on the market, Tableau is a top contender.

3. Looker (Google Looker)

Looker is an enterprise-grade BI platform that is now part of Google Cloud. It’s unique in its approach: Looker uses a centralized data modeling layer (LookML) which allows teams to define business metrics in one place and then create consistent dashboards and reports from that single source of truth. Looker is cloud-native and often praised for its governed approach to analytics.

• Key Features: Looker's hallmark is LookML, a YAML-like modeling language where analysts define data relationships and business logic (like revenue definitions, KPIs) in code. This model is then used to generate queries under the hood, ensuring everyone is using the same calculations.

  The front-end allows creating dashboards and exploring data with a web-based interface – users can slice and dice data, but always within the guardrails of the predefined model. This makes Looker great for data governance and for preventing "multiple versions of the truth."

  It also supports embedded analytics, letting companies embed Looker charts into their own applications. Integration-wise, since it's on Google Cloud, Looker connects naturally to Google BigQuery and other SQL databases; it's optimized for modern cloud data warehouses.

  It also offers features like Git integration (for version-controlling your LookML code), and an API for custom applications.

• Pricing: Looker is known to be one of the more expensive BI tools – its pricing is not publicly listed and is typically quote-based via sales.

  Reports indicate Looker starts around $35K–$60K per year for small deployments. Pricing depends on factors like number of users (they often package a certain number of users in base price), the scale of data, and add-ons.

  According to a 2025 analysis, the standard Looker plan (for less than 50 users) starts around $60,000/year and the average enterprise spends ~$150,000/year. Additional "viewer" users or developer users cost extra in some models.

  In short, Looker is a significant investment primarily aimed at mid-to-large enterprises. (Google has another product Looker Studio, formerly Data Studio, which is free – but that is a separate, more lightweight tool).

• Why It's a Good Choice: For organizations that require strong data governance and single source of truth, Looker is excellent. The central modeling means metrics are consistent across the company – e.g., if "customer lifetime value" is defined once in LookML, every report uses that same definition. This is a big advantage in large companies where ad-hoc reports can otherwise lead to conflicting numbers.

  Looker is also highly extensible – companies use it to build entire data applications and custom embedded analytics for their customers. It's fully web-based (no desktop software), which eases deployment and collaboration.

  Another plus is that it's cloud-native and scalable, handling big data volumes especially when paired with powerful databases.

  However, Looker requires technical expertise to set up and maintain (you need data analysts who can write LookML and manage the model). Casual business users might find it less immediately intuitive compared to drag-and-drop tools, since much is pre-modeled behind the scenes.

  The high cost also means ROI comes if you truly leverage the governance and embedding features.

• Who It's Good For: Large enterprises or data-centric organizations. Looker is best for companies that have a dedicated data team and want to enforce consistent metrics across many departments.

  Sectors like tech, e-commerce, gaming, and SaaS companies have embraced Looker for its flexibility in embedding analytics into products and its ability to define metrics in code.

  It's not typically for small businesses – smaller teams are better served by Looker Studio or other cheaper tools. In fact, a common view is "Looker is for enterprises requiring deep data modeling and embedding, whereas Looker Studio (free) is for small to medium businesses with simpler needs."

  So if you are, say, a rapidly growing company dealing with complex data and want a scalable, governed BI platform (and can budget six figures annually), Looker is a prime candidate.

4. Domo

Domo is a cloud-based end-to-end BI and analytics platform that differentiates itself by offering an all-in-one solution: data integration, analytics, and even app development in one place.

Domo's focus is on enabling business users to get insights quickly, with a user-friendly interface and a vast library of connectors and pre-built content.

• Key Features: Domo shines in data integration – it has over 1,000 pre-built connectors to cloud services (from Salesforce and Google Analytics to social media APIs), making it easy to bring in data without custom ETL.

  It includes Magic ETL (a drag-and-drop data pipeline builder) and SQL dataflow tools for transforming and blending data inside the platform.

  The visualization layer allows creation of interactive cards and dashboards with a variety of chart types.

  Domo also has strong collaboration features – users can comment on cards, share alerts, and view real-time metrics on mobile.

  Another modern feature is Domo AI: the platform has introduced AI and machine learning capabilities, like natural language queries and an AI chatbot, as well as predictive analytics models that users can apply to their data.

  Additionally, Domo supports building custom apps on the platform (using its low-code App Studio or even custom coding) for specific workflows.

  Essentially, Domo attempts to be a one-stop shop for data needs – from connecting data, preparing it, visualizing, to taking action.

• Pricing: Historically, Domo's pricing was not transparent (enterprise-style negotiations).

  As of 2025, Domo uses a consumption-based pricing model. They offer a 30-day free trial with full features.

  After that, there's a paid plan that is pay-as-you-go – you are charged based on usage factors like the number of queries, volume of data, and connectors used.

  Domo no longer publishes exact prices; you need to talk to sales for a custom quote.

  However, research gives ballpark figures: mid-sized companies often spend ~$20,000 to $50,000 per year on Domo, and enterprise deployments can range from $50,000 up to $100,000+ annually.

  Domo's model also typically involves a per-user license (around $750/user/year as a base) plus the consumption credits.

  Notably, every user in Domo is full-featured (there isn't a cheaper "viewer-only" license; all users cost the same).

  This means you pay for each user and the data usage, but you can theoretically have many users share a pool of credits.

  In short, Domo is generally considered a premium-priced solution, especially for larger teams, despite the flexible scaling.

• Why It's a Good Choice: The key reason to choose Domo is its convenience and all-in-one nature.

  For organizations that don't want to stitch together separate tools for ETL, data warehouse, and BI, Domo provides a unified platform.

  The huge number of data connectors (1000+) is a big selling point – you can connect almost any data source quickly.

  Business users find Domo's interface approachable; it emphasizes quick dashboard creation and has a social media-like feel (buzz, comments, etc.) to drive adoption.

  Domo can be deployed very rapidly, sometimes in days, to start delivering value, because it handles storage and processing behind the scenes in its cloud.

  It's also highly scalable for enterprise in terms of data volume and user count, as evidenced by their references of large deployments.

  With new AI features, Domo is keeping up with trends by allowing non-technical users to ask questions or get alerts from the data automatically.

  The trade-offs include the cost (it can get expensive as you scale usage, and budgeting is tricky under usage-based billing) and a less flexible architecture (since everything lives in the Domo ecosystem, there can be some lock-in).

  Also, while Domo is great for quick analytics, extremely complex or specialized analytics might still be better served by more open tools or direct SQL on a warehouse.

• Who It's Good For: Domo is well-suited for business executives and teams that want fast insights without a heavy IT investment.

  It's popular in use cases like executive dashboards, marketing analytics, and sales operations, where many cloud data sources need to be aggregated and updated in real-time.

  A small or mid-size company that lacks a big data engineering team can use Domo to quickly get a full BI solution running.

  Companies with lean IT appreciate that Domo is cloud-hosted and fully managed.

  It's also used in large enterprises for departmental solutions (though enterprise-wide Domo usage will come at a high cost).

  If your organization values ease of use, quick deployment, and has a broad range of data sources to integrate (and you're willing to invest accordingly), Domo can be a powerful choice.

  However, very small businesses with simple BI needs might find Domo's cost prohibitive compared to simpler tools, and very large tech-savvy organizations might opt for building their own data stack.

  For the many in between – Domo provides a nice balance of self-service capability and robust platform.

5. Daymark

Daymark is a newer entrant that takes an AI-first approach to reporting.

It's designed to let users simply ask questions in plain English and get answers from their data – in the form of charts, summaries, or dashboards – without any technical setup.

In essence, Daymark aims to remove the typical barriers of BI tools (like needing to learn a BI interface or SQL) by using natural language processing.

• Key Features: The standout feature of Daymark is its natural language query interface.

  You type a question as you would to a colleague (e.g., "What were our sales last quarter by region?") and Daymark translates it into the necessary data query, returning the answer as an interactive chart or table.

  This is all done through a simple search-bar-like interface.

  Each result comes with an appropriate visualization automatically chosen (bar chart for comparisons, line for trends, etc.), and you can refine or follow up with additional questions.

  Daymark also allows users to save these Q&A results into dashboards for ongoing tracking.

  It connects to common data sources: notably SQL databases, data warehouses, spreadsheets (e.g., Google Sheets), and certain business applications – basically, "if you can query it, we can connect to it," including CSV uploads.

  Setup is cloud-based and very quick: you link your data source and can start asking questions in minutes.

  Collaboration features include sharing answers and dashboards with teammates so that everyone is on the same page with the data.

  In summary, Daymark focuses on simplicity and speed: no code, no complex UI – just ask and answer.

• Pricing:

  Daymark offers a free option – you can Get Started for Free with no credit card required.

• Why It's a Good Choice: Daymark is an excellent choice for non-technical users or teams without a dedicated data analyst.

  Its promise is that "anyone can analyze data" and get instant answers without training.

  This can dramatically improve data-driven decisions in teams that previously might wait days for an analyst to make a report.

  Daymark also eliminates the need to build complex dashboards upfront – you can ad-hoc ask what you need, which is great for exploration and quick checks.

  By consolidating search across all your connected sources, it saves time of figuring out where a metric is stored.

  The collaborative and shareability aspect means once useful insights are found, they can be turned into always-up-to-date dashboards for others.

  Daymark's AI-first design is aligned with the trend of BI tools becoming more conversational and proactive.

  Essentially, it brings an Alexa or Google Assistant-like ease to your company's data.

  The potential downside is that, as a newer product, it might not yet have the breadth of advanced features that mature BI suites do – for example, highly complex data transformations, custom visualizations, or on-premises deployment options.

  It's also still evolving its integration list (it works with many popular sources, but not every niche tool yet).

  That said, Daymark can serve as a complement to traditional BI – enabling quick independent analysis – or as a primary BI tool for organizations that need speed and simplicity above all.

• Who It's Good For: Daymark is perfect for small and medium businesses, startups, or any team that wants analytics without overhead.

  If you're a manager or executive who just wants to get answers from data without learning SQL or waiting on reports, Daymark is built for you.

  It's also useful in larger enterprises for business units that need agile data insights without burdening the BI team – e.g., a sales manager could directly query customer data for trends.

  The Daymark website notes it's for "anyone who needs to make decisions based on data – from executives tracking metrics to product managers and sales leaders".

  This broad applicability underscores that it's about empowering individual decision-makers.

  Team-wise, Daymark can be leveraged by teams that lack a full BI infrastructure; you just plug in your database or upload a CSV and off you go, making it accessible for companies that find tools like Tableau or Looker too resource-intensive.

  In summary, for user groups that value quick answers and ease of use (and may have been intimidated by traditional BI software), Daymark is an ideal modern solution.

Bonus: Metabase (Open-Source Analytics)

For organizations looking for a free or open-source reporting solution, Metabase is a popular choice.

Metabase is an open-source BI tool that you can self-host, and it provides a simple interface for querying data and building dashboards.

It doesn't require coding for basic usage (it has a point-and-click query builder), but also allows SQL for advanced analysis.

We include Metabase as a bonus because it caters to a specific need: those who want full control of their BI tool without per-user fees, and are willing to manage it themselves.

• Key Features: Metabase's philosophy is simplicity and self-service.

  Once connected to your database, non-technical users can use the Metabase GUI "ask a question" interface to filter and summarize data or use custom SQL queries if they prefer.

  It supports a wide range of data sources: MySQL, Postgres, MongoDB, SQLite, and many more (including cloud warehouses like BigQuery or Snowflake via JDBC).

  You can create dashboards with various visualization widgets (bar, line, pie, maps, etc.), and add filters that apply across charts for interactivity.

  Metabase also has permissions control, so you can restrict which data or queries different users can access.

  Since it's open-source, it's highly flexible: you can customize it, integrate with other systems via API, and even white-label or embed charts (with some editions).

  Newer versions of Metabase have introduced an AI assistant (Metabot in beta) and other modern features, but it primarily focuses on core BI functions.

  One of Metabase's strengths is its lightweight nature – it's relatively easy to deploy (just a Java application, or using Docker) and you can get it running on your own server or cloud in minutes.

• Pricing: Open-Source Metabase is free to download and use (licensed under AGPL).

  This makes it extremely appealing for small companies or projects with limited budget – you pay $0 for the software and only incur costs for the server to run it on.

  Metabase, Inc. (the company behind it) also offers paid plans for hosting and advanced features: Metabase Starter (Cloud) is around $100/month (includes 5 users, then ~$6 per additional user), Metabase Pro is ~$575/month (includes 10 users, then $12 per extra user), which adds priority support and more enterprise features, and an Enterprise tier ($15k/year and up) for large-scale, with features like audit logs, SSO, etc.

  Importantly, the free open-source version itself already has a lot of functionality.

  Many teams run Metabase free on their own infrastructure; others opt for the convenience of the hosted cloud service which comes at a modest cost relative to other BI tools.

  For example, $100/month for up to 5 users (Starter Cloud) is quite accessible compared to most proprietary tools.

• Why It's a Good Choice: Metabase offers an incredible value for teams that are tech-savvy enough to manage it.

  You get a fully functional BI tool without per-user fees – this is a huge plus as your team grows.

  It's also very user-friendly for basic queries: non-engineers often find Metabase easier to start with than complex tools, since the UI asks straightforward questions (like "filter by this", "summarize by that").

  The ability to mix GUI queries and SQL is nice for mixed teams of analysts and business users.

  Metabase has a strong community and plenty of community-contributed plugins or field filters that enhance its capability.

  Being open-source, it provides transparency and flexibility (you're not locked to a vendor, and your data stays in your database – Metabase just queries it).

  It's also adaptable: you can embed Metabase charts in apps with no additional fee (the open-source version supports simple embedding with a small footer).

  The downside is that Metabase might not have some of the high-end features of enterprise tools – for instance, its built-in visualizations are solid but not as flashy as Tableau's, and it may lack advanced AI or complex data modeling layers found in paid products.

  Very large deployments might also require more effort to scale (though many large companies do use Metabase successfully).

  Additionally, if you go the self-hosted route, you'll need to handle maintenance, updates, and security of the Metabase server yourself (which is the trade-off for free software).

• Who It's Good For: Metabase is ideal for startups, small-to-medium businesses, or data teams on a budget.

  If you have developers or IT staff who can deploy a Docker container or set up a cloud instance, you can have Metabase delivering value quickly.

  It's great for product analytics, internal dashboards, or even for software companies who need to embed analytics into their product (without paying hefty embedding license fees).

  Engineering-driven organizations like Metabase because it keeps everything in their own environment and can be deeply customized if needed.

  Even enterprises sometimes use Metabase for specific departments or as a secondary tool for ad-hoc analysis.

  Essentially, if cost is a major concern or you desire full control over your BI tool, Metabase is the top open-source option.

  Just remember, you'll need a bit of technical involvement to host it and potentially to tune it for best results.

  For many, that trade-off is worth the tremendous savings in licensing.

Comparison Table of Top Reporting Tools

To recap the key differences, below is a side-by-side comparison of the five main tools (plus Metabase) across important criteria: data source connectivity, visualization strengths, AI capabilities, community support, ease of use, and pricing.

Tool	Data Sources	Visualizations	AI / NL Capabilities	Community & Support	Ease of Use	Pricing (USD)
Power BI	100+ connectors (databases, files, services). Tight integration with Excel & Azure.	Interactive dashboards, rich charts, custom visuals (via marketplace). Strong data modeling (DAX).	Q&A natural language query; new Copilot for report generation (Premium). Automated insights.	Huge user community, active forums, monthly updates from Microsoft. Enterprise support available (especially with Power BI Premium).	Very high for basic use (drag-and-drop). Moderate for advanced (DAX has learning curve).	Pro: $14 user/mo; Premium: $24 user/mo (PPU) or $4,995+/mo per capacity. Free Desktop for single-user.
Tableau	100+ native connectors (Excel, SQL, Big Data, etc.). Web Data Connector for others.	Best-in-class visual exploration. Dynamic, interactive visuals and storytelling. Extensive chart types and mapping.	“Ask Data” natural language Q&A; AI insights (Explain Data, Tableau GPT) built-in. Forecasting and clustering available.	Very large community and resources (forums, Tableau Public). Professional support with Salesforce backbone.	High for basic dashboards. More complex for advanced calc and data prep (Tableau Prep). Some learning curve.	Creator: $75 user/mo; Explorer: ~$42 user/mo; Viewer: $15 user/mo (all billed annually). Enterprise add-ons extra. Free trial available.
Looker	Supports most SQL databases and warehouses via connections. Optimized for cloud DBs (BigQuery, Snowflake). Limited native connectors to SaaS (requires loading those into a DB).	Flexible web-based dashboards. Good for consistent metrics-driven visuals. Custom visualization possible via APIs.	No direct NL in core product (focuses on LookML modeling). However, integrates with Google’s AI (e.g., Looker + BigQuery ML). Some automation in alerts.	Niche but strong community of data professionals. Google Cloud support for customers. LookML documentation and community content available.	Moderate to low for end-users (if well-modeled, easy to use dashboards). Setup requires high technical skill (LookML coding).	Enterprise pricing – approx. $60k+/year to start. Quote-based. No free version (21-day trial was discontinued). Suited for 50+ users deployments.
Domo	1,000+ connectors (cloud apps, databases, files) out-of-the-box. Built-in ETL for integration.	Modern dashboards with a variety of cards/kpis. Emphasis on quick, shareable visuals. Can build custom apps and embed.	Domo AI with NLQ chatbot, automated insights, predictive models. Alerts and natural language summaries.	Active Domo user community; “Dojo” forums. Official support tiers (dedicated teams for enterprise).	High for basic users (no-code ETL, drag-drop charts). Non-technical users can adopt easily.	Free trial 30 days. Paid: Consumption-based (pay for usage). Typical mid-size cost $20k–$50k/year; Enterprise $50k+. ~$750/user/yr base license. Contact sales for quote.
Daymark	SQL databases, data warehouses, spreadsheets (e.g., PostgreSQL, Google Sheets). Adding more “popular business tools” integrations.	Auto-generated charts and tables in response to questions. Simpler visuals (focused on clarity). Dashboards created by pinning Q&A results.	Strong NL query focus – ask in plain English to get charts. AI-driven query parsing. No-code approach to analysis.	Small but growing (new product). Company provides direct support. Documentation and examples available on website.	Very high – designed for non-technical users. Virtually no learning curve for basic Q&A.	Free start (sign-up with no credit card).
Metabase (OSS)	Many databases (MySQL, Postgres, MongoDB, etc.) via drivers. Supports CSV/XLS upload. Additional sources with custom drivers.	Standard charts and dashboards, open-source library. Custom SQL and notebook mode for advanced visuals. Can embed dashboards (with attribution on OSS).	Basic NL (GUI query builder, not conversational NL). Some AI beta features (e.g., Metabot Q&A) but not as advanced as others.	Huge open-source community. Development is active on GitHub. Paid support for enterprise customers, community forum for OSS users.	Moderate – easy for simple questions via GUI; need SQL for complex queries. Friendly interface for those with some data knowledge.	Open-source: Free to self-host. Cloud Starter: $100/mo (5 users incl.); Pro: $575/mo (10 users incl.); Enterprise custom ($$$).

Notes: “NL” = natural language. Pricing is summarized; always check vendors’ current pricing as these can change (e.g., Power BI prices updated in 2025). Community support indicates availability of help from other users and official channels. Ease of use is relative – all these tools aim to be user-friendly, but some require more technical setup or training than others.

Conclusion

In 2025, businesses have a wealth of reporting tools to choose from, each with its own strengths. The best tool for you depends on your specific needs:

• If you prioritize cost-effectiveness and integration (especially with Microsoft products), Power BI is hard to beat, offering robust features at a low price.
• For advanced visualization and a scalable enterprise platform, Tableau remains a top choice – just be ready to invest in licensing and training to unlock its full potential.
• Organizations that need governed, consistent metrics and embedding capabilities might lean towards Looker, benefiting from its modeling approach, provided they have the budget and technical expertise.
• Those wanting an all-in-one cloud solution will find Domo attractive, as it simplifies the data pipeline and reporting into one platform – ideal for fast-moving business units that want quick insights (though at a premium cost).
• If ease-of-use and natural language querying are paramount – for example, empowering non-analysts to explore data – Daymark offers a refreshing AI-driven take that can lower the barrier to entry for analytics.
• And for teams that are technically inclined and budget-conscious, Metabase shows that you can get powerful BI capabilities without recurring per-user fees, as long as you’re willing to self-manage the tool.

Frequently Asked Questions (FAQs)

What is a reporting tool?

A reporting tool (or BI tool) is software that collects data from various sources (databases, spreadsheets, cloud services) and presents it in visual formats like charts, tables, and dashboards. It helps users track key metrics, analyze data interactively, and convert raw data into actionable insights for better decision-making.

What are the best reporting tools?

The top reporting tools in 2025 include Microsoft Power BI (affordable, Microsoft integration), Tableau (advanced visuals), Looker (enterprise modeling), Domo (cloud BI), and Daymark (AI-driven ease). Other notable options include Metabase (open-source), Qlik Sense, Sigma Computing, and Looker Studio (Google's free tool).

How to choose a reporting tool?

Choose based on your use cases, data source compatibility, ease of use vs. depth needs, scalability requirements, and total cost of ownership. Consider who will use the tool (technical vs. non-technical users), what features you need (AI insights, custom visuals), and always trial the tool with real data and get feedback from end users before committing.

How much does a reporting tool cost?

Costs vary widely: from free options (Metabase open-source, Google Looker Studio) to $10-75 per user/month for popular tools like Power BI ($14/user/month) and Tableau ($75/user/month), up to $60,000+/year for enterprise solutions like Looker. Usage-based tools like Domo can range from $20k-100k+/year depending on consumption. Always get a detailed quote based on your specific needs and user count.