Sisense vs Domo: AI-Powered BI Comparison
Compare Sisense and Domo for AI-powered business intelligence. See which tool is better for your data team.
LAST UPDATED August 28, 2025
AUTHOR 
Holistics Team
Holistics Team Feature-by-Feature Comparison Table
Scroll to the right to see more
| Dimension |  Sisense |  Domo |
|---|---|---|
| Demo Playground Availability and quality of demo playground for testing the tool before purchase. | ||
| | ||
| Pricing Structure Pricing model and cost structure of the BI tool. | ||
| | User-Based Custom Pricing Customized solutions based on users, data size, hosting type, and usage intensity. source | Consumption-Based Credit System Pay for what you use with credit system and base user fee starting at $750/year per user. source |
| | $40,600-327,000/year Essential $40,600-60,000/year, Advanced $69,600-138,000/year, Pro $109,000-327,000/year. source | $50,000-200,000/year Small businesses $30,000/year, enterprise-level organizations can exceed $100,000 annually. source |
| Visualizations Chart and visualization capabilities of the tool. | ||
| | Out-of-the-Box Charts Library of out-of-the-box charts, maps, and widgets for quick dashboard building. source | 150+ Native Chart Types Over 150 native chart types including pie, line, bar charts, maps, scatter plots, and Gantt charts. source |
| | Extensive Customization Customize visuals and dashboards with no-code design approach for personalized layouts and themes. source | |
| | Branded Experiences Interactive visualizations matching product look and feel with complete white-labeling in Pro plan. source | Personalized Branding Extensive custom styling and branding with user-friendly no-code design interface. source |
| | AI-Powered Narratives Sisense Intelligence features assistant and narrative generation for natural-language exploration. source | AI-Enhanced Data Stories Conversational AI (AI Chat) for natural language questions and automated alerts for key data changes. source |
| Ease of Use & Self-Service How user-friendly and self-service oriented the tool is for non-technical users. | ||
| | Interactive Dashboards Interactive no-code dashboards for exploration with filtering capabilities. source | Interactive Data Exploration Interactive dashboards with filters and customizable views for intuitive data analysis. source |
| | Natural-Language Exploration Sisense Intelligence features assistant and narratives for natural-language data discovery. source | AI-Driven Data Exploration AI Chat for natural language questions and instant actionable insights through conversational AI. source |
| | ❌ No explicit built-in calculation features mentioned in documentation. source | ❌ No explicit built-in calculation features mentioned in documentation. source |
| | ❌ No explicit report building features mentioned in documentation. source | ❌ No explicit report building features mentioned in documentation. source |
| | ❌ No explicit AI-assisted analytics features mentioned in documentation. source | ❌ No explicit AI-assisted analytics features mentioned in documentation. source |
| Data Delivery How data and reports are delivered to end users. | ||
| | ❌ No explicit alerts or subscription features mentioned in documentation. source | Automated Alerts Automated alerts for key data changes to keep users updated on important information. source |
| | ❌ No explicit sharing or distribution features mentioned in documentation. source | ❌ No explicit sharing or distribution features mentioned in documentation. source |
| Embedded Analytics Capabilities for embedding analytics into other applications. | ||
| | iFrames, APIs & SDKs iFrame embedding across all plans with REST API and Compose SDK for React, Angular, Vue. source | Embedded Analytics Embed analytics into any application, portal, or website to extend data reach and deliver insights. source |
| | Customizable White-Labeling Complete white-labeling in Pro plan with limited options in lower tiers for OEM scenarios. source | Custom Branding White-labeling and custom theming for embedded content to reflect brand's look and feel. source |
| | Intuitive Dashboard Designer Dashboard Designer feature with Basic in Essential plan and Advanced in higher tiers. source | Self-Serve Analytics Simple drag-and-drop tools for teams to create visualizations within embedded content. source |
| Reliability & Performance System reliability, performance optimization, and monitoring capabilities. | ||
| | Proprietary Elasticube System Proprietary Elasticube system for data storage and processing with scalable analytics and low latency. source | ❌ No specific information on query optimization, caching, pushdown, or pre-aggregation mentioned. source |
| | ❌ No explicit monitoring, freshness indicators, or error alerts mentioned in documentation. source | Automated Alerts Automated alerts for key data changes, but no explicit freshness indicators or error alerts mentioned. source |
| Semantic Modeling Data modeling and semantic layer capabilities. | ||
| | Data Modeling & Unified Data Data modeling capabilities with intuitive workflows for unifying data across sources into actionable visualizations. source | ❌ No explicit semantic layer or consistent metrics enforcement mechanisms mentioned in documentation. source |
| |  Git integration for developers with version control capabilities and marketplace add-ons. source | ❌ No information about Git version control for managing semantic models or BI artifacts mentioned. source |
| | ❌ No explicit automated metadata synchronization from dbt or data warehouses mentioned. source | ❌ No explicit automated metadata synchronization from dbt or data warehouses mentioned. source |
| | Pro-Code to No-Code Pro-code, low-code, and no-code capabilities for building dashboards with JavaScript and Compose SDK. source | ❌ No information about defining dashboards or models in YAML/DSL formats or CI/CD workflows mentioned. source |
| Security and Governance Security features and governance capabilities. | ||
| | Trust & Security Enterprise-grade security in Pro plan with marketplace add-ons for data governance and SSO. source | Enterprise-Level Security Enterprise-level security, compliance, and governance with SSO and encryption capabilities. source |
| | ❌ No explicit audit compliance features mentioned in documentation. source | ❌ No explicit audit compliance features mentioned in documentation. source |
| | ❌ No explicit data masking or encryption features mentioned in documentation. source | ❌ No explicit data masking or encryption features mentioned in documentation. source |
| | ❌ No explicit monitoring or logging capabilities mentioned in documentation. source | ❌ No explicit monitoring or logging capabilities mentioned in documentation. source |
Compare Sisense with:
Compare Domo with:
Community Discussions
Discover what other practitioners are discussing about this topic.
r/analytics
Posted on April 2025 View source
What is the future of Business Intelligence? What should I expect in the next 5 years?
Whats the future of Business Intelligence gonna look like in the next 5 years im kinda curious but also confused like will BI tools get smarter or just more complicated how much will AI and automation actually change the game can we expect Business Intelligence to predict trends before they happen or is that just hype and what about data privacy with all these new techs coming up should we be worried also will small businesses finally get access to pro-level Business Intelligence without needing a PhD to understand it or is it gonna stay expensive and elite im really wondering if anyone else feels both excited and a bit nervous about where BI is headed.
2top comments
r/BusinessIntelligence
Posted on June 2025 View source
Anyone using AI in Business Intelligence?
1top comment
LessWerewolf June2025 Yes, mostly I use it for refining my SQL queries or to see what are the different ways in which I can solve the same problem. It's quite helpful in that sense.
I also use it to review queries of my juinor team members. To see if I may have missed out on anything. It helps with that extra set of eyes at times.
And to create documentation for dashboards/reports.
I also use it to ask questions which I can use in stakeholder meetings for requirement gathering. For eg, I explain the context of the meeting and then ask the AI to roleplay with regards to what questions I should be asking in that specific meeting. Helps me prepare and also helps understand perspectives from different POVs.
r/BusinessIntelligence
Posted on March 2025 View source
So has your company actually embraced AI for BI and analytics, or naw?
The C-suite constantly goes on and on about how we're AI-first, etc., but the rubber doesn't seem to meet the road. We have some AI resources like CoPilot on top of MS Office, Salesforce Agent Force, and some people are using their own personal AI accounts -- just curious -- how has it been where you work?
2top comments
sjjafan March 2025 My guess is that the bulk of the companies are just bull$#!7ing with the buzzword. To successfully introduce AI into your BI, you need clean orderly data.
Go ahead and tell me the last time everyone cheered when the data governance team came through the door.
People mostly rool their eyes and crawl into a ball.
jdsmn21 April 2025 So - C suite seems to think AI can “answer all their questions”.
So I respond with “what are the questions you are wondering? I can pull data, schedule reports, to your inbox, or build live dashboards with graphs in any color of the rainbow!” - which is usually met with blank stares.
That’s why I know AI isn’t worth the trouble. It’s a solution to a problem we don’t have.
r/BusinessIntelligence
Posted on July 2024 View source
Has anyone used any AI-powered BI tools? What was the experience like?
Not going to post them here but there has been a lot of 'chat with your data' apps recently.
I am not a professional analyst but I have use ChatGPT in the past to help me write SQL queries, so I can see some appeals with them, although I also can't imagine how these tools can deal with the messy nature of badly maintained tables with duplicated names and nonsensical field names etc.
I also see some of these tools advocate for dynamically generated dashboards (since you can just ask questions to drill down etc.) though in my experience I don't usually need to adjust the dashboard often.
I am curious if anyone here has used these tools? What was the experience like?
I am not a professional analyst but I have use ChatGPT in the past to help me write SQL queries, so I can see some appeals with them, although I also can't imagine how these tools can deal with the messy nature of badly maintained tables with duplicated names and nonsensical field names etc.
I also see some of these tools advocate for dynamically generated dashboards (since you can just ask questions to drill down etc.) though in my experience I don't usually need to adjust the dashboard often.
I am curious if anyone here has used these tools? What was the experience like?
2top comments
jallabi July 2024 Some of the tools are getting better, but I can't help but think they still aren't really solving a problem.
If you're technically minded with some experience in data, then none of them are doing anything better/faster than what you can do with SQL or a BI tool.
If you're on the business side, they still aren't good enough because as the other poster said, you are reliant on a semantic layer so it's not that much better/faster than asking someone for a new dashboard.
vroomx July 2024 The only way these tools can be halfway effective is if they sit on top of a well manicured semantic layer. I also think that the real winner will be the platform that figures out how to invoke an action from the insight. I.e. the analysis picks up on repeat customers and be able to recommend an action to take for those customers and then kick off the process with a simple push of a button …or if the action is low risk enough to do it automatically.
r/BusinessIntelligence
Posted on Feb 2025 View source
Who is actually using AI + BI tools like Thoughtspot, Zenlytic, etc. ?
Bombarded nonstop with talk of AI everything, and a couple of case studies here and there with small companies that I've never heard of. Even Databricks, one of the big guys, keeps mentioning the same customer (Sega) whenever they talk about their BI genie.
So my question is - who is actually using this stuff? Is anyone?
Second question - if you or your company use any of these tools - when did you start using them? How has the experience been so far?
So my question is - who is actually using this stuff? Is anyone?
Second question - if you or your company use any of these tools - when did you start using them? How has the experience been so far?
1top comment
beck_rad Feb 2025 I seriously think that at the end of the day maybe 1% or less of companies at any given point are at a data maturity stage where they could truly leverage cookie-cutter BI-AI solutions. The rest of us are still cleaning up messy data and figuring out what the proper business logic is.
Generative AI has gotten pretty good at descriptive analytics. i recently tried Gemini in Looker and as long as the tables had descriptive column names, it did a great job answering business questions that my stakeholders usually will go to a dashboard for.
I've been in analytics for 16 years, I have used most top models for interacting with data on the analytics maturity spectrum by now. Any job where the core KRA is building dashboards/ reports is already at risk as long as there's an appetite in your company to use AI.
It's garbage in garbage out process. If the underlying table has false/no data description or column names or misleading column names, it's set to come up with incorrect insights. No surprises there and it's more of a data problem than a Gen AI problem.
Reg: first layer, that's what I meant with the descriptive analytics. I've had a bit of success having AI explain what contributed to a spike or drop in a kpi but then I made sure proactively that it had access to data needed to derive that.
It's not able to do exploratory analysis...yet but we've barely crossed 2 years and the capabilities multiplied rapidly. It's just a matter of time it does a decent job on exploratory analysis and once that is accomplished, predictive analytics wouldn't be a far-fetched dream.
Having said this, it's a lot dependent on human governance (prompting, supplying credible data etc.) but those wouldn't be limited to just analysts. Anyone would be able to use natural language to interact with data.