Rippling
Rippling joins people data with IT, finance and outside data in one place. It controls access by role and keeps history, so dashboards can answer questions about the past. Custom reports, dashboards and data feeds from other systems need a Rippling AI subscription on top of the platform price. Data is held in the US, and sharing and automations need rules agreed first.
Rippling’s analytics comes in three steps, and custom work sits on the last
Rippling’s analytics comes in three steps. Fixed dashboards come with the platform, recipes come next, and custom reports and dashboards need a Rippling AI subscription.
You run Rippling for HR or IT, or plan to. You want people data joined to IT and finance data. You want each person to see only the data their role allows.
You need your data stored outside the US. You run more than one Rippling account and want one report across them. You have Platform Core only and need dashboards you can change.
Rippling’s US pricing starts at $8 per employee per month, plus a monthly base fee. Custom reports and dashboards need a Rippling AI subscription on top.
Rippling joins HR, IT and finance data, and you ask for dashboards in plain words
Rippling is an HR, IT and finance platform, and its analytics are built in. Its reporting brings together data from HR, IT, finance and operations. It matches that data to the worker behind each record. One report can therefore combine a person’s HR record with their app activity.
You do not have to build every dashboard by hand. You describe the analysis you want, and Rippling AI designs the dashboard. You can also ask a question in plain words and get a dashboard back. Tag Rippling AI in Slack or Microsoft Teams and it answers there too. Every record behind a chart is clickable, and you can start an automated task from the same screen.
Rippling has hundreds of ready-made report recipes. They include recipes for people analytics and workforce analytics. A recipe is Rippling’s word for a ready-made report. Rippling suggests a people analytics dashboard. It covers headcount, new hires, tenure, span of control, turnover, attrition, diversity and compensation. Span of control means how many people each manager leads.
Rippling’s own examples show the kind of question the dashboards answer. A pay equity view uses Pave pay data by gender, ethnicity and job level. An attrition view splits by department, manager tenure and work location. A forecast combines current headcount with open roles by projected start month. Another example uses employee survey data to find managers with high Net Promoter Scores. Rippling AI can also pull public pay benchmarks for a role and compare them with employee pay. Headcount Planning puts open roles, new recruits and costs in one place. Most of these join two kinds of data, such as pay and demographics. In a demo, test a few of them on your own data.
Data from other tools joins in too. That lets one report cover hiring, pay and tool use together. Hundreds of tools connect in one click. You can also bring your own data warehouse, which is a central store of company data. Rippling’s managed connectors at launch included Salesforce, GitHub, Square and Greenhouse. Connectors are set up in the admin screen with no code or engineering help. Setting up those feeds needs a Rippling AI subscription. Warehouse connections are live and import no data into Rippling.
You can reach the same data from your own AI tool. Rippling’s connection for outside AI tools lets Claude, ChatGPT, Cursor and Gemini use Rippling data. Requests are limited to what the person asking can see or do in Rippling. Employees have no access until an admin creates an access policy. Access ends automatically when a worker is offboarded. Social Security numbers, home addresses, personal email addresses and tax IDs are never exposed through it. Our guide for companies using their own AI tool or data warehouse covers how other products handle this.
Roles control who sees what, history is kept and viewers cost nothing extra
Rippling does several things well for a buyer of people analytics. Each one below helps you answer questions, control who sees the answers, keep licence costs down or trust the supplier.
It keeps history
Rippling keeps what was true at past dates, so a dashboard can answer historical questions. A Data as of setting displays report values as of an earlier date. Trend questions such as turnover over two years depend on that history.
Each person sees only the data their role allows
Rippling governs access by role, with no extra licences needed. That matters for people data, because pay and performance are not for everyone. Admins create permission profiles that set which people or groups can reach data. Every employee field carries a permission class that sets who can see it. When you share a dashboard, you can give each recipient data by their own permissions. Rippling AI inherits the same permissions. So a manager who cannot see salary data in Rippling will not get it through AI either.
AI answers link to their records
Each number and name in an AI answer links to the real record. So you can check an answer instead of trusting it. The AI writes database queries against your real data, and each answer traces to the query. Any action the AI proposes needs explicit human confirmation, unless a user has chosen always approve for an automation. Rippling does not use usage data to train AI models. It never permits outside model providers to train on customer data. ChatGPT and Claude are approved outside providers, used only to generate answers in real time.
Rippling does not charge for viewers or editors
Rippling does not charge for viewer or editor licences. Employees who are not admins cannot create reports, but they can see reports shared with them. That keeps report building with a few people and viewing open to many.
Its security audits, and its financing
A security audit matters when you put pay and performance data in one system. Rippling holds a SOC 2 Type 2 report on security, confidentiality and availability, audited every year. It also holds ISO 27001 certification, and ISO 42001 certification for managing AI. Its SOC 3 report is a public version of the SOC 2 report. Rippling announced $450M in new financing. The financing values the company at $16.8 billion. Those figures show the size of the supplier you would sign with.
Data stays in the US, and reports have size, age and sharing limits
Rippling’s limits cover where data is stored and how current reports are. They also cover how data goes in and out, and who can see or act on a result. Each one below is a point to settle before you sign.
Your data sits in US data centres
All Rippling data is housed in US-based AWS data centres. Data brought in from other systems is stored and processed in Amazon Web Services data centres in Oregon. That data is wiped on offboarding if you leave. This matters if your people work outside the US, because it decides where their records are held. Rippling acts as a data processor for most customer data and processes it only as you instruct. Ask your legal team whether US storage is acceptable.
Cross-account queries are not supported
Rippling does not support cross-account data queries for organisations with multiple Rippling accounts. If you run more than one Rippling account, ask how you would report across them.
Warehouse support depends on the page
Rippling’s Data Cloud page offers zero-copy access to Snowflake, Databricks, BigQuery, Redshift or Azure. Zero-copy means Rippling reads the warehouse where it sits and imports no data. The FAQ on the same page says zero-copy is supported for Snowflake, with more platforms planned. A Rippling blog post on connectors covers Snowflake, with BigQuery and other systems forthcoming. Ask which warehouse works today and get it in writing.
Reports can be hours old, with size limits
These timings decide how current a report is when you read it. Changes to employee data appear in reports within 15 minutes. A custom report is cached for two hours, which means a stored copy of its results is reused. So a figure may be up to two hours behind. Data from other systems syncs hourly by default, and a first sync can take up to 24 hours. Very large data tables, which Rippling calls big objects, refresh on a schedule such as nightly. A report download holds at most 500,000 rows, so ask how a larger history would be exported. Custom data tables are not suited to millions of rows. Adding payroll data to a report adds a pay run date filter, set to the last three months by default. A yearly pay report may cover only three months unless someone changes the filter.
Exports come in set formats
Grid and pivot reports download as Excel, CSV or PDF. Charts download as PNG, JPG or PDF. Sending a report by secure file transfer needs an Unlimited Advanced Automations subscription. Tableau, Looker and Sigma views cannot be embedded in Rippling templates, one-to-one pages or review cycles. If your managers read Tableau, Looker or Sigma charts, ask where they would see them in Rippling.
Its own examples reach individual employees
Rippling’s own examples include burnout flags based on Zoom meeting volume and patterns. Another flags employees with declining performance ratings. Others look for people who may be at risk of leaving. One uses employees in the top 25% of ratings and the bottom 50% of pay. A further example flags anyone underusing their paid time off. A flag on a named person differs from a group figure, because someone may act on it about that person. One example, on low customer satisfaction scores, tells Rippling AI not to assign blame. It also tells it not to treat a low score alone as proof of poor performance. That is a sound rule for the others too. Agree with your legal team what you will measure before anyone builds one.
Sharing and automations can go further than planned
These settings decide who sees a number after you build it. A user can share a report with unrestricted access. Anyone who can view a report on a dashboard can drill into details the top-level number does not show. A Rippling AI answer in a Slack channel or group message is visible to everyone there. Rippling AI can also run a prompt on a schedule or on an event. After a user selects always approve, it takes the action without stopping to ask. Decide who may choose that setting.
It suits companies that already keep their people records in Rippling
Rippling suits a buyer whose records already sit in Rippling. The cards below show who else it is built for. Check each against your own situation before you shortlist it.
Companies already holding records in Rippling
Reports use employee data, app activity, custom apps and data imported from other systems. Bringing in data from other systems through Rippling’s data feeds needs a Rippling AI subscription. If Rippling already holds your employee records, you start from them. Its AI needs no set-up or training. Rippling helps manage employee data and operations as a company grows from 2 to 2,000+ employees.
Companies of 500 to 1,400 employees
Two of Rippling’s customer stories, Barry’s and Chess.com, sit in this range. Barry’s has 1,400 employees. Retention reporting at Barry’s was manual before Rippling, in one executive’s words. Chess.com has 500 to 999 employees. Ask for a customer of your own size and country to speak to.
Teams who want IT and finance data next to people data
Rippling’s examples cross data types. One tracks which employees drive the most Claude usage, by department. Another finds devices with AI desktop applications that Rippling’s device management did not install. Its AI Spend Console maps AI spend to business metrics. Examples are performance ratings and the number of code changes engineers submit. These questions mix IT and people data, so they only work when both sit in one place. Rippling joins them.
Plans start at $8 per employee per month, and custom analytics needs a further subscription
Rippling charges most products per employee per month, and some carry a monthly base fee. What you get depends on the analytics tier. Its US pricing starts at $8 per employee per month, with a monthly base fee. Each HR, finance or IT product is bought on top of the required Rippling Platform. Say you paid that start price for 1,000 employees. It would come to $8,000 a month. The base fee and any extra products come on top.
Analytics comes in three steps. Platform Core includes pre-built dashboards that you cannot customise. Platform Pro adds dashboards from recipes and reports from report recipes. A Rippling AI subscription adds new dashboards with custom reports, built by hand or by AI. Three more things need the same subscription. They are building a report with Rippling AI, data lineage and Rippling’s data feeds from other systems. Data lineage is a record of where each number came from. Outside AI tools get no data from Rippling without an active Rippling AI trial or subscription.
Viewers and editors cost nothing extra. In the UK, Rippling asks buyers to request a custom quote. Generation Digital Limited also sells a Rippling Global HR and IT Platform service. It sells it through G-Cloud 15, the UK government’s Digital Marketplace. Generation Digital prices it as a base fee plus a fixed amount per employee per month.
A buyer who signs an order form accepts several documents. They are Rippling’s Customer Terms of Service Agreement, its privacy terms and any service-specific terms. Rippling’s core terms include AI Services Terms. Read those before you switch on any AI feature.
You could also get these reports and answers from agents built on your own systems. We have mapped what that looks like. The HR Agent Map
Eight questions to put to Rippling
- What does the Rippling AI subscription cost on top of the platform, for our headcount? Good answer: A written price that covers custom reports, custom dashboards and the record of where each number came from.
- Which data warehouses can Rippling read live today? Good answer: A named list in the order form, with a date for each one still planned.
- Where do ChatGPT, Claude and any other outside AI providers process our data? Good answer: A named country for each, and confirmation that admins can switch AI features off.
- Can an admin stop people choosing always approve on an automation? Good answer: Yes, an admin setting limits it to named people.
- Who can share a report with unrestricted access, and can we limit it? Good answer: Only named admins can share, and every share is logged.
- When we add payroll data, how do we set the pay run dates for a yearly report? Good answer: A saved report with the full year already set, and a short guide for report builders.
- If we run more than one Rippling account, how do we report across them? Good answer: A working method in writing, tested on our own data.
- What do we get when the contract ends? Good answer: Our reports and history in a stated file format within a stated time.
Alternatives to Rippling, from Visier to a spreadsheet
If Rippling comes off your shortlist, look at Visier, One Model, Workday, BambooHR or Worklytics. If Rippling already holds your records, buying nothing more is a further option.
Visier and One Model take data from several systems. Visier’s ready-made connectors combine data from HR systems, payroll, hiring tools, talent tools and data warehouses. One Model pulls workforce data together and organises it for reporting, analytics and AI. Both suit a company whose people data sits in more than one HR system. A separate tool depends on how often its feed runs. Visier’s connectors run in batches, not real time, and Visier recommends a daily extraction. Rippling’s own employee data reaches its reports within 15 minutes. One Model can host in an AWS region aligned with a customer’s GDPR programme, such as the EU. Rippling keeps data in the US. One Model sells three editions. They are Essentials, Data Mesh and Enterprise, and each is sold by quote.
Workday and BambooHR build analytics into the HR system itself. Workday HCM and Financial Management include an analytics and reporting toolkit. BambooHR’s custom report builder uses hiring, performance, time off and benefits data as datasets. Changing HR system to get analytics is a large step. Compare the analytics inside the system you already run first. Our guide for companies whose HR system has analytics covers that choice.
Worklytics answers a different question. It connects tools, then anonymises, cleans and aggregates the data into metrics and network graph details. Its connectors ingest data from over 25 tools. It collects Microsoft Teams chat, channel, meeting and call metadata. Worklytics does not store or analyse work content, and it uses log data. Rippling’s Zoom burnout example uses meeting patterns too, but Worklytics is built around that kind of activity data.
Buying nothing more can work if Rippling already holds your records. Platform Core includes pre-built dashboards. Rippling lets grid and pivot reports download as Excel, CSV or PDF. Ask which tier those reports come with, because Core includes only pre-built dashboards. Add the AI subscription only when a question needs a custom dashboard. Our comparison of the 33 products puts them side by side.
https://www.rippling.com/platform/analytics · Rippling analytics · read 6 Nov 2026
https://www.rippling.com/platform/data-cloud · Rippling Data Cloud and its FAQ · read 6 Nov 2026
https://www.rippling.com/platform/ai · Rippling AI · read 6 Nov 2026
https://www.rippling.com/platform/ai/rippling-mcp · Rippling MCP, for outside AI tools · read 6 Nov 2026
https://www.rippling.com/pricing · Rippling pricing · read 6 and 9 Nov 2026
https://www.rippling.com/connectors · Rippling connectors · read 6 Nov 2026
https://www.rippling.com/legal · Rippling legal terms · read 9 Nov 2026
https://www.rippling.com/trust/security · Rippling security · read 6 Nov 2026
https://www.rippling.com/trust/privacy · Rippling privacy · read 6 Nov 2026
https://www.rippling.com/products/hr/headcount-planning · Rippling Headcount Planning · read 6 Nov 2026
https://www.rippling.com/blog/rippling-data-cloud-data-connectors · Rippling blog, Data Cloud connectors · read 6 and 9 Nov 2026
https://www.rippling.com/blog/inside-the-making-of-rippling-ai · Rippling blog, the making of Rippling AI · read 6 Nov 2026
https://www.rippling.com/blog/introducing-ai-spend-console · Rippling blog, AI Spend Console · read 9 Nov 2026
https://www.rippling.com/blog/series-g-fundraising-tender-offer · Rippling blog, Series G financing · read 6 Nov 2026
https://www.rippling.com/customers/barrys · Rippling customer story, Barry’s · read 6 Nov 2026
https://www.rippling.com/customers/chess · Rippling customer story, Chess.com · read 6 Nov 2026
https://www.rippling.com/ai-use-cases/pay-equity · Rippling example, pay equity · read 6 Nov 2026
https://www.rippling.com/ai-use-cases/employee-turnover-trends · Rippling example, attrition trends · read 6 Nov 2026
https://www.rippling.com/ai-use-cases/forecast-maximum-headcount-by-month · Rippling example, headcount forecast · read 6 Nov 2026
https://www.rippling.com/ai-use-cases/identify-manager-nps-drivers · Rippling example, manager NPS · read 6 Nov 2026
https://www.rippling.com/ai-use-cases/compare-role-pay-to-market · Rippling example, role pay against market · read 9 Nov 2026
https://www.rippling.com/ai-use-cases/burnout-risk-zoom · Rippling example, burnout risk from Zoom · read 6 Nov 2026
https://www.rippling.com/ai-use-cases/build-a-leadership-ready-performance-review-dashboard · Rippling example, performance review dashboard · read 6 Nov 2026
https://www.rippling.com/ai-use-cases/identify-top-performers-who-might-have-been-overlooked-by-leadership · Rippling example, overlooked top performers · read 6 Nov 2026
https://www.rippling.com/ai-use-cases/prevent-burnout-by-identifying-employees-not-taking-enough-time-off · Rippling example, time off and burnout · read 6 Nov 2026
https://www.rippling.com/ai-use-cases/review-low-csat · Rippling example, low customer satisfaction scores · read 9 Nov 2026
https://www.rippling.com/ai-use-cases/ai-adoption-departments · Rippling example, AI adoption by department · read 6 Nov 2026
https://www.rippling.com/ai-use-cases/detect-unauthorized-tools · Rippling example, unapproved AI tools · read 6 Nov 2026
https://developer.rippling.com/documentation/rippling-platform/dashboards/getting-started · Rippling developer documents, dashboards · read 6 Nov 2026
https://developer.rippling.com/documentation/rippling-platform/dashboards/sharing · Rippling developer documents, sharing dashboards · read 6 Nov 2026
https://developer.rippling.com/documentation/rippling-platform/reports/getting-started · Rippling developer documents, reports · read 6 Nov 2026
https://developer.rippling.com/documentation/rippling-platform/reports/permissions · Rippling developer documents, report permissions · read 6 Nov 2026
https://developer.rippling.com/documentation/rippling-platform/reports/sharing · Rippling developer documents, sharing and downloading reports · read 6 and 9 Nov 2026
https://developer.rippling.com/documentation/rippling-platform/reports/filters · Rippling developer documents, report filters · read 9 Nov 2026
https://developer.rippling.com/documentation/rippling-platform/data-model/permissions · Rippling developer documents, data permissions · read 6 Nov 2026
https://developer.rippling.com/documentation/rippling-platform/data-model/object-types · Rippling developer documents, object types · read 6 and 9 Nov 2026
https://developer.rippling.com/documentation/rippling-platform/data-model/data-lineage · Rippling developer documents, data lineage · read 6 Nov 2026
https://developer.rippling.com/documentation/rippling-platform/pipelines/faqs · Rippling developer documents, pipelines FAQ · read 6 and 9 Nov 2026
https://developer.rippling.com/documentation/rippling-platform/pipelines/getting-started · Rippling developer documents, pipelines · read 6 Nov 2026
https://developer.rippling.com/documentation/rippling-platform/pipelines/sync-management · Rippling developer documents, sync management · read 6 Nov 2026
https://developer.rippling.com/documentation/rippling-platform/rippling-mcp/overview · Rippling developer documents, MCP overview · read 6 and 9 Nov 2026
https://developer.rippling.com/documentation/rippling-platform/developer/workflows/rippling-ai · Rippling developer documents, AI automations · read 6 and 9 Nov 2026
https://developer.rippling.com/documentation/rippling-platform/developer/workflows/steps · Rippling developer documents, automation steps · read 9 Nov 2026
https://www.applytosupply.digitalmarketplace.service.gov.uk/g-cloud/services/716192361817387 · UK Digital Marketplace, G-Cloud 15 service listing · read 6 Nov 2026
https://assets.applytosupply.digitalmarketplace.service.gov.uk/g-cloud-15/documents/704663/716192361817387-pricing-document-2025-12-11-1344.pdf · UK Digital Marketplace, G-Cloud 15 pricing document · read 6 Nov 2026
https://www.visier.com/workforce-ai/platform/context-engine/ · Visier Context Engine · read 6 Nov 2026
https://docs.visier.com/visier-people/Studio/data/data%20connectors/data-connectors.htm · Visier documentation, data connectors · read 6 Nov 2026
https://www.onemodel.co/products/data-mesh · One Model Data Mesh · read 6 Nov 2026
https://help.onemodel.co/hc/en-us/articles/6598588333199-One-Model-GDPR-Overview · One Model GDPR overview · read 6 Nov 2026
https://www.onemodel.co/pricing · One Model pricing · read 6 Nov 2026
https://www.workday.com/en-us/ai-platform/core-reporting-analytics.html · Workday core reporting and analytics · read 6 Nov 2026
https://www.bamboohr.com/product-updates/new-data-sources-in-custom-report-builder · BambooHR custom report builder · read 6 Nov 2026
https://www.worklytics.co/how-it-works · Worklytics, how it works · read 6 Nov 2026
https://www.worklytics.co/ · Worklytics home page · read 6 Nov 2026
https://docs.worklytics.co/knowledge-base/connectors/teams · Worklytics Microsoft Teams connector · read 6 Nov 2026