Your SaaS product needs a dashboard. Not a generic one — the specific one your customers need.
A SaaS dashboard that shows the right metrics for the right users — operators see operational data, admins see platform analytics, customers see their own performance — built on a data architecture that actually supports the query patterns the dashboard requires. Fixed scope, fixed price.
Your SaaS product needs an analytics and reporting dashboard that surfaces the right data for each user role — but generic charting libraries and bolt-on analytics tools aren't delivering the specific views your customers are asking for.
SaaS dashboards fail in two ways: the data model wasn't designed to support the query patterns the dashboard requires (so the queries are slow, approximate, or require materialised views that need to be maintained separately); and the dashboard surfaces the wrong metrics because the metrics were designed by the developer rather than derived from what the customer is trying to understand.
The data architecture problem comes first: a dashboard that shows "active users in the last 30 days" needs an events table or a sessions table that was designed to support that query. A dashboard that shows "revenue by plan tier" needs the subscription and plan data modelled in a way that makes that aggregation efficient. If the data model was built for the application's transactional operations rather than for the reporting queries, the dashboard queries will either be slow or require denormalised read models maintained by background jobs.
The metrics problem comes second: what does "active" mean for your specific product? For a document editor, it might mean a document was opened. For a communication tool, it might mean a message was sent. For a scheduling tool, it might mean an appointment was booked. Generic analytics tools can show pageviews; a product-specific dashboard shows the metrics that reflect actual product value delivery.
A production SaaS dashboard with role-specific views, chart components backed by efficient query patterns, date range filtering, CSV export, and the real-time or scheduled data refresh the use case requires.
Events and analytics data model
An events table structured for the specific user actions that are meaningful for your product. Event capture in the application (Convex mutations or API route middleware). The data architecture that supports dashboard queries without full-table scans.
Role-specific dashboard views
Customer dashboard showing their own data (usage metrics, activity history, performance against targets). Admin dashboard showing platform-wide metrics (DAU/MAU, revenue, churn, feature adoption). Different metrics for different user roles, served by different query paths.
Chart components
Recharts or Victory charts built with the specific chart types the data requires — line charts for trends over time, bar charts for comparisons, funnel charts for conversion analysis, and table views for the data that requires row-level detail. Date range picker with preset ranges (last 7 days, last 30 days, this quarter, custom range).
CSV export
Downloadable CSV export for any dashboard table view. The customers who need to take data into Excel or Google Sheets can do so without contacting support.
Data refresh
Real-time dashboards (Convex reactive queries) for the use cases where data staleness matters. Scheduled refresh for heavy aggregations that are expensive to run on every request. Cache invalidation on the events that should trigger a refresh.
One honest number to start.
Fixed-scope, fixed-price. The number below is the starting point — final scope is built from your brief.
A production SaaS dashboard with role-specific views, chart components backed by efficient query patterns, date range filtering, CSV export, and the real-time or scheduled data refresh the use case requires.
Three steps, every time.
The same repeatable engagement on every project. No surprises, no mystery, no billable ambiguity.
Brief & discovery.
We send you questions, then get on a call. Output: a written scope with every step, feature, and integration listed.
Build & ship.
Fixed schedule, weekly reviews. No scope creep unless you change the scope — and if you do, we reprice it transparently.
Warranty & retainer.
30-day warranty on every launch. Most clients stay on a monthly retainer for ongoing features and maintenance.
Why Fixed-Price Matters Here
Dashboard scope is defined by the specific metrics each user role needs to see. Well-specified, well-priced.
Related engagements.
Questions, answered.
The specification question: what decision does each user role make with this dashboard? The customer dashboard should show the data that helps the customer use the product more effectively. The admin dashboard should show the data that helps the operator understand the business. Metrics that don't inform a decision are noise — the specification process prunes them.
If the SaaS product already pipes data to a warehouse (Redshift, BigQuery, Snowflake), the dashboard can query the warehouse directly. The implementation depends on the warehouse's query API. Most dashboard builds for SaaS products query the product's own Postgres database directly, with read replicas or materialised views for expensive aggregations.
If the historical data exists in the database in a queryable form, yes. If the events that the dashboard shows weren't captured before the dashboard was built, the historical view only starts from when event capture was added. The specification defines which metrics have historical depth and which start from launch.
A customer-facing dashboard with chart components, date range filtering, and CSV export: $18k–$30k. Full admin + customer dashboard with real-time updates and advanced filtering: $30k–$45k. Fixed-price.
Dashboard builds typically run 6–12 weeks depending on the data architecture complexity.
Tell Ryel about your project.
Describe what you’re building and what outcome you need. You’ll have a written, fixed-price scope within the week.