Embedded analytics doesn't have to be overwhelming
Adding analytics to a SaaS product is a sprawling decision. Data, features, deployment, vendor fit — none of it is plug-and-play, and the wrong call shows up months later as missed deadlines or a roadmap rewrite.
This Product Fit Guide is the pre-evaluation worksheet. Six chapters, four action items, and the questions worth answering before a single vendor demo.
Key considerations
Embedded analytics decisions touch product, data, infrastructure, security, and pricing — usually all at once. Knowing what matters most to your team is half the work. The chapters below walk through the six areas every SaaS team should think about before evaluating vendors:
- Why embedded analytics? The business case for adding analytics to your product.
- Data integration. Connecting to and analyzing the data you already have.
- Product features. The capabilities that drive adoption and retention.
- Developer features. What makes embedding fast — or slow.
- Deployment & architecture. Where it runs, and how it scales.
- Company fit. The partner behind the platform.
Why embedded analytics?
You already know data matters. Every day your application and your users generate it — orders, events, logs, surveys. The data is a gift, but only if you can turn it into something users actually use.
Turn data into insights and actionable intelligence — in real time, without burdening your dev team or your budget.
That's where embedded analytics earns its place. The right platform lets SaaS providers differentiate, deliver new features faster, and unlock new revenue streams without hiring an analytics engineering team.
Analytics for your entire data pipeline
Modern analytics isn't just charts, reports, and dashboards. Cloud-native and container-based platforms now cover the entire pipeline — collection, transformation, visualization, automation, and AI.
Think bigger than features. Think about the architecture, the SDLC, the roadmap, and the budget. The product fit decision touches all of them.
Embedded analytics allows SaaS providers to:
- Increase customer satisfaction
Let end users create the custom reports they actually want, so adoption and CSAT climb together.
- Grow revenue streams
Turn analytics into a paid tier — and into an embedded reason customers renew.
- Boost developer productivity
Low-code JavaScript widgets give devs advanced analytics without months of build.
Data integration
Data comes first. The types, volumes, and locations of the data you want to analyze drive everything else — performance, scalability, cost, administration. Map your data before you map your features.
Data location
Where does the data you need to analyze actually live — on-prem, in the cloud, in third-party systems?
For most SaaS teams: all three. Your analytics layer needs to connect, ingest, and transform data regardless of where it sits.
- What are your current databases?
- Does the embedded analytics vendor have pre-built connectors — or will you need API-based integration?
Data types
Are you analyzing SQL or NoSQL? Structured or unstructured?
A lot of modern data is semi- or unstructured — “dark data” — and general-purpose BI tools often can't ingest or analyze it. Files, audio, video, social, IoT, medical records: if any of these are on your list, your platform needs to handle them.
- What data types and formats are you analyzing?
- Do you have text or media files in the mix?
- Which format dominates — CSV or JSON?
Data volume
How much data do you have today, and how much do you add each month?
Analytics performance is bounded by the slowest data source in the pipeline. Find an engineer, pull the current volume and trailing-12-month growth rate, and use those numbers to pressure-test every vendor demo.
- Will the solutions you're evaluating slow down at your actual data volume?
- Will scaling the database layer be your responsibility or theirs?
Data replication & security
Know your stance on sending data to third-party systems before the first vendor call.
This question has real InfoSec consequences. Pull in your security team early. If you handle HIPAA, PII, or other regulated data, you're almost certainly going to need a platform that deploys inside your own cloud.
- What are your data movement requirements?
- Can data be replicated to a third-party system, or must it stay read-in-place?
Map every database your analytics will need to touch
| Database name | Location | Data type | Used for | Replicable? | Current size | Monthly add |
|---|---|---|---|---|---|---|
| Prod 1 (example) | AWS RDS | SQL | Customer order info | Yes | 15 GB | 0.25 GB |
Product features
Every team's feature list is different — but every team has one. Write down the “must-haves” before any vendor pitch. The deal-breakers should surface in week one, not week six.
Start by interrogating your current setup:
- What kind of upgrade are you looking for from your current solution?
- Is this a modernization (replacing SSRS or similar) — or a fix for feature gaps and cost overruns from an existing embedded tool?
- Are customers asking for more customization and self-service?
- Could the embedding experience and UI customization be better?
Chart options
List the visualizations you need versus the ones you'd like. Categorize “needs” vs. “wants” and mark which are in use today.
Chart and dashboard builders
If end users or your professional services team are asking for custom reports or self-built dashboards, the builders become a central part of the evaluation.
Visualization augmentation
Do you need to layer trend lines, goals, or annotations on top of your visuals?
Filters
What filter levels does each platform support? Single-chart, page/tab, and whole-dashboard filtering are all in scope — and the more flexible, the better.
Mix data on one screen
Does the platform let you combine data from multiple sources on a single dashboard? Not every BI vendor allows this.
Scheduled report generation
If your team still emails reports to management every Monday, automate it. Scheduled reports should be table stakes.
Data refresh rate
How fresh does the data need to be — daily, hourly, by the minute? Push vs. pull, and at what frequency?
Data transformation
Most BI vendors don't include a native transformation layer. What transformations does your data need today? How heavy are they? Are there performance implications at scale?
Data collection from users
Are you collecting data from end users through forms or surveys? Whether that lives inside or outside your analytics layer changes the architecture.
Automation
How do you plan to turn analytics into action? If you're not automating downstream processes today, this is the chapter to revisit it.
“Automation” means different things across BI vendors. Real automation runs processes downstream of the chart — webhooks, posting data to external systems, creating or updating data in existing datasets. Ask for specifics.
AI / ML
Do you have AI or ML initiatives in flight? The space moves fast — ask every vendor what's possible today and what's on the near roadmap.
List the visualizations you need today and the ones you'll need next
| Chart type | Example use case | Required for launch? | Customization required or wanted |
|---|---|---|---|
| Line chart (example) | Daily orders | Yes | Required — compare month over month |
Developer features
Once you've matched the data and the features to your needs, the next question is how this gets embedded into the product. Not every platform is created equal — some are barely embeddable, others embed easily and at scale.
“We have an API” isn't enough. How that API gets used — and how robust it is — is what matters.
Pick a platform that's full-featured but easy to integrate, with a vendor that delivers new features regularly and listens to your feedback. Your developers will thank you.
Widgets availability
What's in the pre-built widget library?
- Are widgets limited to dashboards — or can users build single charts and full dashboards?
- Is there widget coverage for data collection and automation, too?
Embedding type
- Are widgets written in JavaScript?
- Or are they iframes — or something else entirely?
JavaScript widgets are what you want for fast, flexible embedding. iframes drag in security complications that InfoSec will flag — and slow the integration timeline.
White-labeling
- Can analytics components be styled to precisely match your brand?
- Is styling limited to a handful of options — or do you have full CSS control?
API suite
Widgets are a starting point. A robust API suite — with parameters and variables you can pass through calls — is what unlocks fully custom experiences. Review each platform for data ingestion APIs, data retrieval APIs, and configuration APIs for components and features.
Multi-tenancy
If your SaaS is multi-tenant (most are), your analytics layer needs to be too.
- Are you running a multi-tenant application?
- What SSO methods does the vendor support for tenant-based security?
Row-level security is a must. SSO is the only scalable path to multi-tenant authentication inside an embedded analytics platform. Ask every vendor exactly how they handle both.
List the widgets you need at launch and after
| Widget type | Required for launch? | Example use case |
|---|---|---|
| Single chart (viewer) | Y / N | |
| Single chart (builder) | Y / N | |
| Dashboard (viewer) | Y / N | |
| Dashboard (builder) | Y / N | |
| Automation workflow (builder) | Y / N | |
| Forms (viewer / user) | Y / N | |
| Forms (builder) | Y / N | |
| Surveys (viewer / user) | Y / N | |
| Survey (builder) | Y / N | |
| Dataset / connector manager | Y / N |
Deployment & architecture
Analytics used to mean a monolithic, server-based app. AWS, Azure, and GCP changed all of that. Modern analytics platforms scale up with your customers and keep costs in check — but only if you pick one designed for it.
Data security and governance is the constant. Your deployment model decides whether data stays inside your environment or gets sent to a third party. Pull in InfoSec / DevSecOps early so you don't waste evaluation cycles on a vendor you can't actually deploy.
Deployment model
Are you looking for a cloud vendor — or a deployed system that runs next to your application?
This answer has direct security implications. Check in with InfoSec / DevSecOps early so requirements and limitations are crystal clear before you take a single demo.
Scalability & performance
In a deployed model, scaling and performance fall on your IT team. If you're considering a traditional server-based solution, get answers to all of these:
- How many servers are minimally required?
- How many users does that minimum footprint support?
- What are the server specs at each deployment level?
- How is load balancing handled? How many servers for redundancy? What infrastructure is needed?
- Are server licenses an add-on cost?
- How many SDLC environments do you need (everyone needs at least dev + prod)?
- How well do those environments fit your existing DevOps process?
With some vendors, none of those questions matter. A 100% cloud-native architecture maximizes scalability and minimizes cost — and quietly takes most of the system management burden off your team.
Document your deployment requirements
| Is a deployed solution a requirement or a preference? | |
| How many environments do you need to support? | |
| Is cutting infrastructure costs a primary or secondary concern? |
Company fit
A product that fits isn't enough. You also need a partner that moves at your pace, listens to your roadmap, and shows up when something breaks.
Not every analytics vendor understands SaaS. For most, embedded is a side use case — not the business. You want a vendor that treats embedded analytics as a complete platform, not a feature shelf.
An experienced team
- Is this a seasoned analytics team, or a startup figuring it out on the job?
- Do they actually understand the needs of SaaS providers?
- Do their values line up with yours?
Customer support
- What's the support policy — and what's the actual turnaround?
- How do they help you through the trial and the implementation?
Roadmap alignment
- Does the vendor share visibility into their roadmap?
- Without it, your ability to plan around what's coming gets quietly destroyed.
Pricing & licensing
For embedded, you need licensing that works with the business — not against it. Get straight answers to all of these:
- Is it strictly user-based licensing?
- Users plus data volume?
- Are there server licensing fees?
- How long are the contracts?
- Do fees scale with your customer count (a.k.a. revenue share)?
- Is unlimited-user licensing on the table?
Predicting your costs upfront is non-negotiable. Many BI vendors make it hard — especially the ones with user-based pricing. Ask early so there are no surprises after the contract is signed.
By the end of these six chapters, you should have a clear picture of what to bring to every vendor demo — and what to walk away from. The product fit decision is the one that quietly defines how the rest of the year goes.
Ready to evaluate specific vendors?
The Embedded Analytics Evaluation Guide picks up where this one ends — a framework for vendor evaluation, the questions to ask, and the platform limitations to watch for.
Read the guide →
