The Best Sisense Alternative for Startups
Sisense is powerful for embedding analytics, but you still need to build and maintain the entire data pipeline, warehouse, and semantic layer underneath—and pricing starts in the tens of thousands with add-on fees. Definite gives startups the whole stack from ingestion to AI insights, live in under 30 minutes, without hiring engineers.
And you won't need to hire a data engineer to manage it.
How Sisense and Definite Stack Up
Sisense
API-first embedded analytics layer that depends on your own pipelines, warehouse, and developer resources.
Key Features:
Pricing:
No permanent free option—engage sales for trials.
Sisense offers proof-of-concept pilots via sales engagement rather than a self-serve free tier. Evaluation access requires coordination with their account team and comes with usage limits.
Entry-level embedded analytics package.
AWS Marketplace pricing starts around $40,000 annually for Essential tier licensing. Costs rise with additional users, tenants, API volume, premium AI features, and advanced support. Expect further spend on engineering time, ELT tooling, and data warehousing.
Not Included:
Managed data ingestion, governed semantic layer, cloud storage costs, or engineering resources to build and maintain pipelines and embedded experiences.
Definite
Complete data platform with ingestion, storage, governed metrics, AI, and dashboards—all in one.
Key Features:
Pricing:
Access to Growth features for qualifying startups.
Comprehensive tools to enable your business to make data driven decisions.
See What’s Included:
The Full Comparison
Sisense excels at embedded dashboards once you supply data pipelines, models, and engineers. Definite delivers the entire modern data stack—connectors, storage, governed metrics, AI, and activation—so startups get answers fast without building infrastructure.
Setup & Time-to-Value
Live dashboards in under 30 minutes—connect sources, pick governed metrics, ship insights the same day.
Implementation depends on your engineering team building pipelines, modeling data, and embedding SDKs—often weeks before first production dashboards.
Stack Coverage
All-in-one: ingestion, storage, semantic modeling, visualization, AI, and activation in a single platform.
Visualization and embedding only. Relies on external ELT, warehousing, and transformation layers you maintain.
Data Ingestion
Managed 500+ connectors with automated syncs, schema mapping, and anomaly monitoring.
Connects to existing databases and lakes but expects you to ingest data elsewhere first.
Storage & Query Engine
Managed lakehouse plus DuckDB-powered engine for sub-second performance, no tuning required.
No native storage. Requires external warehouses and cube optimization to keep embeds responsive.
Modeling & Metrics
Governed semantic layer (Cube.dev) with reusable, no-code metrics and dbt compatibility.
Custom modeling via developers and Sisense elasticubes; governance handled manually per project.
AI & Insights
Fi AI analyst answers questions in natural language, explains trends, and automates briefs out of the box.
AI features focus on assisted insights within embeds but require configuration, premium tiers, and curated datasets.
Automation & Alerts
Slack, email, Sheets, and webhook automations with no-code triggers and Python extensions.
Basic scheduled alerts; deeper workflow automation demands custom scripting against APIs.
Governance & Access
Role-based access down to metric level, centralized definitions keep every team on the same number.
Tenant isolation and role controls exist, yet KPI definitions live in code and vary by implementation.
APIs & Extensibility
REST and GraphQL APIs plus embedding options built on open standards—use or ignore as needed.
Rich SDKs for embedding and theming, but success hinges on developer bandwidth and ongoing maintenance.
Ideal For
Startups that want enterprise-grade analytics without assembling or operating the stack.
Product teams with engineering capacity to embed analytics into their apps and manage supporting infrastructure.
| Category | Definite | Sisense |
|---|---|---|
| Setup & Time-to-Value | Live dashboards in under 30 minutes—connect sources, pick governed metrics, ship insights the same day. | Implementation depends on your engineering team building pipelines, modeling data, and embedding SDKs—often weeks before first production dashboards. |
| Stack Coverage | All-in-one: ingestion, storage, semantic modeling, visualization, AI, and activation in a single platform. | Visualization and embedding only. Relies on external ELT, warehousing, and transformation layers you maintain. |
| Data Ingestion | Managed 500+ connectors with automated syncs, schema mapping, and anomaly monitoring. | Connects to existing databases and lakes but expects you to ingest data elsewhere first. |
| Storage & Query Engine | Managed lakehouse plus DuckDB-powered engine for sub-second performance, no tuning required. | No native storage. Requires external warehouses and cube optimization to keep embeds responsive. |
| Modeling & Metrics | Governed semantic layer (Cube.dev) with reusable, no-code metrics and dbt compatibility. | Custom modeling via developers and Sisense elasticubes; governance handled manually per project. |
| AI & Insights | Fi AI analyst answers questions in natural language, explains trends, and automates briefs out of the box. | AI features focus on assisted insights within embeds but require configuration, premium tiers, and curated datasets. |
| Automation & Alerts | Slack, email, Sheets, and webhook automations with no-code triggers and Python extensions. | Basic scheduled alerts; deeper workflow automation demands custom scripting against APIs. |
| Governance & Access | Role-based access down to metric level, centralized definitions keep every team on the same number. | Tenant isolation and role controls exist, yet KPI definitions live in code and vary by implementation. |
| APIs & Extensibility | REST and GraphQL APIs plus embedding options built on open standards—use or ignore as needed. | Rich SDKs for embedding and theming, but success hinges on developer bandwidth and ongoing maintenance. |
| Ideal For | Startups that want enterprise-grade analytics without assembling or operating the stack. | Product teams with engineering capacity to embed analytics into their apps and manage supporting infrastructure. |
One Choice: One Platform
Definite pulls in all your data, stores it in a fast, reliable data warehouse, applies consistent models, turns it into interactive dashboards, and lets you query it with AI — all in one managed platform. (Skip the three-month build, five different vendors, and full-time data engineer.)
Fi can work with any part of Definite’s Ecosystem to get you results faster.
Sync data in near‑realtime latency from over 500 sources. Need a custom connection? We’ll build it for you.
Send scheduled or triggered alerts, sync data to Google Sheets or your CRM, or export it as downloads or from the API.
Make Every Decision Definite
Sisense gives you a powerful embedded analytics toolkit—but you still need to fund the data stack, write the code, and maintain the infrastructure. Definite bundles ingestion, storage, governed metrics, AI, and activation into one platform so your team gets answers fast without building BI from scratch.
Sisense gives you an embedded layer you must wire up. Definite gives you the complete data platform with governed metrics and AI built in.
Frequently Asked Questions
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Common questions about Definite vs Sisense