Looker alternative
Choose analytics that look and feel like your product, not a separate BI platform
| Sisense | Looker |
|---|---|
 AI: Sisense helps teams embed AI-powered analytics directly into their products |
 Looker’s generative AI capabilities are designed for use within Looker and Google Cloud experiences |
| Embedding: Sisense is purpose-built for embedded AI and analytics |
Looker offers embedding alongside broad BI capabilities |
| Customization:Â Sisense lets teams build analytics that feel like a natural part of their products |
Looker customizations are built through its LookML modeling layer |
| Developers: Sisense gives developers full control over analytics experiences with APIs and SDK |
Embedding in Looker requires devs to manage signed URLs to securely build |
When teams are building customer-facing, AI-powered analytics that feel like a part of their product, they seek alternatives to Looker. Here’s how Sisense compares:
| Sisense | Looker | |
|---|---|---|
| Purpose-built for embedded analytics | |
BI-first embedding |
| Flexible pricing models | |
Platform pricing plus user licencing |
| Analytics that feel natural to your product | |
Built for BI and self service analytics first |
| API-first developer experience | |
Signed URL and authentication token embedding |
| Analytics creator tools for all technical proficiencies | |
Requires LookML for data modeling |
| AI experiences embedding into your app | |
Gemini features in Looker |
| Strong security and governance | |
|
"Migrating from Looker to Sisense has driven key innovation for our data visualization teams. We’ve cut time-to-market by 90%, reduced cloud costs by 10%, and received overwhelmingly positive feedback on our modern, beautiful dashboards."
The leader in embedded analytics with 500+ 5-star reviews
Frequently asked questions about Sisense vs. Looker
Sisense is designed to help teams build analytics experiences that feel like a natural part of their products, giving developers control over embedding, customization, and user experiences. Google describes Looker as a business intelligence platform that includes data exploration, dashboarding, embedding, and API capabilities.
Sisense is purpose-built for embedded AI use cases and provides APIs and SDKs that help teams create analytics experiences for end-users. Looker supports embedding through capabilities such as signed embedding, Embed SDKs, and authentication workflows.
Sisense gives developers APIs and SDKs to customize analytics experiences and embed them into applications. Looker’s embedding approach uses signed URLs, embed secrets, permissions, and authentication to securely deliver analytics experiences.
Sisense helps organizations embed AI-powered analytics directly into their products, including fully customizable conversational analytics and natural language interactions for end-users. Google’s Gemini capabilities in Looker help users explore data, generate visualizations, and work with LookML primarily within the Looker platform.
LookML is Looker’s modeling language and is used to define dimensions, measures, calculations, and relationships within Looker projects. Google documentation describes LookML as a way for data experts to model and organize analytics. Sisense does not require teams to learn a proprietary modeling language, giving creators more flexibility to connect, model, and build analytics experiences using pro-, low-, and no-code tools.
Sisense offers flexible pricing designed to support embedded analytics and product growth. Google states that Looker pricing includes both platform pricing and user pricing.
Sisense is a strong alternative for teams that want analytics to feel like part of their product, provide AI experiences to end-users, and give developers control over customization and embedding. Organizations evaluating embedded analytics often compare Sisense and Looker based on developer experience, customization options, AI capabilities, and product integration requirements.
