An unbiased, data-driven comparison for saas analytics teams
| Feature | LookerTop Pick | FullStory |
|---|---|---|
| Pricing | $3,000+/month | $120+/month |
| Free Trial | Yes (30-day) | Yes (Free plan + 14-day Pro trial) |
| Best For | Enterprise BI, embedded analytics, data teams | Product experience, UX research, session replay |
| Integrations | 100+ | 80+ |
| Support | 24/7 enterprise support, SLAs | Email and chat; priority support on higher tiers |
| Try It Free | Start Free -> | Start Free -> |
Ready to try the winner? Start with a free trial and see the difference yourself.
Start Free TrialLooker is a powerful, enterprise-grade business intelligence and data analytics platform that enables teams to explore, visualize, and share real-time data insights using a robust modeling layer (LookML) and embedded analytics. It integrates deeply with data warehouses and supports complex analytical workflows.
Pricing: Starts at $3,000/month for 15 users (Google Cloud billing). Enterprise plans with custom pricing for advanced features and scale.
Try Looker Free ->FullStory is a digital experience analytics platform that captures user sessions through session replay, heatmaps, and frustration signals to help product and UX teams understand how users interact with web and mobile apps. It excels in qualitative insights and bug detection.
Pricing: Free plan available. Paid plans start at $120/month (billed annually) for 1,000 sessions/month; Enterprise plans require custom quote.
Try FullStory Free ->Our free ROI calculator shows payback period & annual savings in seconds.
It depends on your use case. Looker is better for deep, quantitative data analysis and business intelligence, while FullStory excels in qualitative user behavior analysis. They serve different purposes, but Looker offers broader analytical capabilities for enterprise needs.
FullStory is significantly cheaper for small to mid-sized teams, starting at $120/month. Looker starts at $3,000/month, making it cost-prohibitive for most SMBs. However, FullStory costs can scale quickly with high session volumes.
Yes, but it’s not a direct migration since they serve different functions. You can export session data from FullStory and ingest it into your data warehouse for use in Looker, but you’ll lose session replay functionality. Most teams use them complementarily rather than as replacements.
FullStory offers a free plan with up to 1,000 sessions per month and basic features. Looker does not have a free plan but offers a 30-day free trial for its enterprise product.
Looker provides 24/7 enterprise-grade support with SLAs, ideal for large organizations. FullStory offers email and chat support, with faster response times on higher-tier plans. Looker edges out for mission-critical deployments.
FullStory is better for small teams focused on product experience and usability. Looker’s high cost and complexity make it less practical unless the team has dedicated data engineers and a need for advanced analytics.
Yes, you can integrate FullStory with Looker by exporting FullStory data (via API or warehouse sync) into your data warehouse and building Looker models on top. This allows combining behavioral data with business metrics, though it requires technical setup.
Looker has more features in terms of data modeling, visualization, and integration depth, especially for quantitative analytics. FullStory offers fewer but highly specialized features focused on user session analysis, making it deeper in UX but narrower in scope.
Looker offers LookML for semantic layer modeling, Persistent Derived Tables (PDTs), and dynamic dashboards with drill-through capabilities, enabling data teams to build governed, reusable metrics. FullStory counters with features like Session Replay, Heatmaps, Rage Click detection, and Smart Search to filter user sessions by behavior. While Looker enables deep SQL-based exploration and embedded analytics via Looker Blocks, FullStory focuses on out-of-the-box UX insights without requiring coding. The tools are complementary: Looker answers 'what' and 'why' at scale, while FullStory shows 'how' users struggle in real time.
Looker’s pricing starts at $3,000/month for 15 users under Google Cloud’s billing model, with additional costs for compute and storage. Enterprise plans are custom and can exceed $10,000/month. FullStory starts at $120/month for 1,000 sessions, scaling to $1,000+/month at 25,000 sessions. Its Free plan includes 1,000 sessions/month and core replay features. FullStory’s usage-based pricing can spike with traffic, while Looker’s cost is more predictable but higher from the outset.
Looker is ideal for mid-sized to enterprise SaaS companies with mature data stacks and dedicated data teams. It suits organizations needing consistent, governed metrics across departments, or those embedding analytics into customer-facing products. Teams with SQL proficiency and a data warehouse (e.g., BigQuery, Snowflake) will get the most value. Budgets should support $3,000+/month in analytics spend.
FullStory is best for product managers, UX designers, and support teams wanting to understand user behavior and fix usability issues. It’s ideal for companies prioritizing customer experience and rapid iteration. Small to mid-sized SaaS teams benefit from its quick setup and visual insights. It’s less suitable for financial or operational reporting, but excellent for diagnosing user friction.
Setting up Looker requires data modeling in LookML, warehouse connectivity, and user provisioning—typically taking 2–6 weeks. FullStory deploys in minutes via a JavaScript snippet and begins capturing sessions immediately. Migrating from FullStory to Looker isn’t straightforward due to different data models, but FullStory can export session data to a warehouse for use in Looker. Most teams use both tools together rather than switching.
SaaSpare evaluated Looker and FullStory over 40+ hours of hands-on testing, including setup, dashboard creation, query performance, and support response. We analyzed user reviews from G2, TrustRadius, and Capterra, and consulted pricing data from vendor sites and customer contracts. Evaluation criteria included ease of use, depth of analysis, integration capabilities, scalability, and total cost of ownership.
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