Best Analytics & CDP (2025)
Ranked picks for analytics & cdp. No "it depends."
Google Analytics
The free data black hole that marketers love and developers dread.
Full Rankings
Google Analytics
Nice PickThe free data black hole that marketers love and developers dread.
Pros
- +Free tier covers most small to medium sites
- +Integrates seamlessly with Google Ads and other Google services
- +Real-time reporting for quick insights
- +Massive community and extensive documentation
Cons
- -Privacy concerns and GDPR compliance headaches
- -Steep learning curve for advanced features
- -Data sampling can skew results on large datasets
The go-to for product analytics, if you can stomach the price tag and the occasional data swamp.
Pros
- +Powerful event-based tracking for granular user behavior insights
- +Intuitive funnels and retention reports that make sense to non-technical teams
- +Real-time data updates so you're not waiting hours for insights
- +Strong segmentation capabilities for targeting specific user cohorts
Cons
- -Pricing can skyrocket quickly as your event volume grows
- -Implementation can get messy without strict governance, leading to data bloat
The product analytics darling that makes you feel like a data wizard, until you realize you're just tracking clicks.
Pros
- +Intuitive funnel and retention analysis that actually helps you spot user drop-offs
- +Powerful user segmentation that lets you slice data by behavior without SQL
- +Real-time event tracking that updates dashboards faster than you can say 'A/B test'
- +Great for non-technical teams with drag-and-drop tools that don't require a data engineer
Cons
- -Pricing can skyrocket as your event volume grows, leading to sticker shock
- -Custom queries and advanced analytics still need workarounds or external tools
Open-source analytics that doesn't spy on your users, but might make you question your own product decisions.
Pros
- +Feature-rich
- +Self-hostable
- +Session replay
- +Feature flags
- +Self-hosted option keeps data in-house and avoids third-party cookie drama
- +Feature flags and A/B testing built-in, so you can iterate without deploying new code
- +Session recordings let you watch users struggle in real-time, which is both terrifying and enlightening
Cons
- -Complex
- -Resource-heavy
- -Overkill for simple sites
- -Self-hosting can turn into a DevOps nightmare if you're not prepared for the infrastructure
- -The UI can feel cluttered when you're drowning in event data, making simple insights harder to find
The data plumber you didn't know you needed until your analytics stack became a spaghetti mess.
Pros
- +Single API to collect once and route everywhere, saving dev time on custom integrations
- +Maintains data quality and compliance with built-in governance tools
- +Unifies customer profiles across sources for better insights
Cons
- -Pricing can escalate quickly with high event volumes
- -Complex setup for advanced routing and transformations
Automatic analytics that captures everything, so you can stop guessing what users actually do.
Pros
- +Auto-captures all user events without manual instrumentation
- +Retroactive analysis lets you query past data without pre-defining events
- +Intuitive visual interface for non-technical team members
- +Session replay and heatmaps integrated with analytics
Cons
- -Can become expensive quickly as data volume grows
- -Data sampling on free and lower-tier plans limits accuracy
- -Requires careful data governance to avoid noise from irrelevant events
The Swiss Army knife of big data, but good luck not cutting yourself on the complexity.
Pros
- +Unified engine for batch, streaming, SQL, and ML workloads
- +In-memory processing speeds up iterative algorithms dramatically
- +Fault-tolerant and scales to petabytes with ease
Cons
- -Configuration hell: tuning Spark is a full-time job
- -Memory management can be a nightmare in production
The Swiss Army knife of big data, but good luck tuning it without a PhD in distributed systems.
Pros
- +In-memory processing makes it blazing fast for iterative algorithms
- +Unified API for batch, streaming, ML, and graph workloads
- +Built-in fault tolerance and scalability across clusters
Cons
- -Memory management can be a nightmare to optimize
- -Steep learning curve for tuning and debugging in production
Head-to-head comparisons
Missing a tool?
Email nice@nicepick.dev and I'll add it to the rankings.