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Top 7 Alternatives to LangSmith for Product Analytics
Teams exploring alternatives to LangSmith typically cite similar concerns: steep pricing at scale, limited product analytics capabilities, and the complexity of managing separate tools for experimentation and user behavior tracking.
LangSmith excels at LLM observability but forces teams to cobble together multiple platforms for comprehensive product insights. The tool's developer-centric design leaves product managers and analysts struggling to extract actionable metrics about user engagement and feature adoption. Most critically, LangSmith lacks the behavioral analytics, experimentation infrastructure, and self-service capabilities that modern product teams need.
This guide examines seven alternatives that address these pain points while delivering the product analytics capabilities teams actually need.
Alternative #1: Statsig
Overview
Statsig delivers enterprise-grade product analytics alongside experimentation, feature flags, and session replay in one unified platform. The system processes over 1 trillion events daily with 99.99% uptime, serving billions of users across companies like OpenAI, Notion, and Brex.
Unlike LangSmith's focus on LLM observability, Statsig provides comprehensive behavioral analytics with funnels, retention curves, user paths, and cohort analysis. Teams can track DAU/WAU/MAU, build custom dashboards, and analyze conversion metrics without SQL knowledge.
"Statsig's powerful product analytics enables us to prioritize growth efforts and make better product choices during our exponential growth with a small team." — Rose Wang, COO, Bluesky
Key features
Statsig's product analytics toolkit matches industry leaders like Amplitude and Mixpanel while integrating seamlessly with experimentation workflows.
Core analytics capabilities
Advanced funnel analysis to identify drop-offs and optimize conversion paths
Retention curves with L7/L14/L28 analysis and stickiness metrics
User journey mapping to understand behavior patterns before and after key actions
Real-time dashboards processing trillions of events without latency
Segmentation and cohorts
Define custom cohorts like power users or churn risks
Analyze how different segments engage with your product
Track engagement patterns across user groups
Export cohort data for deeper analysis
Self-service analytics
No SQL required for building reports and dashboards
One-click access to underlying SQL queries for transparency
Collaborative tools for sharing insights across teams
Integration advantages
Native connection to feature flags and experiments
Automatic impact measurement for every release
Warehouse-native deployment for data control
"The biggest benefit is having experimentation, feature flags, and analytics in one unified platform. It removes complexity and accelerates decision-making." — Sumeet Marwaha, Head of Data, Brex
Pros vs. LangSmith
Comprehensive behavioral analytics
Statsig offers full product analytics capabilities that LangSmith lacks, including funnels, retention analysis, and user pathing. Teams can understand user behavior beyond just LLM interactions - they can track complete product journeys and conversion metrics.
Cost-effective event pricing
Statsig provides the most affordable analytics pricing at every scale. The platform includes 2M free events monthly and costs significantly less than competitors like PostHog or Amplitude at higher volumes.
Unified platform benefits
Every feature release automatically connects to analytics, eliminating manual instrumentation. Teams can launch features, measure impact, and iterate without switching tools or reconciling data across systems.
Enterprise-grade scalability
Processing over 1 trillion daily events with 99.99% uptime ensures reliability at any scale. Companies like OpenAI and Notion trust Statsig for mission-critical analytics without performance degradation.
"Having a culture of experimentation and good tools that can be used by cross-functional teams is business-critical now. Statsig was the only offering that we felt could meet our needs." — Sriram Thiagarajan, CTO, Ancestry
Cons vs. LangSmith
Limited LLM-specific tracing
Statsig lacks native prompt-level tracing and token usage tracking that LangSmith provides. Teams need custom event logging to capture granular LLM metrics like latency per prompt or token consumption.
No built-in LLM evaluation tools
Unlike LangSmith's specialized LLM testing features, Statsig requires manual setup for prompt evaluation workflows. Teams must build custom metrics to track model performance and prompt effectiveness.
Missing LangChain integration
Statsig doesn't offer direct LangChain/LangGraph integration like LangSmith. Developers using these frameworks need additional instrumentation to connect their LLM pipelines to Statsig's analytics.
Alternative #2: Amplitude
Overview
Amplitude specializes in self-service behavioral analytics with powerful segmentation and journey tools widely adopted by growth teams at consumer companies. The platform focuses on understanding user behavior patterns through event tracking and cohort analysis rather than LLM-specific observability.
Product teams choose Amplitude when they need to understand user journeys and conversion funnels without relying on engineering resources. The platform excels at answering growth questions: Which features drive retention? Where do users drop off? What behaviors predict conversion?
Key features
Amplitude provides comprehensive product analytics designed for behavioral analysis and user journey optimization.
Behavioral analytics
Event tracking with automatic property capture and custom event definitions
User journey mapping with detailed funnel analysis and drop-off identification
Cohort analysis with retention curves and behavioral segmentation tools
Path analysis revealing common user flows and unexpected navigation patterns
Predictive capabilities
Predictive cohorts identify users likely to convert or churn before they take action
Recommendation APIs enable personalized user experiences based on behavior patterns
Multi-touch attribution modeling tracks marketing impact across channels
Statistical significance testing built into all comparative analyses
Self-service analytics
Drag-and-drop chart builder empowers non-technical stakeholders
Pre-built templates accelerate common analytics workflows
Real-time dashboards update immediately as users interact with your product
Natural language queries help teams find insights without learning query syntax
Integration ecosystem
Native connections to marketing platforms, CDPs, and data warehouses
SDK support spans web, mobile, and server-side implementations
Data export capabilities feed external analytics and BI tools
Reverse ETL pushes behavioral insights back to operational systems
Pros vs. LangSmith
Mature analytics ecosystem
Amplitude brings years of product analytics refinement that LangSmith's newer platform lacks. The depth shows in advanced features like predictive analytics, sophisticated attribution models, and recommendation engines purpose-built for growth teams.
Intuitive user interface
Non-technical team members regularly build complex analyses without engineering support. This self-service capability stands in sharp contrast to LangSmith's developer-focused console that typically requires technical expertise to navigate effectively.
Extensive integration library
The platform connects seamlessly with the modern data stack. Marketing tools sync user segments automatically, CDPs receive behavioral data in real-time, and business intelligence platforms pull insights directly. These integrations create comprehensive analytics workflows that extend far beyond LangSmith's LLM-focused connections.
Proven scalability
Amplitude handles massive event volumes for consumer applications processing billions of events monthly. Netflix, Peloton, and Shopify rely on the platform for mission-critical analytics - validation that speaks to both reliability and performance at scale.
Cons vs. LangSmith
No native LLM tracing
Amplitude lacks any built-in capabilities for monitoring LLM performance. Teams building AI applications need separate tools for tracking prompts, responses, or model metrics. The gap becomes particularly painful when trying to correlate user behavior with AI feature performance.
Limited experimentation depth
Basic A/B testing exists, but Amplitude doesn't provide the statistical rigor of dedicated experimentation platforms. Teams requiring sophisticated experiment design, CUPED variance reduction, or multi-armed bandits must supplement with additional tools.
Expensive at high volumes
Pricing scales aggressively with monthly tracked users and event volumes. Product analytics platform costs analysis shows Amplitude ranking among the most expensive options once you exceed initial tiers.
Separate pricing for data export
Advanced features like data warehouse exports and custom integrations require higher-tier plans. This contrasts with LangSmith's approach of bundling core functionality, creating unexpected costs as teams mature their analytics practices.
Alternative #3: Mixpanel
Overview
Mixpanel delivers real-time product usage insights through event-based tracking and fast ad-hoc querying. While open-source alternatives like Langfuse focus on LLM observability, Mixpanel takes a different approach as a general-purpose product analytics platform.
Startups favor Mixpanel when they need quick answers without heavy data infrastructure. The platform's strength lies in its approachable interface - teams can start tracking events and building reports within hours of implementation.
Key features
Mixpanel's product analytics capabilities center on event tracking, user segmentation, and behavioral analysis across platforms.
Event tracking and analysis
Custom event properties capture detailed context about user actions
Real-time data processing shows results immediately after implementation
Interactive dashboards support drag-and-drop query building without SQL
Retroactive cohort analysis applies new definitions to historical data
User segmentation and cohorts
Dynamic segments update automatically as users meet criteria
Behavioral cohorts track groups based on actions rather than attributes
Time-based cohorts analyze user retention across signup periods
Cross-platform identity resolution links users across devices
Mobile and web SDKs
Native SDKs support iOS, Android, JavaScript, and server implementations
Automatic tracking captures page views, clicks, and form submissions
Cross-platform user identification maintains consistent analytics
Offline event queuing ensures data completeness on mobile devices
Reporting and visualization
Funnel analysis pinpoints exact conversion bottlenecks
Retention reports show engagement patterns over custom time windows
Formula calculations combine multiple events and properties
Alert notifications trigger when metrics cross defined thresholds
Pros vs. LangSmith
Approachable setup and user interface
Mixpanel's visual query builder requires zero coding knowledge. Marketing teams, product managers, and analysts all build reports independently. This accessibility dramatically reduces the analytics bottleneck common with technical tools.
Strong mobile SDK support
Native mobile SDKs provide robust tracking that handles offline scenarios gracefully. The SDKs automatically manage session tracking, device properties, and user identification - critical for teams building mobile-first AI applications.
Generous free tier for experimentation
The free plan includes 100,000 events monthly, letting teams validate their tracking approach without budget approval. Small teams exploring product analytics can run meaningful experiments before committing to paid plans.
Real-time data processing
Events appear in dashboards within seconds of occurring. This immediacy enables rapid iteration during feature development and instant validation of tracking implementations.
Cons vs. LangSmith
Manual event implementation required
Every user action needs explicit tracking code, unlike LangSmith's automatic LLM tracing. Engineers must identify relevant events, implement tracking, and maintain consistency across codebases. The manual process creates ongoing maintenance burden.
Limited LLM-specific features
Mixpanel provides no specialized tools for prompt tracking, token usage monitoring, or model performance analysis. AI teams end up building complex workarounds to capture LLM-specific metrics within Mixpanel's generic event model.
Basic statistical testing capabilities
A/B testing exists but lacks sophisticated evaluation methods. No sequential testing, variance reduction techniques, or Bayesian approaches that LangSmith alternatives designed for AI applications typically include.
No session replay or feature flag integration
Pure event analytics miss qualitative context about user struggles. Teams need separate tools for session recordings, feature management, and comprehensive user experience monitoring - creating the multi-tool sprawl that unified platforms avoid.
Alternative #4: PostHog
Overview
PostHog delivers open-source product analytics with autocapture, session replay, and feature flags in a single platform. You deploy it self-hosted for complete data control or use their cloud version with usage-based pricing.
The platform appeals to engineering teams who want flexibility without vendor lock-in. PostHog's approach differs from specialized tools by offering broader analytics alongside LLM monitoring capabilities. According to discussions on Reddit, developers appreciate consolidating their analytics stack.
Key features
PostHog combines traditional product analytics with modern experimentation and observability tools.
Analytics and tracking
Autocapture records clicks, pageviews, and inputs without manual instrumentation
Custom events track specific LLM interactions and model responses
Conversion funnels visualize user paths through AI-powered features
Cohort analysis segments users based on interaction patterns
Feature management
Feature flags enable percentage rollouts and targeted deployments
A/B tests run with built-in statistical significance calculations
Instant rollbacks protect against problematic releases
Multivariate testing supports complex experiment designs
LLM observability
Custom dashboards monitor latency and error rates for AI features
Event properties capture prompt variations and response quality
User session correlation links AI interactions to broader behavior
Performance tracking identifies slow or failing model endpoints
Deployment options
Self-host on your infrastructure with complete data ownership
Cloud deployment scales automatically with usage-based pricing
One-click installations on AWS, GCP, and Azure
Docker and Kubernetes support for containerized deployments
Pros vs. LangSmith
Open-source flexibility
Complete source code access lets you modify PostHog for specific needs. Self-hosting eliminates vendor dependencies and keeps sensitive user data within your infrastructure. The open-source model means zero licensing fees for core analytics functionality.
Unified analytics platform
Combining product analytics, feature flags, and session replay reduces context switching. You correlate LLM performance with user behavior, conversion metrics, and qualitative feedback in one interface. This integration reveals insights that siloed tools miss.
Cost-effective scaling
Self-hosted deployments cost only infrastructure expenses regardless of event volume. Cloud pricing remains predictable with transparent per-event billing. Analysis shows PostHog offers competitive pricing for mid-scale deployments.
Strong community support
Active contributors regularly release plugins and integrations. GitHub discussions provide rapid troubleshooting from both community members and the PostHog team. Extensive documentation covers everything from basic setup to advanced configurations.
Cons vs. LangSmith
Limited LLM-specific features
PostHog lacks specialized tracing for complex chains and agent workflows. Custom instrumentation becomes necessary for detailed LLM interaction tracking. The platform offers no built-in prompt versioning or model-specific evaluation metrics.
Cloud pricing complexity
High-volume applications generate expensive bills through usage-based pricing. Feature flag checks and session replay minutes add separate costs. Cost comparisons indicate PostHog ranks as second-most expensive for feature flags at scale.
Self-hosting overhead
Running PostHog requires significant DevOps expertise and ongoing maintenance. Your team handles updates, security patches, and infrastructure scaling. This operational burden often exceeds the cost savings from avoiding SaaS fees.
Statistical engine limitations
Basic A/B testing lacks advanced methods like CUPED or sequential analysis. The platform provides no sophisticated experiment design tools needed for rigorous LLM evaluation. Teams supplement with external statistical packages for complex analyses.
Alternative #5: Heap
Overview
Heap takes a fundamentally different approach by automatically capturing every user interaction without manual event tracking. This autocapture methodology eliminates upfront engineering work, making it attractive for teams with limited development resources.
The platform records all clicks, form submissions, and page views by default. You retroactively define events and analyze historical data - addressing the common analytics pain point of missing data from events you didn't anticipate needing.
Key features
Heap's core strength lies in comprehensive autocapture capabilities and retroactive analysis tools.
Automatic data collection
Records all user interactions without manual event instrumentation
Captures clicks, form submissions, and page views automatically
Maintains complete historical data for retroactive event definition
Tracks custom properties and user attributes without code changes
Visual event creation
Point-and-click interface defines events on live web pages
No-code event creation eliminates developer dependencies
Visual labeling system organizes and categorizes user actions
Retroactive event definitions apply to all historical data
Advanced analytics features
Comprehensive funnel analysis identifies conversion optimization opportunities
Retention cohort analysis tracks user engagement over time
User journey mapping reveals common paths and unexpected flows
Attribution modeling connects user actions to business outcomes
Enterprise governance
Data governance controls manage privacy and compliance requirements
Role-based access ensures appropriate data visibility
Advanced segmentation enables complex user group analysis
Data warehouse integration syncs with existing BI infrastructure
Pros vs. LangSmith
Instant data availability
Heap's autocapture eliminates setup delays inherent in manual tracking. You start analyzing behavior immediately while competitors still plan their event taxonomy. This speed advantage proves crucial during rapid product iteration.
Retroactive analysis capabilities
Define new events and immediately analyze months of historical data. LangSmith's forward-only trace collection can't match this flexibility. Teams discover insights in existing data without waiting for new collection periods.
Reduced engineering overhead
Product managers and analysts create events independently using visual tools. No tickets, no sprints, no deployment cycles. This autonomy accelerates insight discovery and reduces analytics bottlenecks.
Comprehensive user journey mapping
Detailed flow analysis shows complete paths through your product. Understanding these journeys requires significant custom development in observability-focused tools. Heap provides these insights out of the box.
Cons vs. LangSmith
Data noise and relevance
Autocapture generates massive datasets filled with irrelevant interactions. Finding signal in this noise requires careful event definition and filtering. LangSmith's targeted logging provides cleaner, more focused datasets.
Limited LLM observability
Heap offers no native support for tracing AI interactions or model metrics. Teams building LLM applications need additional tools for comprehensive observability beyond surface-level user interactions.
Performance impact at scale
Heavy autocapture scripts slow page load times and impact user experience. Query performance degrades with large data volumes. LangSmith's selective logging approach maintains better performance for high-traffic applications.
Complex pricing structure
Session-based pricing becomes expensive as traffic grows. Advanced features require additional licenses. The model lacks transparency compared to straightforward usage-based alternatives discussed in comprehensive LangSmith alternative guides.
Alternative #6: FullStory
Overview
FullStory approaches analytics through digital experience intelligence, combining session replay with frustration detection to surface UX issues impacting revenue. The platform captures complete user sessions and automatically identifies rage clicks, dead clicks, and error patterns.
While LangSmith alternatives typically focus on technical observability, FullStory targets the user experience layer. This makes it valuable for understanding how users actually interact with AI features rather than just monitoring the underlying technology.
Key features
FullStory centers on capturing and analyzing user behavior through digital experience data.
Session replay and capture
Autocapture DOM interactions without manual instrumentation
Pixel-perfect session playback reveals exact user behavior
Support for single-page applications and complex frameworks
Privacy controls automatically mask sensitive information
Frustration detection
Rage click algorithms identify user confusion automatically
Dead click monitoring reveals broken interface elements
Error click tracking surfaces technical issues affecting users
Frustration scores quantify overall experience quality
Conversion analysis
Funnel analysis tracks progression through key workflows
Impact measurement connects UX issues to business metrics
Segment-based analysis reveals experience variations
Revenue attribution links frustration to lost conversions
Privacy and compliance
Automatic PII masking protects sensitive user data
GDPR and CCPA compliance features built into collection
Granular permissions control team access to recordings
Data retention policies align with regulatory requirements
Pros vs. LangSmith
Superior qualitative insights
Visual session replay provides context that text logs can't match. You watch exactly how users interact with AI features, understanding confusion and delight moments. This visual evidence proves invaluable when advocating for UX improvements.
Objective frustration metrics
FullStory quantifies user struggle through algorithmic detection. These metrics help prioritize fixes based on actual user impact rather than technical severity. Rage click counts speak louder than error logs to stakeholders.
No instrumentation overhead
Autocapture eliminates manual tracking setup entirely. You get comprehensive behavior data immediately without modifying application code. This approach reduces implementation friction compared to manual observability tools.
Revenue impact visibility
The platform directly connects UX friction to conversion metrics and revenue loss. This business impact data justifies improvement investments more effectively than technical metrics alone.
Cons vs. LangSmith
Limited technical depth
FullStory provides no application tracing, API monitoring, or system debugging capabilities. Engineering teams need separate tools for technical performance insights that LangSmith delivers natively.
No experimentation capabilities
The platform lacks A/B testing or feature flag functionality. Controlled rollouts and hypothesis testing require additional tools, unlike comprehensive platforms bundling these capabilities.
Session-based pricing escalation
Costs scale aggressively with session volume, creating budget pressure for high-traffic applications. Many teams discover pricing becomes prohibitive as they grow beyond initial tiers.
Shallow product analytics
While capturing interactions, FullStory doesn't provide deep analytics for cohort analysis, retention tracking, or advanced segmentation. Teams typically need supplementary tools for comprehensive product measurement.
Alternative #7: Pendo
Overview
Pendo combines in-app guidance, feedback collection, and product analytics into a platform designed for SaaS companies. The tool emphasizes user onboarding, feature adoption, and customer education through interactive guides alongside behavioral tracking.
Product teams choose Pendo when they need to understand not just user actions but the reasoning behind behaviors. The platform bridges quantitative analytics with qualitative feedback through integrated survey tools and in-app messaging.
Key features
Pendo spans user guidance, feedback collection, and product analytics with comprehensive platform support.
In-app guidance and messaging
Interactive walkthroughs guide users through complex workflows
Targeted tips appear based on user behavior patterns
Resource centers provide self-service help documentation
Announcement banners communicate updates and changes
Feedback and survey tools
NPS surveys measure satisfaction at key journey points
Custom polls gather specific feature feedback
In-app feedback widgets collect qualitative insights
Response data integrates with behavioral analytics
Product analytics capabilities
Path analysis tracks user journeys and drop-off points
Feature usage metrics show adoption rates over time
Cohort analysis segments users by behavior patterns
Retention tracking monitors engagement trends
Mobile and web SDKs
Cross-platform tracking maintains consistency across devices
Event capture works automatically for standard interactions
Custom event tracking handles unique user actions
Real-time processing provides immediate insights
Pros vs. LangSmith
Combines quantitative and qualitative insights
Pendo merges behavioral data with direct user feedback in one platform. This combination reveals the "why" behind user actions that pure observability tools miss. Understanding motivation drives better product decisions than metrics alone.
Built-in user engagement tools
Native messaging and guidance features let you act on insights immediately. Deploy targeted interventions based on analytics findings without tool switching. This tight loop from insight to action accelerates improvement cycles.
Strong onboarding and adoption focus
The platform excels at measuring and improving feature discovery. Guided experiences reduce time-to-value while analytics track progress. This focus on adoption helps teams maximize their product investments.
Comprehensive mobile analytics
Robust mobile SDKs provide detailed app usage insights across iOS and Android. Cross-platform tracking maintains data consistency between web and mobile. This unified view proves essential for multi-platform products.
Cons vs. LangSmith
No native LLM observability
Pendo lacks any specialized tools for AI monitoring or model performance tracking. Teams building LLM-powered features need additional tooling for technical observability beyond user interactions.
Implementation complexity across platforms
Setting up Pendo requires injecting scripts across web, mobile, and backend systems. The process demands coordination across engineering teams. This complexity delays time-to-value compared to simpler solutions.
Pricing scales with user volume
Monthly active user pricing creates budget pressure as products grow. Advanced features require premium packages that significantly increase costs. The pricing model punishes success with higher bills.
Limited technical debugging capabilities
Pendo focuses on user experience metrics rather than technical performance. Engineering teams need separate tools for application debugging, error tracking, and infrastructure monitoring. This gap creates tool sprawl for technical teams.
Closing thoughts
Choosing the right LangSmith alternative depends on your team's specific needs. If you need comprehensive product analytics with built-in experimentation, Statsig offers the most complete solution at competitive pricing. Teams focused purely on behavioral insights might prefer Amplitude or Mixpanel's specialized analytics. Those prioritizing data control should evaluate PostHog's open-source flexibility.
The key is selecting a platform that grows with your needs. Many teams start with basic analytics but eventually require experimentation, feature management, and deeper insights. Choosing a unified platform from the start prevents the painful migration and tool proliferation that fragments your data and slows decision-making.
For teams ready to explore these alternatives, Statsig offers a generous free tier with 2 million events monthly. You can experiment with the full platform capabilities before committing to paid plans.
Hope you find this useful!