AI Research Engineer

Trodo vs PostHog: which product analytics tool for ai-native teams? Trodo and PostHog both call themselves product analytics platforms, but they target different problems. Here is a clear comparison for teams shipping AI-native products in 2026. Trodo vs PostHog PostHog alternative PostHog AI analytics AI product analytics open source product analytics product analytics for AI PostHog is one of the most popular product analytics platforms of the last few years - open-source, developer-friendly, and broad in scope. Trodo is a newer platform built specifically for AI-native product teams. The two tools overlap on the surface (both call themselves product analytics) but diverge sharply once you look at what they actually optimize for. This post compares them honestly. Short version. PostHog is general-purpose product analytics with a strong open-source story and a wide feature set including session replay and feature flags. Trodo is purpose-built AI product analytics for teams where prompts, agents, and tool calls are core to the product experience. PostHog is the better fit for traditional SaaS that has light AI usage. Trodo is the better fit when AI is the product. Where PostHog shines. * Open source with a self-host option - appealing for cost-sensitive teams or strict data residency. * Bundled session replay, feature flags, and experimentation in one tool. * Mature SQL-based query layer for engineers who want full control. * Strong developer ergonomics and a large community. For traditional SaaS or B2C apps where the product is mostly clicks and forms, PostHog is a strong default. The question becomes harder when AI features start to dominate the experience. Where Trodo shines. * Native modeling of prompts, completions, tool calls, and agent runs - not just events. * AI feature adoption, prompt-level funnels, and agent-driven retention out of the box. * Engineering and product see the same traces, joined to the same users and sessions. * Natural-language querying so PMs and growth leads can answer their own questions. * AI-generated PRDs from real product signal. * Built-in agent observability - traces, latency, errors, and cost on the same surface. The data-model gap. The hardest difference to see at a glance is the data model. PostHog (like Mixpanel and Amplitude) is built around a flat events table - each row is a "user did thing." Modeling an agent run in flat events means choosing how to compress hierarchy into properties, and that compression loses information. You either record one event per run (and lose the steps) or record one event per step (and lose the relationship between them). Trodo models hierarchical traces natively. An agent run is a tree of spans (plan | tool calls | completion | user action), each with full metadata, all joined to the user. Funnels can step through that tree. Retention cohorts can filter on it. Nothing flattens unless you ask it to. Cost and total cost of ownership. PostHog's open-source self-host option looks cheap on paper, but the real cost includes engineering time to operate the cluster, build AI-native instrumentation that the platform was not designed for, and maintain that custom layer as agents evolve. Trodo is hosted SaaS with a free tier up to 1M events/month - most AI-native startups land on lower total cost of ownership once the engineering time of "make PostHog understand my agents" is included. When to choose PostHog. Choose PostHog when AI features are <20% of user activity, when bundled session replay and feature flags are valuable to you, when self-hosting matters for compliance or cost, or when SQL-style flexibility outweighs AI-native modeling. When to choose Trodo. Choose Trodo when AI is core to the product, when prompts and agent runs need first-class analytics treatment, when product and engineering need a shared source of truth, or when natural-language querying for non-engineers is a real requirement. Most AI-native startups in 2026 fall into this bucket. Can you use both? Yes - some teams keep PostHog for session replay and feature flags while moving AI product analytics and agent observability to Trodo. The trade-off is maintaining two analytics surfaces. Most teams eventually consolidate; which way you consolidate depends on whether AI or classic product is the bigger workload. Bottom line. PostHog is great general-purpose product analytics. Trodo is purpose-built AI product analytics. If your product is AI, Trodo is the better default. If AI is a feature, PostHog is fine until it isn't - and most teams find the moment it isn't comes faster than expected.

Analyze landing page metrics with posthog. April 15, 2026 PostHog is an open-source product analytics platform that tracks website traffic, user behavior, and engagement metrics. Sliq connects to PostHog so your AI assistant can give you daily traffic digests, answer questions about your landing page performance (top pages, traffic sources, bounce rate), and run custom queries - all from chat. This guide walks you through setting up PostHog from scratch and connecting it to Sliq. Create a posthog account. * On the product selection screen, select Web Analytics only. You can deselect everything else (Product Analytics, Session Replay, etc.) - Sliq only needs web analytics data. Install posthog on your website. This is the most important step. PostHog needs a JavaScript snippet on your website to collect pageview and click data. Without it, there's no data for Sliq to read. PostHog provides a wizard that auto-detects your framework and installs the SDK for you. Run this from your website project's root directory: npx -y @posthog/wizard@latest The wizard will auto-detect your framework, install the PostHog package, and set up event tracking in your code. Just follow the prompts - it supports Next.js, React, Vue, Nuxt, Astro, SvelteKit, Angular, Django, Flask, and more. Note: Run this in your website's codebase, not in Sliq. This installs PostHog's tracking snippet on your site so it can start sending analytics events. The wizard will detect your project directory and framework, then ask you to continue: The wizard will analyze your project, install the PostHog package, and insert tracking code automatically. This takes a few minutes. The wizard may ask if you want to install the PostHog MCP server for your code editor. This is optional and not required for the Sliq integration - feel free to install it if you'd like to query PostHog from your editor, or skip it. When you see "PostHog integration complete", you're done with this step. Configure tracking options. PostHog will ask which tracking features to enable. Here's what each one does: * Autocapture frontend interactions (required) - Automatically captures clicks, form submits, and other interactions on your site. Sliq uses this data to show you which buttons and links your visitors click most. Make sure this is on. * Enable heatmaps (optional) - Captures mouse movements and scrolling to generate visual heatmaps of where users click and look. Useful if you want to see engagement patterns visually in PostHog, but Sliq doesn't use this data. * Enable web vitals autocapture (optional) - Tracks page load performance metrics (Largest Contentful Paint, First Input Delay, etc.) using Google's web vitals library. Helpful for monitoring site speed, but not used by Sliq. * Enable session recordings (optional) - Records video-like replays of user sessions so you can watch exactly how visitors navigate your site. Great for debugging UX issues, but not used by Sliq and can add to your PostHog bill at high traffic volumes. Add your domain. After installation, PostHog will ask you to add Authorized Domains. Add your production website URL (e.g., https://www.yoursite.com). This tells PostHog which domains to track for Web Analytics. Verify analytics in production. After deploying, visit your production website in a browser, then go to Web Analytics in PostHog's left sidebar. You should see your visit show up within a minute or two - pageviews, visitors, and traffic sources will start appearing. If the Web Analytics page shows data, you're ready to connect to Sliq. If it's empty, open your browser's DevTools (Network tab) on your production site and look for requests going to Trysliq.i.posthog.com - if you don't see any, the snippet isn't firing and you may need to re-run the install wizard or check your deployment. Create a Personal API key. Sliq connects to PostHog via a Personal API key. Here's how to create one: * In PostHog, go to Settings > Personal API Keys (or go directly to posthog.com/settings/user-api-keys). Click Create personal API key. * Configure the key: * Label: Give it a name like "Sliq" * Organization & project access: Select Projects and pick the specific project you want to track. You don't need "All access". * Scopes: Click the Performing analytics queries preset - this selects the query:read scope, which is the only permission Sliq needs. * Click Create key. A modal will appear with your key - copy it immediately. The key will never be shown again. * Verify the key was created correctly. Back on the Personal API Keys page, you should see your "Sliq" key listed with Status: Active, Scopes: query:read, and the correct project access. Find your project ID. In PostHog, go to Settings > General. Your Project ID is displayed under the "Project token & ID" section. Copy it - you'll paste it into Sliq in the next step. Connect to Sliq. * From chat: If Sliq is walking you through setup, click the "Connect PostHog to Sliq" link in the chat - a modal will pop up where you can paste your API key and project ID. * From Integrations: Go to the Integrations page in Sliq, find PostHog, click Connect, and enter your API key and project ID. What you get. Once connected, Sliq can: * Daily traffic overview - unique visitors, total pageviews, and daily trends * Traffic sources - where your visitors are coming from (Google, Twitter, direct, etc.) * Top pages - which pages get the most views * Top clicks - which buttons and links visitors click most, with click rates * Engagement metrics - average session duration and bounce rate * Custom queries - ask your AI assistant anything about your traffic and it will query PostHog directly You can also set up a daily analytics digest as an automated task in Sliq to get a summary delivered to Slack every morning. Need help? Last updated: April 2026

PostHog is now available in Vercel's v0. Feb 12, 2026 PostHog, Inc. has released a brand new PostHog x Vercel integration. That integration made the setup intentionally boring. Flags and experiments are defined in PostHog, synced into Vercel's native Flags system, credentials are handled automatically, and your app simply consumes them via the Flags SDK. That integration focuses on how flags and experiments run in production. Now, PostHog, Inc. is extending the same product context into Vercel's v0 via MCP, so it's available while you're building. What you can do with PostHog in v0. v0 can build directly on real codebases, enabling teams to ship production-ready apps and agents rather than prototypes or toy projects. With PostHog as a partner, v0 doesn't just generate code against your repository - it can do so with an understanding of how your product actually behaves in production. It has access to experiment results, user behavior patterns, feature flag state, and error impact, which means the software you generate reflects real product data rather than assumptions about how the product should work. There's so much you can do with PostHog in v0 that PostHog, Inc. asked its Growth team what types of prompts they'd start with. Act based on experiment results. When iterating on onboarding or activation flows, existing experiments often already contain the answer to "which direction should we take." With PostHog connected, v0 can reference experiment data and use it as context when generating UI, so new designs reflect what has already been tested in production. Suggested prompt: Based on the onboarding experiment testing long vs short signup, show me the metrics and then generate a signup flow aligned with the winning variant. Insights from its Growth team: You can use this when redesigning signup or pricing pages. For example, if a shorter signup form improved completion rate but reduced activation, that tells you friction wasn't the only issue. You might keep the shorter form but strengthen the first-run experience, add clearer value framing, or tailor the flow by acquisition channel instead of treating all users the same. Turn ideas into multivariate flags. Product changes are rarely binary. v0 can help turn a concrete idea into a properly structured multivariate feature flag that matches PostHog's experiment model, making it ready to roll out and measure without additional setup. Suggested prompt: Create a multivariate feature flag in PostHog for its new 'Quick Actions' button. Variant A is yellow, variant B is green. Insights from its Growth team: Use this for things like CTA color, button placement, or dashboard layout variations. Instead of debating whether a brighter button "feels more clickable," you can ship both versions behind a multivariate flag and measure downstream impact. Not just clicks, but whether users actually complete the next meaningful action. Ask product questions and act on the answers. Understanding what users do often comes down to a few focused questions. v0 can run trends, funnels, and HogQL queries in PostHog and surface the results directly, so decisions about flows or copy are informed by actual usage rather than assumptions. Suggested prompt: Show me a funnel from page view to signup, broken down by referral source. Insights from its Growth team: You can use this when evaluating campaign traffic. If paid users convert differently from organic users, you could generate different landing page variants tailored to intent. It's also helpful before rewriting homepage copy - if most drop-offs happen before pricing is even viewed, the issue likely isn't the pricing table. Find drop-offs and close the loop. Identifying where users drop off is only useful if it leads to follow-up. With PostHog in v0, you can move directly from analysis to targeted feedback, without stitching together multiple tools. Suggested prompt: Find my worst-performing funnel, then create a survey targeting users who dropped off asking what went wrong. Insights from its Growth team: You can use this for onboarding steps with unexplained friction. If users consistently abandon at, for example, "Connect your first integration," you can automatically trigger a short survey for that segment and ask whether it's confusion, missing documentation, or lack of perceived value. That feedback can immediately shape the next iteration. Create and summarize surveys. Qualitative feedback is most useful when patterns are clear. v0 can help create surveys and then group responses by theme once data starts coming in, making it easier to move from raw feedback to concrete next steps. Suggested prompt: Create an open-text survey asking users: 'What's the one thing you'd change about its product?' and target it to users who've been active for at least 30 days. Insights from its Growth team: Try to use this after launches or major UI changes. Instead of manually reading hundreds of open-text responses, look for recurring themes like "navigation confusion" or "missing export options." That will make it easier to prioritize fixes based on frequency and impact rather than on the loudest individual comment. Once survey responses come in, use a follow-up prompt to pull survey stats and identify the most common themes. Debug anticipating user impact. Errors are easier to prioritize when they're tied to user impact. v0 can query error data from PostHog so investigation starts with which issues affect the most users, not just which ones appeared most recently. Suggested prompt: Show me the top errors affecting the most users this week, and any new errors that appeared after yesterday's deploy. Insights from its Growth team: This should be useful after releases. If a new deploy introduced an error affecting, let's say, 15% of active users in a critical flow, that takes priority over a rare edge-case bug. It will also help you connect technical issues to real user journeys - for example, whether the error blocks upgrade, onboarding, or core usage. Keep LLM costs visible. When building AI features, cost trends matter as much as functionality. v0 can surface model-level spend from PostHog so usage decisions are based on actual data rather than surprises at the end of the month. Suggested prompt: Which LLM model is costing me the most this week, and how is the spend trending? Insights from its Growth team: This comes handy when testing different models for AI-powered features. If GPT-4-level performance doesn't meaningfully improve user outcomes compared to a cheaper model, that's an immediate optimization opportunity. Product data is most useful when it shows up at the moment decisions are made. With PostHog available in v0, you can build agents, features, and experiments with real context in mind instead of assumptions. Check it out: v0.app PostHog is an all-in-one developer platform for building successful products. PostHog, Inc. provide product analytics, web analytics, session replay, error tracking, feature flags, experiments, surveys, LLM analytics, data warehouse, CDP, and an AI product assistant to help debug your code, ship features faster, and keep all your usage and customer data in one stack.