PostHog vs Lightdash
Side-by-side trajectory, velocity, and editorial themes.
PostHog is wiring itself into the MCP ecosystem while shoring up mobile-SDK feature parity.
PostHog continues its weekly grind, but the May releases cluster around two themes: an MCP toolchain (alerts to Slack and webhooks, SDK Doctor, mode selection via header) and LLM analytics BYOK providers (Together AI, Azure OpenAI). At the same time the mobile teams are filling in iOS and Android session-replay controls, rage-click detection, and survey delays that previously only the web SDK had.
The shape of PostHog's surface keeps widening rather than deepening: more LLM-vendor coverage in the analytics product, more MCP-tooling so AI agents can read and act on PostHog data, more parity across SDKs. Less obvious is which surface becomes the headliner; right now Conversations, Logs, Experiments, and Client Libraries are all shipping into a single weekly digest with comparable weight.
Expect MCP integration to keep expanding from peripheral utilities into the core insights and alerting paths, with PostHog positioning itself as the analytics endpoint AI agents read from when reasoning about product usage. Mobile SDK parity work should compress in the next month or two as the gap with the web SDK closes.
Lightdash chips away at the SQL barrier with NL-to-formula table calcs and metric-tree visualization.
The release cadence is high and the work spans three areas: lowering the technical barrier (spreadsheet-style formulas in table calculations, plain references to grand totals), enriching what a chart and dashboard can express (color palettes at every scope, row/column limits, rich-text table cells), and self-serve operability (default user spaces, expiring preview projects, dashboard-version rollbacks that include chart configs). The Canvas now hosts persistent metric trees, hinting at a heavier semantic-layer story.
Lightdash is positioning between a dbt-native semantic layer (where SQL-fluent analysts live) and a self-serve BI tool (where business users live). The intent-driven formula editor and reference-total functions chip away at the SQL prerequisite for table calculations, while Saved Trees push the metric model into something visually editable. Underneath, the platform is doing the unglamorous self-serve work — personal spaces, palette hierarchies, preview hygiene — that BI products need to survive in larger orgs.
Expect the formula editor to grow into broader AI-assisted authoring (filters, joins, custom dimensions) and Saved Trees to evolve into a more general semantic-layer view that consumes from dbt and produces governance artifacts. Color and palette work suggests embedded/customer-facing BI ambitions next.
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