How Framedash compares to general-purpose game analytics

Framedash and general-purpose game analytics products overlap, but they are tuned for different jobs. This page describes what Framedash is built to do well, so you can decide where it fits alongside or instead of a broader analytics stack. It names no specific product and makes no superiority claim; the goal is to help you match a tool to a need.

What Framedash is optimized for

Framedash concentrates on performance telemetry tied to place, build, and automation.

• Spatial performance telemetry. Samples carry a world-space position and a map_id, so FPS, frame time, GPU time, and memory can be drawn as heatmaps over your level. That answers where a build is slow, not only whether it is slow. See performance heatmaps.
• CI regression gating. framedash perf-diff and run-profile-test compare a candidate build against a baseline and exit non-zero when it regresses beyond a threshold, so a performance regression can fail a pull request. See CI-integrated profiling.
• Agent and MCP access. An MCP server, a CLI, and a published OpenAPI schema let AI coding agents and scripts query telemetry directly. See the MCP server.
• Direct SQL over ClickHouse. With a Full key, the query tool and POST /v1/query run read-only SQL against the raw events table, rather than only pre-built reports.

Framedash also provides dashboard KPIs, retention cohorts, and funnel analysis, so it is not perf-only. Those tools exist to support the performance and spatial work above, not to replace a full behavioral-analytics suite.

What general-purpose analytics and LiveOps products focus on

Broad game-analytics and LiveOps platforms are typically built around player behavior and the live economy: deep monetization and in-app-purchase reporting, A/B testing, remote config and feature flags, live-event scheduling, player segmentation, and push or in-app messaging. These are large surfaces that Framedash does not aim to cover.

Choosing between them

If your immediate need is to see where frame rate drops on a map, to block a build that regresses in CI, or to let an AI agent query telemetry over MCP and SQL, Framedash is built for that path. If you need monetization analytics, experimentation, or live-operations tooling as the primary job, a general-purpose product is likely the better fit, and it can run alongside Framedash rather than instead of it.

Next steps

• Introduction: what Framedash collects and shows
• CI-integrated profiling: the regression gate
• MCP server: agent and SQL access