Trifle
Time-series product metrics that write into your existing database, with an MCP surface for AI agents
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trifle.ioAbout Trifle
Trifle is a time-series metrics layer that deliberately refuses to be another observability stack. The pitch is that you already run a database, and product metrics — orders, revenue, signups, churn, background-job success rates, feature adoption by plan — do not justify standing up a separate columnar store and a separate query language to answer. Trifle's libraries write counters and hierarchical values straight into the database you already operate, so a single `Trifle::Stats.track` call with a key, a timestamp and a nested values hash records the order count, the revenue figure, the country breakdown and the acquisition channel in one shot. Three surfaces sit on top of that primitive: Trifle App for dashboards, digests and alerting; Trifle CLI for querying from a terminal or from an AI agent; and Trifle Stats, the open-source instrumentation libraries for Ruby, Elixir and Go. The AI-agent angle is the newest and the most interesting one for this catalogue — Trifle exposes metrics to agents through a local SQLite file and an MCP server, so a coding assistant can read your production KPIs without being handed warehouse credentials. The published case study claims a customer tracking 80 million daily product calculations and 900 million events a day on the design, which is a useful sanity check that the write-into-your-own-database approach scales past hobby volume.
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Key Features
Trifle Pros & Cons
✅ Pros
- +No new datastore to operate — metrics live beside your application data
- +The free self-hosted tier is genuinely unlimited on users and databases
- +MCP surface lets coding agents read product KPIs without warehouse credentials
⚠️ Cons
- −Library support is limited to Ruby, Elixir and Go today
- −Cloud seat counts are tight — Starter caps at 3 users for $39/month
- −Not a replacement for infrastructure observability; this is product/business metrics
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