Analytics and monitoring decision
Stalkr
A capable developer can build a useful social-listening workflow (ingest, dedupe, classify, alerts) in a few weeks, but matching Stalkr's polished UI, reliability, and multi-tenant/team features would be costly to reproduce fully; obsei is a close open-source building block.
Visit website↗Built by Marc Lou, who ships 32 products in this index
$29/mo
$348/yr
Read off the official pricing page.
$100one-off120 h to build
$50/mo6 h/mo upkeep
On cash alone, building overtakes the subscription at 3 seats.
No open-source build does this yet
Nothing published replaces this one, so a replacement starts from an empty file. Here is what it would have to cover.
What a replacement has to do
- Poll public sources for keyword matches → dedupe and store mentions → classify mentions by category/urgency with an LLM → surface in a feed and trigger email alerts / saved views
What it still won’t have
- Polished product UX and polish (filters, saved view UX, team workflows)
- Native support for LinkedIn (not listed in Starter plan) and any closed-source platform integrations
- Reliable production-scale ingestion, rate-limit handling, and enterprise SLAs
- Team features and multi-brand workflows present in paid tiers (team members, higher caps)
What remains hard
- Product polish and ongoing maintenance
First-year cost
Keep paying
Paying is—cheaper in year one.
On cash alone, building overtakes the subscription at 3 seats.
Money you would actually spend
Time you would spend
—
What you would spend
What we assumed
The verdict above measures whether you could build it. This one is only about money.
Runnable build prompt
Build a minimal social-listening service in Node.js (Express) + PostgreSQL + Redis + React (Vite) with background workers (BullMQ) deployed to a single VPS or small cloud instance. In scope: connectors to X, Reddit, and YouTube (use official APIs or HTML scraping where APIs lack endpoints) with polling cron jobs; dedupe and store canonical mentions in Postgres; LLM-based classification and urgency scoring via an external API (e.g., OpenAI) with retries and cost controls; a React web UI showing a live feed, saved views, mention detail, search, and simple keyword management; an email alert worker that sends immediate alerts for high-urgency mentions and a daily digest; basic account auth (email/password) and admin controls for keyword limits. Out of scope: enterprise SSO, mobile apps, multi-brand/team billing, LinkedIn ingestion. Require error handling, exponential backoff for API calls, rate-limit handling, unit and integration tests for connectors, classification logic, and alerting, plus a Docker Compose setup and documentation for deployment and monthly maintenance steps.
How we checked
How the score was reached
- Partly verdict base52
- 2 cited sources+1
- Price verified on pricing page+3
- 3/3 assessment runs agreed+4
- Evidence score60
The base comes from the verdict. Everything under it is a check that either happened or did not, and each one is a fact frozen in this record rather than a judgement made at render time - so the same evidence always produces the same number.
How scoring works →Cited sources · 2
Every page the run actually retrieved.
- official pricingStalkr — Social listening for startups
- official productSocial listening for product feedback | Stalkr
Integrity checks
What held up, and what did not.
