Analytics and monitoring decision
Feedly
A small team can reproduce a narrow core workflow (ingest, extract, LLM summaries, deliver alerts) within a few weeks, but Feedly’s value relies on proprietary source coverage, a large threat graph, and many trained models that would be costly or slow to replicate.
Visit website↗Open-source builds that already do this
Every project below is open source and already does this job today. Fork one, self-host it, or take the parts you need - the build prompt further down assumes an empty file, and this is the shortcut past that. Licences differ; check the one on each card before you ship. All Feedly alternatives, with the arithmetic →
What a replacement has to do
- Continuously ingest security-relevant sources, extract entities/indicators, build/query a lightweight threat graph, generate cited AI summaries/reports, and deliver alerts/newsletters to Slack/email or export as STIX.
What it still won’t have
- access to Feedly’s 10,000+ curated TI sources
- the described Real-Time Threat Graph of millions of relationships
- the vendor’s 1,000+ trained AI models and task-specific tooling
- built-in SOC 2 compliance and enterprise operational polish
- no-code integrations and enterprise advisor support
What remains hard
- Proprietary data
10,000+ trusted TI sources
- Proprietary data
Real-Time Threat Graph Relationships between 10M+ articles, 979 threat actors, 300K+ CVEs, 12K+ malware families, 800 TTPs, 681M+ IoCs
- Proprietary models
1,000+ AI models continuously extract entities, identify incidents, and tag TTPs
First-year cost
Keep paying
Paying is—cheaper in year one.
On cash alone, building overtakes the subscription at 37 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 threat-intel pipeline in Node.js + Postgres (or Python + Postgres) that: 1) ingests RSS and HTTP article feeds on a schedule and normalizes them into a Postgres articles table; 2) runs an open-source NER/IOC extractor to pull CVEs, IPs, domains, malware names and stores extracted entities and edges in a simple graph table; 3) provides a query endpoint to surface articles linked to a CVE or actor; 4) uses an LLM (configurable provider key) to produce a 200–400 word source-cited briefing per query; 5) posts briefings to Slack and supports exporting selected intelligence as a STIX 2.1 JSON file. Out of scope: building a 10k-source ingestion network, training proprietary models, and enterprise admin UI. Include error handling, retries for fetches, basic unit tests for ingestion and extraction, and README deployment steps for a single VM (estimated infra: small cloud VM + managed Postgres).
How we checked
How the score was reached
- Pay verdict base20
- An open-source build was found+5
- 5 cited sources+3
- 3/3 assessment runs agreed+4
- Hard moats found in the evidence-6
- Evidence score26
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 · 5
Every page the run actually retrieved.
- official productFeedly Threat Intelligence (product)
- official pricingFeedly Threat Intelligence pricing
- official docsFeedly Threat Intelligence new features
- open sourceOpenCTI-Platform/opencti
- open sourcesmicallef/spiderfoot
Integrity checks
What held up, and what did not.
