Hey everyone, I've been lurking for a bit while trying to get my company's threat intel pipeline off the ground. I'm pretty new to building data pipelines for this kind of thing, so I wanted to share a recent change we made because it feels like a win, but I also have some questions.
We were using Recorded Future exclusively for about a year. The data was great and the API was easy to pull from, but the cost was getting really hard to justify for our scale. My manager asked me to look into alternatives, and honestly, I was pretty overwhelmed at first. There are so many feeds and tools out there.
What we ended up doing was switching to a hybrid model. We kept a very targeted, minimal commercial feed (for our most critical, high-confidence IOC types) and then built out the rest using open source feeds. We set up a simple Airflow DAG to pull from places like Abuse.ch and some curated GitHub repos, do some basic de-duplication and formatting, and then load everything into BigQuery for our analysts to query. The pipeline itself isn't too complex yet—mostly Python scripts and some SQL transformations.
The crazy part? Our costs are literally half of what they were before. It's been running for three months now and the analysts say they're not missing much for our use cases. But here's where I get unsure—this feels almost too good to be true?
I'm worried about maintenance and data quality as we scale. Like, how do you all handle schema changes in the open source feeds? Or vetting the reliability of a new feed before adding it? I'd love to hear if anyone else has gone down this path and what pitfalls you hit. 😅
I'm a sales ops manager at a 120-person SaaS company, and we built a similar hybrid intel pipeline last year to feed our CRM and security scoring, using a mix of commercial APIs and scraped OSINT feeds.
**Total Cost of Ownership:** Recorded Future started around $85k/year for us, while our hybrid model (one focused commercial feed, cloud functions, BigQuery) runs about $32-40k annually. The hidden cost is 15-20 engineering hours per month for maintenance and feed vetting.
**Data Quality & Coverage:** RF's unified scoring and consistent format saved us probably 10 hours a week in analyst time. Our hybrid setup matches on core IOCs (IPs, domains) but lags on emerging threats and requires manual tuning for false positives from the open-source feeds.
**Implementation & Maintenance:** RF's API integration took about two weeks. Building the initial hybrid pipeline with Airflow and scripts took six weeks, and we spend roughly half a day each week adjusting parsers or adding new sources.
**Operational Fit:** For a team with 1-2 dedicated analysts who can triage data, hybrid works. If your team is under 5 security personnel total and lacks scripting bandwidth, a full commercial feed like RF is likely worth the premium for the saved time.
I'd recommend sticking with your hybrid model since you've already built it and have analysts to use it. To be sure, tell us your team's size and how many hours per week they currently spend cleaning the open-source feed data before it's usable.
That's a fantastic outcome, especially for getting started. Halving costs while building internal capability is a huge win.
I'm curious about your pipeline's stability over time, though. Open source feeds can change formats or go offline without warning. How have you handled that so far? We've had a few incidents where a critical feed moved, and our DAG broke silently for a day or two.
Also, with the commercial feed being so targeted now, are you finding gaps in coverage for your less-critical but still-important IOC types?
— Jane
Congratulations on getting that initial pipeline built and seeing those cost savings, that's a huge step. Your description of feeling overwhelmed at first really resonated with me; the sheer number of options can be paralyzing.
> The pipeline itself isn't too complex yet
I think that's actually its greatest strength right now. Starting simple gives you a solid foundation you can understand completely before you need to scale. The complexity will come, inevitably, from managing the stability of those open source feeds as user1267 pointed out. Have you started thinking about a monitoring layer for your DAG? A simple alert on failure is one thing, but detecting when a feed's format drifts or its volume drops anomalously is a whole other challenge that can save you from those silent breaks.
My one piece of unsolicited advice: document the "why" behind each open source feed you integrate now. When you're six months in and a feed starts acting up, you'll want to remember if you added it for a specific malware family or geographic coverage to decide if it's worth the engineering time to fix or just replace.
The right tool saves a thousand meetings.