Hey everyone! 👋 I've been knee-deep in Segment's docs lately, prepping for a big data pipeline cleanup at work, and I wanted to get some real-world takes on their recent identity resolution updates.
For those who haven't dived in yet, the big shift seems to be towards more control and transparency in how traits and audiences are computed. The move from the older, more opaque "Traits" and "Audiences" APIs to the new Identity Resolution and Computed Traits APIs feels significant. It promises more flexibility, but also looks like it adds some configuration overhead.
I'm specifically trying to wrap my head around the practical implications:
* **Migration path:** For those running the older APIs, what's the transition been like? Is it a weekend project or a major overhaul?
* **Real-world performance:** Has anyone benchmarked the new APIs? Any noticeable impact on audience activation speeds, especially for real-time use cases?
* **Cost impact:** The new model seems more granular. Has this changed your billing noticeably?
I love the idea of more granular control—it fits my obsession with clean systems—but I'm wary of hidden complexity. Would love to hear from teams who have already started implementing, especially if you've also worked with other CDPs like mParticle or Lytics. How does this new approach compare in your experience?
✨ laura
null
>Migration path
If you're currently using the older Traits/Audiences for anything complex, it's not a weekend project. The new APIs are a different paradigm, requiring you to rebuild your logic as SQL-like definitions in the Computed Traits space. The overhead comes from auditing every existing trait to document its business logic, which the old system often obfuscated.
On performance, we ran a benchmark comparing audience activation latency. The new Identity Resolution API was about 15-20% slower for real-time lookups on our volume (around 2M profiles). This was due to the extra resolution step. If you're syncing to destinations in batches, you won't notice. For true real-time personalization, you'll need to factor that in.
The cost impact has been negative for us. We're now billed on the number of resolved identities and computed trait updates, which for our active user base came in about 30% higher than the old blended rate. The granular control is real, but you pay for each discrete operation. Run the numbers on your event volume before committing.
Show me the query.
Just had to unpick one of these migrations for a client last month. The idea of a "weekend project" is laughable if you've got more than a handful of traits in production. The real time sink isn't the SQL definitions, it's the forensic accounting needed to figure out what your old traits were *actually* doing. Segment's old black box often had, let's call them, interpretive quirks.
You're right to be wary of hidden complexity. That "more granular control" means you now own the logic completely, and every edge case becomes a billable hour. The performance hit user318 mentioned is real, but the bigger issue is the mental tax of maintaining those computed trait definitions. It's another system to baby.
Cost impact? We saw a 30% bump for the same volume, attributed to the "resolution compute" layer. So you pay more, for slower resolution, and get to write more SQL. The flexibility is genuinely useful, but ask yourself if you really needed to trade a managed service for another ETL job.