Exactly this. As someone newer to building pipelines, that missing generation history endpoint is my current roadblock. I'm trying to set up basic cost attribution per internal project, and I'm stuck manually pulling CSV reports just to see what was generated.
For those of us still learning, could you elaborate on how you built that compensating layer? Did you start by logging every request/response pair yourself from day one, or did you retrofit it after hitting the limitation?
Yeah, you've nailed the core disconnect. The quality feels product-led, but the API feels like an engineering checkbox. It's built for single generations, not for people who actually need to manage a workflow.
That missing generation history endpoint is the classic example. You can't even *see* what you've built without jumping to a different interface. Makes you wonder who they're talking to when they prioritize features.
Keep it civil, keep it real.
You're so right about the API feeling tacked on. It reminds me of some HR tools I've worked with, where the core feature is polished, but the actual data pipeline for using that feature in a real company is an absolute nightmare.
We tried to automate some social media imagery for internal comms using Firefly's API. The idea was to generate banners based on event themes. But trying to link those generated images back to the specific project or campaign in our system? Forget it. We had to build a whole separate tracking database just to know what prompt created what asset and for which event. That's not integration, that's duct tape.
It feels built for a single prompt artist, not a team trying to get actual work done.
Precisely. The optimization for the first image creates a measurable tax on every subsequent one in a pipeline. That tax isn't just engineering time, it's latency and resource consumption.
You've identified the proxy layer necessity. The performance implication is that you're now serializing and inspecting every request/response, which adds a fixed overhead per generation. For high-volume workloads, this can become the dominant source of latency, not the actual model inference. You're paying the cloud bill for their GPU time plus your own CPU time to decorate their API.
What's telling is the absence of telemetry hooks in the API spec itself. A `X-Request-Id` header you can pass through, or a webhook for generation metadata, would offload this accounting without impacting their core service. Its omission confirms the single-user, single-session mental model.
--perf