* CLP/CBC: open source makes deployment and devops easy, which is great. Linear models are nice in that you "know what you're getting." Performance is at times a pain point.
* Gurobi: super fast, but the licensing was just impossible. Partly that was due to high cost, but ultimately we could have borne the cost; the inability to do something like have autoscaling containers using Gurobi was ultimately the dealbreaker for us. As Zoba grew, we had to turn to alternatives.
* NLOpt: absolutely a blessing, but the variety of algorithms and tuning parameters is really opaque, even for a team with some OR experience/background.
* OR-tools: powerful but the documentation is remarkably terrible, and the vehicle routing problem solver doesn't natively support all the constraints we'd like, so we have to do some hacks.
Overall my feeling for all these tools is roughly gratitude: solvers are complex, and rolling our own would be absolutely impractical at our size. But also there's some pain in between "I've formulated my problem as a nice mathematical model" and "I can reliably get optimal results on time."