PhD program: Berkeley or UCLA?

I applied for PhD programs in the computational/data science + health space and I'm really happy that I have gotten offers from two great schools: UC Berkeley+UCSF's Computational Precision health PhD, and UCLA's Medical Informatics program! I got some admits from some other schools too, but these two schools are my top 2 so at this stage, I'm going to have to make the decision between one or the other, but I'm really unsure and confused right now.

With Berkeley, I got an offer letter where I'm basically admitted to a professors lab, while in UCLA, the offer I got is rotation based so there's a bit more flexibility there. I had a chat with the faculty in both programs, as well as their current PhD students and I'm seeing pros and con's of each school.

UC BERKELEY/UCSF:

Pros:

  1. Great schools for health and data science/engineering, and great career prospects being based in the Bay area
  2. Professor said that she will give me very focused attention and give me access to classes from Berkeley, UCSF and Stanford, whatever help I need. 
  3. Most of the students in her lab have told me in private conversations that she is a great mentor, has lots of data, and she has good technical knowledge.
  4. Prof is a doctor as well so my work will be really clinically useful
  5. Funding is in a good state, even in the current climate, as per the prof.

CONS (or potential cons maybe?)

  1. she only has 1 PhD student now. Most alumni of the lab have gone on to become doctors or done PhDs in other schools. Is having one student only a red flag?
  2. Not sure if doing internships are encouraged in the program. The prof said that its okay for me to do an internship but she said she's not seen many students do an internship in the program.
  3. Everyone runs computational experiments on cloud computing and there's no in-house server - I'm worried this may make my experiments more constrained to save money but not sure, I guess its also nice because I can run small or large experiments.
  4. the program format isn't a conventional PhD format. The dissertation defense is apparently optional, and also, the qualifier exams have only an oral component, no written qualifier. Is this good/bad?
  5. Since I'm admitted to this prof's lab, does that mean I cannot work with anyone else if I don't enjoy working with the current lab?

UCLA:

Pros:

  1. Just got back from the campus visit and the campus is amazing!
  2. Faculty are very nice, super supportive, and have done great work. My boss in my current job personally knows the faculty, vouches for them and says they have graduated many students.
  3. Faculty do encourage doing industry internships during the program.
  4. PhD format is more conventional unlike the Berkeley program.
  5. Funding is in a decent state, even in the current climate.

Cons:

  1. Won't get proximity to a big health tech network at UCLA compared to Berkeley
  2. Data access is harder at UCLA as per the profs, but it can be managed.
  3. I feel the environment at Berkeley is more cut-throat and intense since they are at very big institutions (for health and STEM atleast) while it is a bit more laid back at UCLA. I feel this intensity will help me, even though it'll be harder.
  4. Program is primarily based in a medical school so I feel my opportunities to gain computational experience will be a bit limited.

Any thoughts on the above, especially folks who have studied at UCLA and/or Berkeley? What are the most important things to think about when finally choosing a PhD program?

Thanks!