Pros:

- Do not have to specify the number of clusters before running the algorithm
- Results are reproducible and not subject to randomness introduced by choice of initial centroids as in K-Means

Cons:

- Requires computation of pairwise linkage matrix, which can be computationally expensive
- Results can differ based on the linkage criteria used
- Can be sensitive to noise in data