Instead of creating separate files for two sequences, you can provide them as standard input (stdin).

blastp -query <(echo -e ">seq1\nMKSTGGGGTGATGATAG") -subject <(echo -e ">seq2\nMKSTGGGGTGATGATAG")

Outputs:

BLASTP 2.2.27+


Query= Name
Length=17  
Subject= Name
Length=17

 Score = 29.6 bits (65),  Expect = 8e-09, Method: Compositional matrix adjust.
 Identities = 17/17 (100%), Positives = 17/17 (100%), Gaps = 0/17 (0%)

Query  1   MKSTGGGGTGATGATAG  17
           MKSTGGGGTGATGATAG
Sbjct  1   MKSTGGGGTGATGATAG  17

Lambda      K        H        a         alpha
   0.299    0.124    0.352    0.792     4.96 

Gapped
Lambda      K        H        a         alpha    sigma
   0.267   0.0410    0.140     1.90     42.6     43.6 

Effective search space used: 289

I would say that the high resource cost is associated with starting blast, initializing the databases, then reading the files and processing them. Creating the file itself is probably not the bottleneck, it is most likely the quickest step of them all. Optimizing that probably won't do much good.

But dont't take my word for it, create a small script that creates the file and measure how long it takes.

python -m timeit -n 100 'f= open("test.fa", "wt"); f.write(">1\nAAAAAAAAAAAAAAAA\n"); f.close()'
100 loops, best of 3: 714 usec per loop

so that says less than a millisecond per file creation, not likely to be a factor.

What you should look into is collecting your sequences into one file and running blast that way, rather than invoking blast repeatedly to do pairwise alignments.