What are the main practical limitations of homomorphic encryption, and how do practitioners work around them?

HE is extremely computationally expensive (orders of magnitude slower than plaintext) and largely non-interactive/inflexible — so teams often prototype on synthetic data first and reserve HE for narrow, high-value computations.

Limitations:

• Performance: FHE can be thousands-to-millions of times slower than the same computation in plaintext; ciphertexts are large.
• Non-interactive / rigid: the computation is typically fixed in advance; you can't easily branch on intermediate (encrypted) values.
• Complexity: correct, secure schemes are hard to implement.

The "synthetic-data-first" workaround: develop and validate your analytics pipeline on synthetic data (which carries no real personal information and runs at full speed), and only switch to HE for the final, sensitive production computation. This minimizes how much expensive encrypted computation you actually run.