pyobfus vs PyLocket¶
PyLocket encrypts individual functions' bytecode and bundles a licensing and commerce platform around it. It answers a different question from pyobfus: how to sell a desktop application, rather than how to protect a codebase you already ship.
PyLocket is a newer, developer-first platform (launched after this document's other comparisons were written) that positions itself against SOURCEdefender as "a full platform" rather than an encryption utility.
| Feature | pyobfus | PyLocket |
|---|---|---|
| Commercial model | $45 (one-time Pro) | Subscription plus activated-licence pricing; verify current vendor terms |
| Python versions | 3.9 - 3.14 | 3.12 - 3.14 only |
| Protection unit | AST transformation on whole modules | Per-function bytecode encryption |
| Key management | Local Pro license / Runtime String Vault | Device-bound keys issued at activation, never embedded in the shipped app |
| Anti-debug / anti-VM | Anti-debugging (Pro) | Anti-debug + anti-VM + dynamic API resolution + memory protection |
| Open source | Yes (Core: Apache 2.0) | No |
| Built-in commerce (licensing, delivery, checkout, trials) | No — bring your own | Yes, this is the product's headline differentiator |
| Debugging / traceback workflow | Reverse stack-trace mapping (--unmap), purpose-built for AI-assisted debugging |
Not mentioned anywhere in PyLocket's own docs or marketing |
| AI-assisted development workflow | Explicit design goal | Not mentioned |
Where PyLocket is genuinely stronger¶
Per-function bytecode encryption with device-bound keys issued only at activation — and never embedded in the shipped binary — is a materially stronger tamper-resistance claim than pyobfus's AST transformation plus the Pro Runtime String Vault. There is no static file-plus-key combination an attacker can extract from a single copy of the app; each licensed device gets its own key at runtime. If your threat model is "prevent a static, offline analysis of a shipped desktop binary," that architecture is a real advantage.
Where PyLocket doesn't compete with pyobfus at all¶
PyLocket's documentation, comparison pages, and marketing copy contain no
mention of debugging support, traceback handling, or AI-assisted development
workflows. That is not an oversight this comparison is reading into — it
reflects a different design goal: PyLocket optimizes for opaque,
tamper-resistant delivery of a finished desktop application, the same
tradeoff family as PyArmor and Nuitka above. Once code ships through
per-function runtime decryption, there is no natural mapping back to
readable stack traces for whoever has to debug a customer's crash report.
pyobfus's --save-mapping / --unmap workflow exists specifically to keep
that path open — see the Layered Deployment Strategy
below for how the two tradeoffs coexist rather than compete head-on.
When to Choose pyobfus¶
- You need Python 3.9-3.11 support (PyLocket requires 3.12+)
- You want one-time pricing instead of a subscription + per-seat licensing
- You need to debug production issues without giving up protection
- You're protecting a library or backend service, not selling a licensed desktop app
When to Choose PyLocket¶
- You're shipping a commercial desktop application and want licensing, device binding, and checkout built in rather than assembled yourself
- Your threat model is static offline analysis of a distributed binary, and maximum per-function tamper resistance matters more than post-deployment debuggability
- You only need to support Python 3.12+
Part of the pyobfus tool comparison, which also carries the feature matrix, pricing, and the reasoning behind layering more than one tool. Its dated scope and disclaimer apply here as well.