Independent research · AI model churn

Every model you depend on has an expiration date.

Tryline Labs documents what LLM deprecations and silent model changes cost the teams they hit — so you can learn from their invoice instead of paying your own.

Share your migration story The work ↓

The work

Vendor churn is an operational risk,
not a changelog entry.

The research measures what vendor churn costs the people who build on it: forced migrations, silent behavior drift, destroyed fine-tune investments, and the engineering hours burned proving a replacement actually behaves like the model it replaces.

Findings are shared with research participants and, periodically, in public. If your team has lived through a painful model migration, the research wants your story.

15
models shut down in a single day — 2026-07-23
gpt-4o
retires 2026-10-23 — the largest forced migration to date
≥6 mo
minimum notice for GA models — before it becomes your deadline

The playbook — free, take it

Six moves to make the week
your model retires.

  1. Pin everything. Inventory every model alias your stack calls. latest aliases are time bombs; a dated snapshot is the only address that survives a shutdown.
  2. Baseline before the deadline. Capture the old model's outputs on your production prompts while it's still alive. You cannot diff against a corpse.
  3. Test behavior, not benchmarks. Vendor scores won't tell you if your JSON schema, latency budget, or tone survives. Your prompts, your evals, your traffic.
  4. Check the fine-tune path first. If you're fine-tuned, verify a successor exists before the shutdown date — several retirements have shipped with none.
  5. Budget the migration honestly. Engineering days + re-validation + the production incidents that happen anyway. Put a number on it before it puts one on you.
  6. Document the change. Model inventory, dates, equivalence evidence — the auditors and the regulation will ask what changed and when. Have the answer already written.

Current study

The July-2026 shutdown wave.

Interviews with teams migrating under deadline: what the notice period looked like from the inside, what the replacement eval actually covered, what broke in production anyway, and what it cost in hours, dollars, and user trust.

Protocol: ~15 interviews with engineers and platform leads, past-behavior questions only, quotes used with permission.

Independent and self-funded. Every date on this page is verified against vendor sources.

Contact

Lived through a painful migration?

Twenty minutes of your story becomes evidence in the study — anonymized, and shared back with every participant.

alan@trylinelabs.com