In mid-May 2026, with the Gemini 2.0 Flash deprecation visibly on approach in June, I started walking through every indie job I have on the API. Year-old production jobs and brand-new experiments were mixed together, and waiting for the cutover before touching them was guaranteed to break something. These are the notes from that May pass, in case you are running Gemini at a similar small scale.
I am Masaki Hirokawa, an artist and indie developer running wallpaper apps since 2014 (over 50 million downloads across the portfolio). My Gemini API usage is mostly image-metadata generation and App Store review summarization, and the story below is the May 2026 review of those batches.
Stop treating the deprecation as a June event
The first thing I changed was the framing. The deprecation has a calendar date, sure, but the thing that actually hurts indie jobs is the quiet behavior drift around the cutover, not the date itself.
From earlier model transitions I have seen Gemini change in shape on:
- Japanese politeness register
- JSON output
nullhandling (field omission vs explicitnull) - Punctuation distribution in longer summaries
- Subtle differences in tool-argument formatting
None of these show up as API errors, so a calendar-only mindset means your production jobs degrade silently after the cutover. Well before the deprecation date I started running a small batch that sends the same inputs to both model versions and diffs the output.
My four checkpoints
These are the four checkpoints I have been running through across all jobs in May.
1. Is there a provider abstraction?
If some calls go direct to the Gemini API and others go through a thin in-house wrapper, the migration grows non-linearly. I wrote about 30 lines of abstraction and routed every call through it.
from dataclasses import dataclass
from typing import Optional
from google import genai
@dataclass
class GeminiRequest:
prompt: str
model: Optional[str] = None # None falls through to the provider default
json_schema: Optional[dict] = None
class GeminiProvider:
DEFAULT_MODEL = "gemini-2.5-flash" # default already flipped in May
FALLBACK_MODEL = "gemini-2.0-flash" # kept until the deprecation lands
def __init__(self, api_key: str):
self._client = genai.Client(api_key=api_key)
def call(self, req: GeminiRequest) -> str:
model = req.model or self.DEFAULT_MODEL
cfg = {"response_mime_type": "application/json"} if req.json_schema else {}
try:
res = self._client.models.generate_content(
model=model, contents=req.prompt, config=cfg
)
return res.text
except Exception:
if model != self.FALLBACK_MODEL:
return self.call(GeminiRequest(req.prompt, self.FALLBACK_MODEL, req.json_schema))
raiseThe intent is to flip the default to 2.5 Flash but keep 2.0 Flash as a fallback through the deprecation window. The point is to accumulate real evidence that the default runs 100% of traffic before the cutover lands. I also count fallback invocations per day so a regression there is visible immediately.
2. JSON schema behavior diff
The biggest practical difference I see between 2.0 Flash and 2.5 Flash is in JSON output: 2.0 used to emit "items": [] for empty arrays, while 2.5 omits the field more often. My wallpaper-app review summary schema assumed "keywords": [] was always present, so I updated both the schema and the parser to tolerate either shape before the deprecation date.
3. Tag model name in every log line
Every call log carries the model name now. This is for post-deprecation triage so we can trace which job was on which model. Two of my batches were not structured-logging, and the May review added a model_id field to all of them.
4. Cost and latency measurements
In my own measurements, Gemini 2.5 Flash adds roughly 5-15% to inference time per 1,000 input tokens compared with 2.0 Flash. For latency-sensitive jobs (an in-app chatbot in a wallpaper app, for instance) that cost is real, so I am keeping such jobs on 2.5 Flash rather than reaching for 2.5 Pro, which would make the indie economics ugly.