batchwatch — Python client

Client for batchwatch.dev: crowdsourced measurement of queue time on LLM batch APIs.

Batch endpoints cost 50% of the synchronous ones, but "completes within 24 hours" is impossible to plan around. batchwatch measures what the queue actually does and answers one question: should I use batch for this job?

Standard library only. No dependencies, and none planned.

Install

Not published to PyPI yet. Until it is:

pip install git+https://github.com/batchwatch/client#subdirectory=python

or copy src/batchwatch/ into your project — it is two files.

Two lines

from batchwatch import Batchwatch

bw = Batchwatch(token="tk_...")        # token optional; falls back to $BATCHWATCH_TOKEN

# 1. before you submit — does this belong in the queue?
if bw.should_batch("gpt-5.6-sol", max_wait="15m"):
    job = client.batches.create(...)
else:
    answer = client.chat.completions.create(...)

# 2. measure it, so the next person gets a better answer
with bw.track("gpt-5.6-sol", input_tokens=9720) as t:
    result = wait_for(job)
    t.done(output_tokens=result.usage.completion_tokens)

Get a key with no email and no card:

curl -X POST https://batchwatch.dev/v1/keys -d '{"label":"my pipeline"}'

It fails open, always

If batchwatch is down, slow, or broken, your job must not notice. That is the first requirement, ahead of collecting any data at all.

• Every submission runs on a daemon thread. track() does no network I/O on your thread.
• Two-second timeout by default (BATCHWATCH_TIMEOUT).
• Every batchwatch error is swallowed and logged at DEBUG on the batchwatch logger. Nothing is printed unless you ask for it.
• should_batch() is the one synchronous call, because you are waiting for the answer. If it cannot answer, you get your own default back — never a guess. The default is False, "run it synchronously": being wrong that way costs money, being wrong the other way blows a deadline.
• An exception raised inside your own with block is recorded as failed and re-raised untouched. We swallow our errors, never yours.

tests/test_fail_open.py proves it against a port nothing listens on and against a socket that accepts but never answers.

It never sends your content

No prompts, no completions, no system prompts, no tool calls, no file names. The request body is built from a fixed allowlist — provider, model, mode, endpoint, request count, token counts, timestamps, status — and everything else is dropped in _rens() on the way out. There is no field to put text in.

tests/test_no_content.py asserts it on the bytes a real HTTP server received, and includes a positive control so the test cannot pass by the client simply sending nothing.

output_tokens defaults to None, never 0

You know your input tokens. You cannot know your output tokens before the model has answered. So the default is absence, not zero.

Zero is not a harmless placeholder here: output costs five to six times as much as input, so a saving computed on zero output is systematically too low — measured at 3.4x too low on a real model — and nothing in the response would tell you. If you know a ceiling, pass max_tokens instead and the answer comes back labelled as a ceiling.

Spooling

When a measurement cannot be delivered, the completed record is appended to a JSONL file and replayed later through POST /v1/calls/complete. Losing measurements exactly when the network is bad means losing them exactly when they are most interesting.

• Default path: $BATCHWATCH_SPOOL, or batchwatch-spool.jsonl in the system temp directory. Set BATCHWATCH_SPOOL="" or pass spool=None to turn it off.
• The spool is replayed automatically, at most once a minute, right after a successful call — that is the moment we know the network is up. Call bw.flush_spool() yourself from a shutdown hook if you want it drained on exit.
• Spooling requires a token. /v1/calls/complete takes your own timestamps, so it is closed to anonymous callers; without a key a spool file could never be sent, and writing one would just leak disk. Without a token, undeliverable measurements are dropped and logged at DEBUG.
• The file is capped at 5 MB. Beyond that, measurements are dropped rather than filling your disk.
• A replayed measurement can arrive twice if the original PATCH reached the server but the response did not. That is deliberate: a duplicate is visible in the dataset, a lost measurement is not.
• Threads are handled. Two processes sharing one spool file may send a record twice — give each process its own BATCHWATCH_SPOOL if that matters.

Configuration

enabled=False turns every network call into a no-op, which is what you want in CI.

API

• should_batch(model, max_wait=None, default=False, **kw) -> bool
• advice(model, max_wait=None, provider="openai", input_tokens=None, output_tokens=None, max_tokens=None, risk="p90") -> dict | None
• wait_now(model, provider="openai", mode="batch") -> dict | None
• track(model, provider="openai", mode="batch", requests=1, input_tokens=None, endpoint=None) — context manager
  • t.done(output_tokens=None, status="completed", ttfb_ms=None)
  • t.failed()
  • t.started(input_tokens=...) when the count is only known after submission
• flush(timeout=5.0) -> bool — wait for outstanding submissions before exit
• flush_spool(timeout=None) -> int — send what is on disk, returns accepted

Tests

python -m pytest -q

25 tests, no network beyond loopback. They start real HTTP servers on ephemeral ports rather than monkeypatching urllib: the thing under test is network behaviour, so the network should be in the test.

Licence

MIT