thrifty-ml 0.1.0

Proxy-model library: replace per-row LLM calls with lightweight ML classifiers

thrifty-ml

Replace expensive per-row LLM calls with a lightweight ML classifier trained on your own data. Get the same answers at 100–1000× lower cost.

The problem

You have a DataFrame with 100 000 rows. You want to filter or classify every row using an LLM prompt. Calling the LLM once per row is slow and expensive. At $0.25 / 1M input tokens and ~100 tokens per row, that's $2.50 — for a simple yes/no filter.

How thrifty-ml solves it

thrifty-ml implements the proxy model technique from Google Research (SIGMOD 2026):

1. Sample a small subset of rows (~1 000).
2. Label the sample with the LLM — the only rows that ever touch the API.
3. Embed all rows with an embedding model.
4. Train a fast classifier (logistic regression by default) on the labeled embeddings.
5. Evaluate the classifier on a holdout split. If it matches the LLM within a tolerance τ, use it.
6. Predict the remaining rows with the classifier — no LLM calls, ~0.1 ms per 1 000 rows.

On a 100 000-row dataset with sample_size=1000, you pay for 1 000 LLM calls instead of 100 000. 100× cost reduction, with accuracy that matches the LLM on most tasks.

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Installation

pip install thrifty-ml

# For LightGBM support (non-linear proxy, better on hard tasks):
pip install thrifty-ml[lgbm]

Requires Python ≥ 3.10. LLM and embedding calls go through LiteLLM, so any provider works — Anthropic, OpenAI, Bedrock, Vertex, local Ollama, etc.

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Python API

Binary filter

Keep rows that match a natural-language condition.

import pandas as pd
from thrifty_ml import ml_filter

df = pd.read_parquet("reviews.parquet")

mask = ml_filter(
    df,
    prompt="Is this a positive review?",
    text_column="review_text",
    llm="anthropic/claude-haiku-4-5",
    embedding_model="text-embedding-3-small",
)

positive_reviews = df[mask]

ml_filter returns a boolean numpy array of shape (len(df),).

Multi-class classification

Assign each row to one of a fixed set of labels.

from thrifty_ml import ml_classify

labels = ml_classify(
    df,
    prompt="Classify this support ticket by topic.",
    text_column="body",
    llm="anthropic/claude-haiku-4-5",
    embedding_model="text-embedding-3-small",
    classes=["billing", "technical", "account", "other"],
)

df["topic"] = labels

If the LLM returns a value not in classes, that row is labeled "__unknown__".

All parameters

Both ml_filter and ml_classify accept:

Parameter	Default	Description
df	required	Input DataFrame
prompt	required	Natural-language instruction for the LLM
text_column	required	Column name containing text to evaluate
llm	required	LiteLLM model string (e.g. "anthropic/claude-haiku-4-5")
embedding_model	required	LiteLLM embedding model string, or a custom EmbeddingBackend
classes	—	List of class labels (ml_classify only)
proxy	"lr"	Proxy model type: "lr", "svc", or "lgbm"
sample_size	1000	Number of rows to label with the LLM
fallback_threshold	0.1	τ — if proxy F1 < 1.0 − τ, fall back to full LLM
max_concurrency	8	Max simultaneous LLM API calls
cache_dir	~/.cache/thrifty_ml/	Override the embedding/label cache directory
seed	None	Random seed for reproducible sampling

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Offline mode: fit once, predict forever

For recurring workloads, train the proxy once and reuse it without any LLM calls.

from thrifty_ml import Proxy

# Train — labels a sample with the LLM, fits the proxy
proxy = Proxy(
    prompt="Is this a positive review?",
    llm="anthropic/claude-haiku-4-5",
    embedding_model="text-embedding-3-small",
    model="lgbm",         # "lr" | "svc" | "lgbm"
    sample_size=2000,
)
proxy.fit(train_df, text_column="review_text")
proxy.save("sentiment_proxy.joblib")

# Later — no LLM, no API key needed
from thrifty_ml import Proxy
proxy = Proxy.load("sentiment_proxy.joblib")
labels = proxy.predict(new_df, text_column="review_text")

save writes two files: sentiment_proxy.joblib (the model) and sentiment_proxy.joblib.meta.json (metadata). load reads both to restore the proxy fully.

If you used a custom EmbeddingBackend, pass it to load:

proxy = Proxy.load("sentiment_proxy.joblib", embedding_model=MyBackend())

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Custom embedding backends

Pass any EmbeddingBackend subclass instead of a model string to use your own embeddings.

import numpy as np
from thrifty_ml import EmbeddingBackend, ml_filter

class SentenceTransformerBackend(EmbeddingBackend):
    def __init__(self, model_name: str):
        from sentence_transformers import SentenceTransformer
        self._model = SentenceTransformer(model_name)

    @property
    def model_id(self) -> str:
        return f"st:{self._model.get_sentence_embedding_dimension()}"

    def embed(self, texts: list[str]) -> np.ndarray:
        return self._model.encode(texts, convert_to_numpy=True).astype("float32")

mask = ml_filter(
    df,
    prompt="Is this relevant?",
    text_column="text",
    llm="anthropic/claude-haiku-4-5",
    embedding_model=SentenceTransformerBackend("all-MiniLM-L6-v2"),
)

model_id is used as the diskcache key — keep it stable across runs.

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Proxy model types

Type	Key	Notes
Logistic Regression	"lr"	Default. Fast, interpretable, works well on modern embeddings.
Linear SVM	"svc"	Similar to LR; sometimes better on very high-dimensional embeddings.
LightGBM	"lgbm"	Best for non-linear tasks; requires pip install thrifty-ml[lgbm].

The fallback threshold τ (fallback_threshold=0.1) controls quality vs cost. If the proxy's F1 on the holdout split is below 1.0 - τ, thrifty-ml warns you and falls back to labeling all rows with the LLM. Tighten τ (e.g. 0.05) for higher accuracy requirements; loosen it (e.g. 0.2) to force proxy use even when accuracy is lower.

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Caching

Embeddings and LLM labels are cached automatically at ~/.cache/thrifty_ml/ using diskcache. Re-running the same call with the same inputs costs nothing.

Cache keys include the model ID, prompt, and (for multiclass) the class list, so changing any of these triggers fresh calls.

# Use a project-specific cache directory
mask = ml_filter(df, ..., cache_dir="./my_project_cache")

---

CLI

thrifty-ml ships a CLI for use without writing Python.

Filter rows

thrifty-ml filter reviews.parquet \
  --prompt "Is this a positive review?" \
  --text-col review_text \
  --out positive.parquet \
  --llm anthropic/claude-haiku-4-5 \
  --embedding-model text-embedding-3-small

Writes a parquet file containing only matching rows, with an added _thrifty_mask column.

Classify rows

thrifty-ml classify tickets.csv \
  --prompt "Classify this support ticket by topic." \
  --text-col body \
  --classes "billing,technical,account,other" \
  --out classified.csv \
  --llm anthropic/claude-haiku-4-5 \
  --embedding-model text-embedding-3-small

Appends a label column to the output file.

Embed a column

thrifty-ml embed reviews.parquet \
  --text-col review_text \
  --model text-embedding-3-small \
  --out embeddings.npy

Saves embeddings as a numpy .npy file.

Label a sample

thrifty-ml label reviews.parquet \
  --prompt "Is this a positive review?" \
  --text-col review_text \
  --sample 1000 \
  --out labels.csv \
  --llm anthropic/claude-haiku-4-5

Labels a random sample and saves the results — useful for inspecting LLM outputs before running a full pipeline.

Clear the cache

thrifty-ml cache clear

# Clear a specific cache directory
thrifty-ml cache clear --cache-dir ./my_project_cache

Common CLI flags

All commands accept:

Flag	Default	Description
--llm	anthropic/claude-haiku-4-5	LiteLLM model string
--embedding-model	text-embedding-3-small	LiteLLM embedding model string
--proxy	lr	Proxy type: lr, svc, lgbm
--sample-size	1000	Rows to label with the LLM
--fallback-threshold	0.1	τ quality threshold
--max-concurrency	8	Parallel LLM calls
--cache-dir	—	Override cache directory
--seed	—	Random seed

Input files can be .parquet, .csv, .json, or .jsonl. Output format matches input unless you specify a different extension in --out.

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When does the proxy get used?

thrifty-ml prints a warning and falls back to full LLM labeling if:

• The labeled sample contains only one class (proxy can't learn anything).
• The proxy F1 on the holdout split is below 1.0 - fallback_threshold.

In both cases you still get correct labels — just at full LLM cost for that run.

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Environment variables

Set API keys for your chosen providers:

export ANTHROPIC_API_KEY=sk-ant-...
export OPENAI_API_KEY=sk-...

thrifty-ml passes these through to LiteLLM, which supports all standard provider env vars. See the LiteLLM docs for the full list.

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Appendix: thrifty-ml vs BigQuery AI.IF and AlloyDB

The proxy model technique was published in arXiv 2603.15970 and ships inside two Google products: AI.IF / AI.LABEL in BigQuery, and accelerated semantic functions in AlloyDB. The cost and latency wins are real — Google reports 300–1 000× improvement at 10M-row scale — but the implementation is locked inside Google's data warehouse SQL surface. thrifty-ml ports the same technique to Python and removes every one of those constraints.

No infrastructure dependency

BigQuery and AlloyDB require your data to be in GCP, a billing account, and IAM setup. thrifty-ml works on a local pandas DataFrame, a parquet file, or a CSV — on your laptop, in a notebook, in a CI job, or in any Python environment. No cloud account required.

Any LLM and any embedding provider

Google's products are wired to Vertex AI and Gemini models. thrifty-ml uses LiteLLM as an adapter, so the same API works with Anthropic, OpenAI, AWS Bedrock, Google Vertex, Azure OpenAI, a local Ollama instance, or any other provider. You can also bring your own embeddings via the EmbeddingBackend ABC — pre-computed vectors, a fine-tuned sentence-transformer, a proprietary model — without touching the rest of the pipeline.

Deploy-once offline mode

In BigQuery and AlloyDB, the sample-label-train pipeline reruns on every query. thrifty-ml's Proxy class separates training from inference: fit once, serialize to disk, deploy the classifier wherever you need it. Subsequent predict() calls make zero LLM API calls and run at classifier speed (~0.1 ms / 1 000 rows for logistic regression). This matters for production pipelines where you want a stable, versioned model — not one that retrains on every invocation.

Full observability

Inside a SQL function you cannot inspect intermediate state. thrifty-ml exposes everything:

• EvalResult.proxy_f1 — the holdout F1 score that drove the fallback decision
• EvalResult.use_proxy — whether the proxy was used or the LLM fell back
• EvalResult.holdout_size — how many rows the evaluation was based on
• The trained proxy model itself — a standard sklearn or LightGBM object you can serialize, explain with SHAP, or hand to your model registry

You can also tune fallback_threshold explicitly to trade accuracy for cost in a way that is visible and reproducible, rather than relying on opaque platform defaults.

Integration with the Python ML ecosystem

Because proxy models are sklearn or LightGBM objects, the full Python ML toolchain applies: feature importances, calibration curves, cross-validation, SHAP explanations, MLflow or W&B logging, and standard model registries. None of this is accessible through a SQL function.

Tighter iteration loop

A data scientist tuning a prompt or trying a different embedding model gets immediate feedback in a notebook: run the cell, inspect the sample labels, check the proxy F1, adjust, re-run. The BigQuery equivalent is: upload data to a warehouse, write a SQL query, wait for a query job to complete, inspect a result table — then repeat. thrifty-ml collapses that loop to seconds.

The wins transfer

The 300–1 000× cost reduction reported in the paper comes from the proxy technique itself, not from BigQuery. thrifty-ml uses the identical algorithm — random sampling, embedding-based proxy, holdout F1 evaluation, τ-threshold fallback — so the same efficiency gains apply to any DataFrame workload, without a Google account.