# Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction

arXiv: https://arxiv.org/abs/2201.00561 · HTML: https://mlsanity.com/paper/2201.00561/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 38 references that are in the map.
- First posted on arXiv 3 Jan 2022.

## Builds on (most cited first, 16 of 16)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017)
- [Learning Scheduling Algorithms for Data Processing Clusters](https://mlsanity.com/paper/1810.01963/) (2018)
- [Neo: A Learned Query Optimizer](https://mlsanity.com/paper/1904.03711/) (2019)
- [Learned Cardinalities: Estimating Correlated Joins with Deep Learning](https://mlsanity.com/paper/1809.00677/) (2018)
- [Bao: Learning to Steer Query Optimizers](https://mlsanity.com/paper/2004.03814/) (2020)
- [An End-to-End Learning-based Cost Estimator](https://mlsanity.com/paper/1906.02560/) (2019)
- [Learning to Optimize Join Queries With Deep Reinforcement Learning](https://mlsanity.com/paper/1808.03196/) (2018)
- [Deep Reinforcement Learning for Join Order Enumeration](https://mlsanity.com/paper/1803.00055/) (2018)
- [Plan-Structured Deep Neural Network Models for Query Performance Prediction](https://mlsanity.com/paper/1902.00132/) (2019)
- [NeuroCard: One Cardinality Estimator for All Tables](https://mlsanity.com/paper/2006.08109/) (2020)
- [DeepDB: Learn from Data, not from Queries!](https://mlsanity.com/paper/1909.00607/) (2019)
- [Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings](https://mlsanity.com/paper/2002.12393/) (2020)
- [Opportunistic View Materialization with Deep Reinforcement Learning](https://mlsanity.com/paper/1903.01363/) (2019)
- [Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload](https://mlsanity.com/paper/2103.12465/) (2021)

## Built on by (most cited first, 16 of 16)

- [TpuGraphs: A Performance Prediction Dataset on Large Tensor Computational Graphs](https://mlsanity.com/paper/2308.13490/) (2023)
- [Data Agent: A Holistic Architecture for Orchestrating Data+AI Ecosystems](https://mlsanity.com/paper/2507.01599/) (2025)
- [PRICE: A Pretrained Model for Cross-Database Cardinality Estimation](https://mlsanity.com/paper/2406.01027/) (2024)
- [ML-Powered Index Tuning: An Overview of Recent Progress and Open Challenges](https://mlsanity.com/paper/2308.13641/) (2023)
- [A Learned Cost Model-based Cross-engine Optimizer for SQL Workloads](https://mlsanity.com/paper/2506.02802/) (2025)
- [Optimizing Context-Enhanced Relational Joins](https://mlsanity.com/paper/2312.01476/) (2023)
- [GenJoin: Conditional Generative Plan-to-Plan Query Optimizer that Learns from Subplan Hints](https://mlsanity.com/paper/2411.04525/) (2024)
- [DistJoin: A Decoupled Join Cardinality Estimator based on Adaptive Neural Predicate Modulation](https://mlsanity.com/paper/2503.08994/) (2025)
- [BitE : Accelerating Learned Query Optimization in a Mixed-Workload Environment](https://mlsanity.com/paper/2306.00845/) (2023)
- [COSTREAM: Learned Cost Models for Operator Placement in Edge-Cloud Environments](https://mlsanity.com/paper/2403.08444/) (2024)
- [CONCERTO: Complex Query Execution Mechanism-Aware Learned Cost Estimation](https://mlsanity.com/paper/2412.00749/) (2024)
- [Reqo: A Comprehensive Learning-Based Cost Model for Robust and Explainable Query Optimization](https://mlsanity.com/paper/2501.17414/) (2025)
- [Low Rank Learning for Offline Query Optimization](https://mlsanity.com/paper/2504.06399/) (2025)
- [HSG-12M: A Large-Scale Benchmark of Spatial Multigraphs from the Energy Spectra of Non-Hermitian Crystals](https://mlsanity.com/paper/2506.08618/) (2025)
- [Bootstrapping Learned Cost Models with Synthetic SQL Queries](https://mlsanity.com/paper/2508.19807/) (2025)
- [MICRO: A Lightweight Middleware for Optimizing Cross-store Cross-model Graph-Relation Joins [Technical Report]](https://mlsanity.com/paper/2603.13835/) (2026)

## Abstract (arXiv)

In this paper, we introduce zero-shot cost models which enable learned cost estimation that generalizes to unseen databases. In contrast to state-of-the-art workload-driven approaches which require to execute a large set of training queries on every new database, zero-shot cost models thus allow to instantiate a learned cost model out-of-the-box without expensive training data collection. To enable such zero-shot cost models, we suggest a new learning paradigm based on pre-trained cost models. As core contributions to support the transfer of such a pre-trained cost model to unseen databases, we introduce a new model architecture and representation technique for encoding query workloads as input to those models. As we will show in our evaluation, zero-shot cost estimation can provide more accurate cost estimates than state-of-the-art models for a wide range of (real-world) databases without requiring any query executions on unseen databases. Furthermore, we show that zero-shot cost models can be used in a few-shot mode that further improves their quality by retraining them just with a small number of additional training queries on the unseen database.