LLM-Generated SQL: Cloud Compute Costs

• Cloud compute costs for LLM-generated SQL queries are determined by API pricing and query execution metrics, defining a complex cost model for cloud services.
• Innovative pipeline designs like multi-agent frameworks and agentic NL2SQL dramatically reduce token usage and operational expenses.
• Empirical benchmarks show that optimized routing, shared executions, and precise cost modeling can yield significant cost savings and improved efficiency.

Cloud compute costs arising from LLM-generated SQL queries represent a complex intersection of natural language understanding, database analytics, and cloud pricing structures. The deployment of text-to-SQL systems powered by LLMs introduces multifactorial expenses, including LLM API costs, data warehouse consumption charges, resource provisioning for query execution, and pipeline-level efficiency factors. Recent empirical studies reveal that strategic pipeline design, adaptive query execution, principled model routing, and agentic optimization can significantly alter the operational cost profile without sacrificing accuracy.

1. Fundamental Cost Components in LLM-Driven SQL Generation

Cloud cost attribution for LLM-generated SQL consists of two broad categories:

• LLM API costs: Principally determined by the number of model calls per query, total input/output token volume, and per-token/model pricing (e.g., $0.03/1K input tokens and$0.06/1K output for OpenAI GPT-4 Turbo, $0.02/$0.03 for Anthropic).
• Query execution costs on cloud DBMS: Billed by data scanned (bytes processed), compute slot-utilization time, I/O volume, memory footprint, and, in serverless contexts, invocation/request overhead and storage access (Deochake et al., 26 Dec 2025, Talaei et al., 2024).

Cost models generally assume:

$$C_{\rm query} = C_{\rm token}\;\frac{T}{1000} + C_{\rm call}\;N$$

with $T$ the total tokens and $N$ the LLM calls. For data warehouse execution, primary metrics are bytes processed ($BP$), slot utilization ($SS$), and per-TB rates (e.g., \$6.25/TB in BigQuery) (Deochake et al., 26 Dec 2025, Marroquín et al., 2018).

2. Impact of Pipeline Designs: Multi-Agent, Agentic, and Routing Approaches

Pipeline architecture fundamentally shapes per-query costs:

• Multi-agent frameworks (e.g., CHESS) decompose the mapping from NLQ to SQL into specialized agents for IR, schema pruning, candidate generation, and validation. CHESS reduces context tokens 2.5× and LLM API calls by a factor of 5, cutting costs by roughly \$0.034 per query (from \$0.0605 to \$0.0265) (Talaei et al., 2024). See:

| Method | Tokens/query | Calls/query | Cost/query (\$) | Accuracy (EX %) | |-------------|-------------:|------------:|----------------:|----------------:| | Baseline | 2,000 | 1 | 0.0605 | 46.3 | | CHESS | 800 | 5 | 0.0265 | 64.6 |

• Agentic NL2SQL (Datalake Agent) interactively requests only necessary metadata, reducing token consumption by up to 87% and cost per query by 87% (\$0.519 to \$0.064 for 319-table schemas) (Jehle et al., 16 Oct 2025).
• Routing-based schemes use lightweight classifiers (score-based or BERT-based) to assign queries to the cheapest LLMs that can generate correct SQL. These routers reduce spend by 10–40% at a median execution accuracy penalty of 0.9–6 points compared to always calling the most capable LLM (Malekpour et al., 2024).

3. Empirical Measurement of SQL Execution Costs in Cloud Data Warehouses

Recent benchmark-driven analyses quantify consumption-billed costs of SQL generated by LLMs on warehouse platforms:

• Google BigQuery: Reasoning-enabled LLMs process 44.5% fewer bytes (mean BP 2,140MB vs. 3,857MB), yield a 44.5% reduction in dollar cost (\$0.0134 vs. \$0.0241/query), and reduce cost outlier frequency (Deochake et al., 26 Dec 2025). Cost variance between models is up to 3.4× per query.
• SQL anti-patterns identified: SELECT *, unintended CROSS JOINs, missing partition filters, excessive CTE nestings significantly raise bytes processed. For example, missing partition predicates trigger full-table scans on 113GB tables, causing single-query costs to spike (\$0.23 for worst outlier).
• Shared query execution: Batched queries by rewriting a session’s queries into one SQL statement yield dramatic cost reductions—100× in Athena, 16–128× in BigQuery (Marroquín et al., 2018).

4. Cost Modeling of Learned Resource Consumption and Query Optimization

Model-driven pipelines forecast cloud execution cost with high accuracy:

• GNN-based cost models predict per-query runtime, I/O, memory, and network usage. Training with LLM-generated synthetic SQL allows reduction in the number of required training queries by 45% and yields 10% better routing cost (Nidd et al., 27 Aug 2025). Cloud unit costs are mapped as:

$$\begin{aligned} CPU_{\rm cost}(q) &= \left(\frac{T_{CPU}(q)}{3600}\right)\,p_{CPU} \ MEM_{\rm cost}(q) &= \left(\frac{M_{PEAK}(q)}{2^{30}}\right)\left(\frac{T_{CPU}(q)}{3600}\right)p_{MEM} \ IO_{\rm cost}(q) &= \left(\frac{V_{IO}(q)}{2^{30}}\right)\,p_{IO} \ NET_{\rm cost}(q) &= \left(\frac{V_{NET}(q)}{2^{30}}\right)\,p_{NET} \end{aligned}$$

For example, a typical query may cost as little as \$0.0050 per execution.

• Prestroid pipeline (tree-CNN over logical plan subtrees) reduces Azure GPU training cost per batch up to 13.2× and memory footprint by 13.5×. This enables daily retraining and rapid adaptation to dynamic LLM-generated SQL workloads (Kang et al., 2021).

5. Tactical Strategies for Cost Minimization: Practical Guidelines

Key deployment recommendations, universally supported in recent literature (Deochake et al., 26 Dec 2025, Li et al., 2024, Dönder et al., 20 May 2025, Parab et al., 11 Jun 2025):

• Model selection: Prefer reasoning-capable LLMs—higher correctness at substantially lower cloud warehouse cost. Avoid throughput-optimized LLMs whose cost variance and outliers increase operational risk.
• Pre-execution cost guardrails: Employ static or learned cost estimators (using dry-run or proxy models) to block or throttle queries forecasted to exceed budget constraints.
• SQL anti-pattern detection: Deploy automatic analysis tools to intercept costly query structures (SELECT *, missing joins or partitions).
• Pipeline-level optimizations:
  • Shared execution middleware for session-level LLM query batching.
  • Dynamic hint integration (HI-SQL) amortizes one-time hint cost over $N$, reducing verification retries and multi-agent overhead—75% lower LLM costs vs. pipelines with multi-stage agentic flows (Parab et al., 11 Jun 2025).
  • Zero-shot prompting and schema compaction (SEA-SQL) cuts per-query cost to 0.9–5.3% of GPT-4–few-shot alternatives at nearly identical accuracy (Li et al., 2024).
  • “N-rep” schema diversity achieves ~69% execution accuracy at \$0.039/query—an order of magnitude cheaper than chain-of-thought pipelines at similar performance (Dönder et al., 20 May 2025).
  • Key–value cache maximization and column/row reordering yields 32% savings on API costs and up to 3.4× job speedups (Liu et al., 2024).

6. Resource Provisioning: Serverless Query Engines and Utilization Patterns

• Serverless execution architectures (e.g., Starling on AWS Lambda) decouple cost from provisioned cluster size, yielding flat per-query costs (\$0.0256/query for 1TB TPC-H) at low-to-moderate arrival rates. These outperform fixed-provision clusters when query spacing exceeds 1 minute (Perron et al., 2019).
• Cost formula for serverless pipelines:

$$C_{\rm total}(\lambda) = \lambda\,C_{\rm per\,query} + C_{\rm idle}$$

Cloud function cost composition includes Lambda invocations, compute seconds, shuffle (S3) operations, and storage calls.

7. Limitations, Caveats, and Best-Practices for Cost Attribution

Published cost formulas and empirical studies routinely caution that:

• Model, token, and call pricing are provider-dependent and change over time.
• Schema caching in “real-world” deployments may reduce baseline context costs.
• Auxiliary infrastructure costs (embedding DBs, custom hint engines, vector search) often fall outside API and execution billing.
• Network latency and high query-per-second load can inflate per-call surcharges.

Best practice is to parameterize cost formulas by actual measured token and call volumes, provider rates, and expected retry/verification overhead, thereby tailoring cost projections to the deployed environment (Talaei et al., 2024, Parab et al., 11 Jun 2025). For large-scale operations, amortizing fixed costs (training bias eliminators, hint curation) over expected query volumes remains essential to avoid misleading budget calculations.

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References:

• CHESS: Contextual Harnessing for Efficient SQL Synthesis (Talaei et al., 2024)
• Bootstrapping Learned Cost Models with Synthetic SQL Queries (Nidd et al., 27 Aug 2025)
• Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload (Kang et al., 2021)
• Cost-Aware Text-to-SQL: An Empirical Study of Cloud Compute Costs for LLM-Generated Queries (Deochake et al., 26 Dec 2025)
• Towards Optimizing SQL Generation via LLM Routing (Malekpour et al., 2024)
• HI-SQL: Optimizing Text-to-SQL Systems through Dynamic Hint Integration (Parab et al., 11 Jun 2025)
• Agentic NL2SQL to Reduce Computational Costs (Jehle et al., 16 Oct 2025)
• Pay One, Get Hundreds for Free: Reducing Cloud Costs through Shared Query Execution (Marroquín et al., 2018)
• Optimizing LLM Queries in Relational Data Analytics Workloads (Liu et al., 2024)
• Starling: A Scalable Query Engine on Cloud Function Services (Perron et al., 2019)
• Cheaper, Better, Faster, Stronger: Robust Text-to-SQL without Chain-of-Thought or Fine-Tuning (Dönder et al., 20 May 2025)
• SEA-SQL: Semantic-Enhanced Text-to-SQL with Adaptive Refinement (Li et al., 2024)