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Query Cost Forecasting: Predictive Models to Avoid Budget Overruns

qqueries

2026-02-01

10 min read

Build predictive models that forecast query spend across ClickHouse, Snowflake, and cloud storage to avoid budget overruns.

Stop surprise bills: forecast query spend across ClickHouse, Snowflake, and cloud storage

Hook: If you manage analytics infrastructure you’ve likely faced an unexpected spike in query spend that blew a monthly budget. Slow queries, untracked ad-hoc analysis, and cross-system pricing make forecasting costs hard. This guide shows how to build predictive models that forecast query spend across ClickHouse, Snowflake, and cloud storage using historical metrics — so engineering and finance teams can set a total budget (think Google’s 2026 total campaign budgets) and avoid overruns.

Why query cost forecasting matters in 2026

Cloud analytics costs keep rising as teams centralize data in lakes and warehouses and run more complex ML and BI workloads. Recent developments amplified the problem: ClickHouse’s large 2025 funding round and continued growth as a high-throughput OLAP engine, plus the wider adoption of serverless and per-scan billing patterns, mean teams run more variable workloads against diverse engines. Meanwhile, in January 2026 Google rolled out total campaign budgets for Search, demonstrating a shift toward period-level budget controls rather than daily tweaking — a useful analogy for analytics budgeting.

"Set a total campaign budget over days or weeks, letting Google optimize spend automatically and keep your campaigns on track without constant tweaks." — Google, Jan 2026 rollout

Key stakes: Overspend, underutilized budgets, blocked feature launches, and finger-pointing between data consumers and platform teams. Forecasting removes guesswork and enables automated pacing, approvals, and anomaly alerts.

What to forecast (and why)

Forecasting should be actionable — predict metrics you can convert into dollars and then use to enforce budgets.

Total daily/weekly/monthly query spend (primary target). Aggregated across engines and cloud storage.
Cost-per-query distribution by query fingerprint or user group — enables pre-execution cost gating.
Engine-level spend (ClickHouse vs Snowflake vs cloud object storage scanning/egress).
Anomaly scores and predicted tail events — probability of exceeding a budget.

Data you need: telemetry and billing fingerprints

Accurate forecasts require high-fidelity historical data. Instrument at two layers: technical telemetry and billing mapping.

Essential telemetry

Timestamp, cluster/warehouse identifier, execution node
Query fingerprint (normalized text), user/team, service
Query duration, CPU time, memory, concurrency slot usage
Rows scanned, bytes scanned (storage read), rows returned
Cache hits, materialized view hits
Failure rates, retries

Billing inputs

Snowflake: credits consumed per second per warehouse size (credits = seconds * vCPU-equivalent)
ClickHouse: managed pricing (if using ClickHouse Cloud) or mapped vCPU/VM-hour cost + storage I/O
Cloud storage: per-GB scanned, PUT/GET, egress, and per-month storage tier costs
Pricing tiers, reserved discounts, spot/commitments

Map telemetry to dollars at the query level. For Snowflake, use credits * credit price. For ClickHouse, compute vCPU-hours used * vCPU price plus network/storage IO costs. Aggregate to daily spend by engine and team.

Modeling approaches: from simple to advanced

There’s no single best model — choose based on data volume, explainability needs, and required update frequency.

1) Baselines — moving averages and seasonality

Start here for quick wins. Compute rolling 7/14/30-day averages and apply simple seasonality adjustments (weekday vs weekend, end-of-month ETL peaks). Baselines are fast, explainable, and often accurate enough for short horizons.

2) Statistical time-series models

ARIMA / SARIMA — captures autocorrelation and seasonal patterns.
Exponential smoothing (Holt-Winters) — good for level and trend decomposition.
Prophet — robust to changepoints and seasonality, easier to tune for business calendars.

Use these when historical spend is stable and interpretable.

3) Tree-based regressors with lag features

XGBoost or LightGBM with engineered features often outperform classical approaches when you have many covariates: recent query counts, bytes scanned, active users, scheduled jobs, repo deploy events, promotions, and calendar flags. Include lagged variables and rolling aggregates.

4) Deep learning and sequence models

LSTM/GRU — for longer sequence dependencies.
Temporal Fusion Transformer (TFT) — state-of-the-art for multi-horizon forecasting with static and time-varying covariates.
Seq2Seq architectures for multi-step horizons and scenario simulation.

Choose these for complex workloads with non-linear interactions across engines and external signals (campaigns, Black Friday, product launches).

5) Probabilistic and quantile forecasting

Budgets need uncertainty estimates. Use quantile regression, Bayesian structural time series, or ensembles to get prediction intervals. For example produce 50%, 75%, and 95% quantiles and use the 95% quantile to set conservative budgets.

6) Anomaly detection and change-point detection

Layer anomaly detection to catch tail events: isolation forest, One-Class SVM, or statistical change-point detection (e.g., ruptures library) on cost-per-query and aggregate spend. Flag anomalies automatically and trigger slowdown or manual review.

Feature engineering: what improves predictions

Better features beat more complex models. Prioritize these:

Lag features: spend_{t-1}, spend_7d_mean, spend_30d_std
Usage signals: queries/sec, distinct query fingerprints, top-10 heavy queries count
Pricing signals: current credit price, committed discounts, reserved instances active
Operational events: deployment timestamps, schema migrations, data backfill windows
Calendar & promotions: marketing campaigns, release windows, fiscal month-end
Engine health: queue lengths, average CPU, memory pressure — correlated to runtime and therefore cost

Converting predictions to budgets: pacing algorithms

Google’s total campaign budgets show the value of period-level control. For analytics, the goal is to guarantee that aggregate projected spend over the budget window doesn't exceed the cap while maximizing useful compute for teams.

Two practical approaches

Paced allocation (deterministic): split the total budget B across the period using predicted daily demand D_t.
Daily allocation A_t = B * (D_t / sum_{i}(D_i)) where D_t is the model’s expected spend for day t. Enforce A_t as a hard cap per-team or per-cluster. Implement a carry-forward mechanism for unused allocation.
Feedback controller (adaptive): use a proportional-integral (PI) controller that adjusts allocation based on cumulative spend error.
At each interval compute error e_t = (spent_so_far + predicted_remaining) - B. Update per-day cap with proportional term to correct pace and an integral term to smooth systematic bias.

For high-variance environments combine pacing with safety controls: per-query pre-execution cost estimates and soft/ hard enforcement (warnings vs blocking).

Pre-execution cost estimation and gating

To stop runaway queries implement preflight cost estimation:

Use query fingerprints + historical cost-per-fingerprint to estimate expected cost.
Predict bytes scanned using explain-plan stats and statistics on table sizes, then map to storage scan price.
Apply a cost policy: if estimated cost > threshold or pushes team over allocated daily cap, either throttle, queue, or require approval.

Keep this inline with developer workflows to avoid friction: provide quick feedback including predicted dollars, confidence interval, and suggested rewrites or materialized view alternatives.

Anomaly detection and rapid mitigation

Even good forecasts miss black swan events. Implement a layered detection and mitigation system:

Real-time scoring: compute anomaly scores on cost-per-query and aggregate spend. Detect sudden spikes in bytes scanned or concurrency.
Automated mitigation: temporarily throttle non-essential workloads, reduce warehouse size for Snowflake transiently, or pause specific user groups based on risk profile.
Postmortem tooling: capture fingerprints, explain plans, and query text for root cause analysis and long-term fixes (rewrite, caching).

Cross-engine mapping: reconcile ClickHouse, Snowflake, and storage costs

Each engine exposes different telemetry and pricing models. Your forecasting pipeline must normalize these into a common dollar metric.

Snowflake

Credits = warehouse_size_factor * runtime_seconds (credits/sec)
Map credits to dollars using effective credit price after discounts
Account for auto-suspend/resume effects and multi-cluster warehouses

ClickHouse

Managed ClickHouse Cloud: use vendor billing APIs (consumption or node hours)
Self-hosted: map node vCPU-hours, storage IO and network egress to cloud cost
Adjust for differences: ClickHouse being CPU/IO bound, include per-query CPU and disk read metrics

Cloud storage

Map bytes scanned to per-GB scan price (e.g., cloud provider object storage billing)
Include egress and cross-region transfer costs
Factor in lifecycle tiers if long-term scans touch cold storage at different rates

Normalization tip: compute a unified feature: predicted_dollars = f_snowflake + f_clickhouse + f_storage where each f_ maps engine telemetry to dollars using current pricing, then feed predicted_dollars into your forecasting model. This unified metric is the same concept discussed in observability & cost-control playbooks.

Evaluation: how to trust forecasts

Measure with the right metrics and test seasonally and under stress.

MAE and RMSE for point estimates
MAPE for relative error but beware when costs near zero
Prediction interval coverage — ensure your 95% interval contains actual spend ~95% of the time
Cost overrun rate — percent of periods where spend > budget
Alert precision/recall for anomaly signals

Backtest models on multiple historical windows including busy days (product launches, Black Friday) and quieter baselines. Run stress tests by injecting synthetic heavy queries and validate the pacing and mitigation logic.

Operationalizing the pipeline

Build a production pipeline with these components:

Collectors: query logs, cloud billing, metrics (Prometheus), and metadata (deployments, calendars)
ETL/feature store: transform telemetry into features and store snapshots for models
Model training and validation: retrain periodically and on drift triggers
Serving layer: API for predictions, pre-execution estimator, and anomaly scoring
Enforcement and orchestration: pacing service, approval workflows, and throttles integrated with query gateways or SQL editors
Dashboarding and alerting: per-team budgets, spend forecasts, and incident timeline

Real-world example: how a mid-market SaaS saved 32% monthly

Scenario: A mid-market SaaS platform ran mixed workloads: daily ETLs, ad-hoc analytics, and ML training. They had Snowflake for BI, ClickHouse for high-throughput telemetry analysis, and a single cloud object store. After building a combined forecasting pipeline (Prophet + XGBoost ensemble for multi-horizon), they:

Set a monthly total budget and used paced allocation across teams.
Enabled pre-execution gating for queries with predicted cost > $5 and required approval for $50+ queries.
Auto-paused low-priority backfills during budget pressure and rewrote three heavy queries to leverage materialized views.

Result: 32% reduction in monthly spend in the first two months, 45% fewer budget overrun incidents, and improved team satisfaction because developers no longer had to guess costs.

2026 trends and future predictions

More vendor billing transparency: Expect richer per-query cost APIs from Snowflake and ClickHouse Cloud in 2026, making mapping to dollars more precise.
Serverless and per-scan pricing growth: Cloud providers will push more serverless analytics, increasing short-term variability and the value of forecasting.
AI-driven budget management: Reinforcement learning controllers that optimize pacing and cost-performance tradeoffs will enter production by late 2026.
Cross-engine optimization: Intelligent query routing between ClickHouse and Snowflake to minimize cost-per-result while preserving SLAs.

Checklist: build a practical forecasting project in 8 weeks

Week 1: Collect telemetry from Snowflake, ClickHouse, and cloud billing; define unified cost mapping.
Week 2: Build baseline rolling-average forecasts and dashboards for weekly/monthly spend.
Week 3–4: Engineer features and prototype ARIMA/Prophet models; validate on last 6 months.
Week 5: Add XGBoost with lag features and compare with baseline; compute prediction intervals.
Week 6: Implement pre-execution estimator for top 100 query fingerprints and gating rules.
Week 7: Integrate anomaly detection and automated throttling for non-critical workloads.
Week 8: Deploy pacing service, run a 30-day total budget pilot with one team, measure overrun rate.

Actionable takeaways

Start with simple forecasts and a unified cost metric before adding complexity.
Map per-query telemetry to dollars — that alignment is the most leverageable piece.
Use probabilistic forecasts and set budgets on a conservative quantile (e.g., 90–95%).
Combine pacing with pre-execution cost gating and anomaly detection for robust control.
Iterate fast: run short pilots, measure overrun rate, and expand policy coverage.

Final notes: aligning teams and tools

Forecasting isn't just a model — it’s a cross-functional process that requires finance, platform engineering, and data consumers to agree on cost mappings and policies. Use transparent dashboards, documented rules, and clear escalation paths. Treat forecasting models as part of platform SLAs: retrain on drift, version models, and keep human-in-the-loop for extreme events.

Call to action

Ready to stop surprise bills? Start a 30-day pilot: collect two weeks of query telemetry, run the baseline forecast, and set a conservative total budget. If you want a jumpstart, download our forecasting workbook and sample code (prophet + XGBoost pipeline) or contact our team to run a pilot across Snowflake, ClickHouse, and your cloud storage. Protect your next release from budget shock — forecast, pace, and automate.

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