vs BigQuery
TrailStack vs BigQuery cost comparison for event analytics.
Honest cost math for teams watching their BigQuery bill climb.
TL;DR
At 50M events per month with auto-refreshing dashboards, TrailStack costs ~$41. BigQuery costs ~$852 with date partitioning, column pruning, and 50% cache, ~21x more. For ad-hoc queries only, BigQuery is cheaper ($7.50 vs $21).
Check first
TrailStack won't replace BigQuery if you depend on any of these:
- Ad-hoc-only queries — BigQuery's free tier covers a few queries per day for less money
- BigQuery ML (BQML) — or native integration with other GCP services
- Existing pipeline — if you already have working ingestion and schema management, the ops cost argument does not apply
The scenario
| Parameter | Value |
|---|---|
| Events per month | 50,000,000 |
| Average event size | 1 KB |
| Data volume per month | 50 GB |
| Retention | 12 months |
Technical founder on GCP. Pub/Sub and Cloud Functions feed events into BigQuery. A 6-widget Looker Studio dashboard refreshes every 5 minutes. The query bill has exploded.
What 50M events per month costs
At 12 months of retention, the dataset reaches 600 GB at steady state.
| Line item | BigQuery | TrailStack |
|---|---|---|
| Storage (600 GB) | ~$7.50/mo | $0.025/GB = $15 |
| Ingestion (50 GB/mo) | $0.025/GB (2 TB free) = $0 | Free |
| Queries (ad-hoc only) | $6.25/TB (1 TB free) = ~$0 | 0.25 CU (free) |
| GCP egress† (50 GB/month) | — | $6 |
| Total (ad-hoc) | ~$7.50 | $21 |
BigQuery charges $0.02 per GB active, dropping to $0.01 per GB after 90 days. The weighted average across 600 GB is about $7.50 per month.
At ad-hoc-only usage, BigQuery is about 3x cheaper ($7.50 vs $21). The tradeoff: you build the ingestion pipeline, manage schemas, and accept GCP ops overhead. TrailStack: POST JSON, get SQL, zero ops.
What dashboards cost
6 widgets, auto-refreshing every 5 minutes during 10 business hours per day. Each query scans 30 days (50 GB). A typical widget reads 3-5 of ~15 columns — about 25% of the data:
- Queries per day: 6 widgets x 12 refreshes per hour x 10 hours = 720 queries per day
- Data scanned per query: 50 GB x 25% column pruning = 12.5 GB
- Data scanned per day: 720 x 12.5 GB = 9 TB per day
- Data scanned per month: 9 TB x 30 days = ~270 TB per month
| BigQuery on-demand | BigQuery BI Engine | TrailStack | |
|---|---|---|---|
| Storage | $7.50 | $7.50 | $15 |
| Queries | 135 TB × $6.25 = $838 | — | — |
| BI Engine (12.5 GB) | — | 12.5 GB × $25 = $312 | — |
| Compute | — | — | 1 CU = $20 |
| GCP egress† | — | — | $6 |
| Total | ~$846 | ~$320 | $41 |
All three options model a single dashboard workflow. 1 CU is one workflow on TrailStack — add $20/CU for each additional one.
On-demand with column pruning and 50% cache: ~21x more than TrailStack. BI Engine: ~8x more, best for stable datasets.
BigQuery Editions with capacity pricing can reduce costs at scale.
What happens to old data
Both platforms keep data indefinitely. BigQuery drops to $0.01 per GB after 90 days untouched. TrailStack charges a flat $0.025 per GB per month.
At 50 GB per month, storage grows linearly:
| Retention | TrailStack (1 CU) | BigQuery BI Engine | BigQuery (on-demand) |
|---|---|---|---|
| 1 month (50 GB) | $27.25 | ~$313 | ~$845 |
| 12 months (600 GB) | $41 | ~$320 | ~$852 |
| 36 months (1.8 TB) | $71 | ~$332 | ~$864 |
On-demand: TrailStack is ~21x cheaper. BI Engine: ~8x cheaper. BI Engine cost is dominated by the fixed reservation ($312/month) — retention barely moves it.
What you get for the money
BigQuery at ~$7.50 per month (ad-hoc) gives you a data warehouse with SQL and free ingestion within 2 TB. You build and maintain the pipeline:
- Configure Pub/Sub topics and Cloud Functions for ingestion
- Define and evolve table schemas as your events change
- Set up and maintain date partitioning and clustering
- Manage GCS lifecycle policies for cost control
- Monitor delivery failures and debug broken pipelines
- Tune query patterns to avoid scan cost spikes
BigQuery at ~$852 per month (with dashboards) gives you the same warehouse, but auto-refreshing dashboards push query costs far higher. BI Engine can reduce this for stable workloads.
TrailStack at ~$41 per month (1 CU) gives you JSON ingestion with automatic schema detection, real-time ClickHouse SQL, and wire protocol access. No dashboards. Bring Grafana, DBeaver, or AI tools. Compute is flat.
With BigQuery, you maintain the pipeline. With TrailStack, the endpoint is the pipeline.
The pricing model
BigQuery's per-scan model excels at ad-hoc exploration but breaks when dashboards, alerts, and scheduled reports run continuously.
Caveats & Sources
Both platforms charge per GB, so event size affects both equally. BigQuery is cheaper on storage ($0.01-0.02 vs $0.025 per GB). The difference is the query pricing model: per-scan vs flat compute.
†GCP internet egress is $0.12 per GB (Premium Tier). If your app runs outside GCP, this cost does not apply.
- cloud.google.com/bigquery/pricing — $0.02/GB active, $0.01/GB long-term, $6.25/TB query
- Airbyte BigQuery pricing guide — Storage Write API $0.025/GB, 2 TB free
- cloud.google.com/vpc/network-pricing — internet egress $0.12/GB Premium Tier
Verified against published pricing pages as of April 2026.