Astrato

Key Cost Drivers in Databricks 🔧

1. Data volume (especially for ETL)
2. Concurrency and session duration (number of users × usage time)
3. Warehouse type and size (Standard vs Pro vs Serverless)
4. Workload type:
 1. Jobs Compute: for scheduled pipelines (ETL)
 2. SQL Compute: for BI dashboards (like Astrato)
 3. All-purpose compute: notebooks and ad hoc exploration
5. Auto-scaling and auto-termination settings

1. ETL / Data Engineering (Jobs Compute)
 
 1. Small Cluster (1 node) - ~50GB/day 
 Estimated DBU/hr: ~2–3 | Cost: $1–3/hr
 2. Medium Cluster (2–4 nodes) - ~500GB/day 
 Estimated DBU/hr: ~6–10 | Cost: $5–10/hr
 3. Large Cluster (&gt;4 nodes) - ~1TB/day
 Estimated DBU/hr: ~12+ | Cost: $10–20/hr
 
 💡Tip: Most ETL jobs are batch-based, running 1–3 hours per day.
 
2. BI App Usage (SQL Compute) with Astrato – Optimized Costs 📊
 
 1. Light Usage 
 5 users, 1M rows (~2GB/query)
 Usage: 2–3 hrs/day
 Cluster: 1–2 node SQL warehouse
 Est. Monthly Cost: $80–200/mo 
 2. Moderate Usage
 20 users, 10M rows (~10–20GB)
 Usage: 8 hrs/day
 Cluster: 2–4 node SQL warehouse
 Est. Monthly Cost: $400–900/mo
 3. Heavy Usage
 50 users, 50–100M rows (~100GB)
 Usage: 8–10 hrs/day
 Cluster: 4–8 node SQL warehouse
 Est. Monthly Cost: $1,200–3,000/mo
 
 - Assumes Serverless SQL, result caching, auto-pause, and lean warehouse config. ✅
 - Astrato’s zero-copy architecture means compute is aligned with actual usage — no background refresh costs.

Metrics That Matter for BI Costing 🎯

Beyond data volume and concurrent users, key cost drivers include:

- Query frequency and complexity (joins, filters, aggregation depth) 🔃
- Caching utilization (Databricks SQL cache can reduce costs ~30–60%) 💾
- Data freshness requirements (real-time = more compute than hourly/daily updates)⌚ 
- User interaction level (passive consumption vs heavy exploration) 🧠
- Auto-pause / resume thresholds (shorter = lower cost) 💤
- Concurrent query load (peak concurrency = warehouse size) 🧩

Example Scenarios excluding caching 📚

1. Example 1: Light Embedded BI in SaaS App ✅
 1. 10 external users log in 1–2×/day
 2. Each runs 3–5 simple queries on ~5–10M row dataset
 3. SQL Compute: 2-node Serverless warehouse with auto-pause
 4. Monthly BI Cost: ~$100–200
 5. ETL Pipelines: Run nightly on 50GB → ~$300/month
 - → Ideal for MVP or early-stage analytics
2. Example 2: Enterprise Internal BI Platform ✅
 1. 50 internal users, ~20 concurrently active during peak
 2. Datasets of 100M rows, moderate joins
 3. SQL Compute: 4–6 node Serverless with caching, aggressive auto-pause
 4. Monthly BI Cost: $500–1,200
 5. ETL: Runs hourly on ~1TB → $1,500–2,500/month
 - → Modern enterprise BI with high interactivity and governance

1. Key Cost Drivers in Databricks 🔧
 Databricks pricing primarily depends on:
 
 1. Data volume (especially for ETL)
 2. Concurrency and session duration (number of users × usage time)
 3. Warehouse type and size (Standard vs Pro vs Serverless)
 4. Workload type:
 1. Jobs Compute: for scheduled pipelines (ETL)
 2. SQL Compute: for BI dashboards (like Astrato)
 3. All-purpose compute: notebooks and ad hoc exploration
 5. Auto-scaling and auto-termination settings
 
2. Rules of Thumb 📏
 1. ETL / Data Engineering (Jobs Compute)
 
 1. Small Cluster (1 node) - ~50GB/day 
 Estimated DBU/hr: ~2–3 | Cost: $1–3/hr
 2. Medium Cluster (2–4 nodes) - ~500GB/day 
 Estimated DBU/hr: ~6–10 | Cost: $5–10/hr
 3. Large Cluster (&gt;4 nodes) - ~1TB/day
 Estimated DBU/hr: ~12+ | Cost: $10–20/hr
 
 💡Tip: Most ETL jobs are batch-based, running 1–3 hours per day.
 
 2. BI App Usage (SQL Compute) with Astrato – Optimized Costs 📊
 
 1. Light Usage 
 5 users, 1M rows (~2GB/query)
 Usage: 2–3 hrs/day
 Cluster: 1–2 node SQL warehouse
 Est. Monthly Cost: $80–200/mo 
 2. Moderate Usage
 20 users, 10M rows (~10–20GB)
 Usage: 8 hrs/day
 Cluster: 2–4 node SQL warehouse
 Est. Monthly Cost: $400–900/mo
 3. Heavy Usage
 50 users, 50–100M rows (~100GB)
 Usage: 8–10 hrs/day
 Cluster: 4–8 node SQL warehouse
 Est. Monthly Cost: $1,200–3,000/mo
 
 - Assumes Serverless SQL, result caching, auto-pause, and lean warehouse config. ✅
 - Astrato’s zero-copy architecture means compute is aligned with actual usage — no background refresh costs.
 
3. Metrics That Matter for BI Costing 🎯
 Beyond data volume and concurrent users, key cost drivers include:
 
 - Query frequency and complexity (joins, filters, aggregation depth) 🔃
 - Caching utilization (Databricks SQL cache can reduce costs ~30–60%) 💾
 - Data freshness requirements (real-time = more compute than hourly/daily updates)⌚ 
 - User interaction level (passive consumption vs heavy exploration) 🧠
 - Auto-pause / resume thresholds (shorter = lower cost) 💤
 - Concurrent query load (peak concurrency = warehouse size) 🧩
 
4. Example Scenarios excluding caching 📚
 1. Example 1: Light Embedded BI in SaaS App ✅
 1. 10 external users log in 1–2×/day
 2. Each runs 3–5 simple queries on ~5–10M row dataset
 3. SQL Compute: 2-node Serverless warehouse with auto-pause
 4. Monthly BI Cost: ~$100–200
 5. ETL Pipelines: Run nightly on 50GB → ~$300/month
 - → Ideal for MVP or early-stage analytics
 2. Example 2: Enterprise Internal BI Platform ✅
 1. 50 internal users, ~20 concurrently active during peak
 2. Datasets of 100M rows, moderate joins
 3. SQL Compute: 4–6 node Serverless with caching, aggressive auto-pause
 4. Monthly BI Cost: $500–1,200
 5. ETL: Runs hourly on ~1TB → $1,500–2,500/month
 - → Modern enterprise BI with high interactivity and governance

Databricks SQL Serverless is ideal for Astrato’s live-query, low-latency use cases

BI Cost ≈ direct function of dashboard usage &amp; business value - no usage = no cost, usage = cost = business value

Leverage dashboard telemetry in Astrato to right-size warehouse sizes

Split workloads: Custom Reports may need separate tuning from dashboards

- Databricks SQL Serverless is ideal for Astrato’s live-query, low-latency use cases
- BI Cost ≈ direct function of dashboard usage &amp; business value - no usage = no cost, usage = cost = business value
- Leverage dashboard telemetry in Astrato to right-size warehouse sizes
- Split workloads: Custom Reports may need separate tuning from dashboards

<a href="https://docs.google.com/spreadsheets/d/1JnpHIe_JU1KSC5UlUsfHoRIeWbx_UfSk/edit?gid=564547242#gid=564547242" rel="nofollow noopener noreferrer" target="_blank">Databricks - Astrato Cost Calculator</a>

- <a href="https://docs.google.com/spreadsheets/d/1JnpHIe_JU1KSC5UlUsfHoRIeWbx_UfSk/edit?gid=564547242#gid=564547242" rel="nofollow noopener noreferrer" target="_blank">Databricks - Astrato Cost Calculator</a>

Break down Databricks pricing, calculate total costs, and use our Excel cost calculator to optimize your data platform spending.

Databricks Pricing Breakdown: Understanding Your Cloud Analytics Spend

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Databricks Pricing Breakdown: Understanding Your Cloud Analytics Spend

Key Cost Drivers in Databricks 🔧

Rules of Thumb 📏

Metrics That Matter for BI Costing 🎯

Example Scenarios excluding caching 📚

Additional Advice ➕

Cost Calculator