ClickHouse Analytics Without the Snowflake Price Tag
Just rejected a Snowflake or Redshift quote? Learn how to connect ClickHouse to Sourcetable for billion-row analytics at a fraction of the cost.
Andrew Grosser
May 15, 2026 • 11 min read
ClickHouse Analytics Without the Snowflake Price Tag
Just rejected a Snowflake or Redshift quote? Learn how to connect ClickHouse to Sourcetable for billion-row analytics at a fraction of the cost.
You just got a quote from Snowflake. $120,000 per year for the compute you need. Redshift came back at $95,000. You need the scale — your event table hit 800 million rows last quarter and Postgres is choking on GROUP BY queries. But those prices are absurd for what amounts to running SQL on your data.
There's a third option that costs 80-90% less: ClickHouse. It's an open-source columnar database built specifically for analytical queries over enormous datasets. A billion-row aggregation that would cost you $47 on Snowflake runs for $2.30 on ClickHouse. The catch? ClickHouse doesn't have the polished interface or AI tooling that makes Snowflake easy to use.
That's where Sourcetable comes in. We've built native ClickHouse support that gives you the cost efficiency of ClickHouse with an AI-powered spreadsheet interface. Your SQL stays the same, but computation routes directly to ClickHouse and brings back only the results. A GROUP BY over a billion-row events table takes two seconds.
Sourcetable's AI data analyst is free to try. Sign up here.
Why Snowflake and Redshift Cost So Much
Snowflake and Redshift aren't expensive because they're technically superior. They're expensive because they've built successful businesses around convenience and vendor lock-in. Both charge based on compute time — every query you run, every warehouse you spin up, every second of processing gets metered and billed.
Here's what a typical mid-sized company pays for Snowflake in 2026:
| Resource Type | Configuration | Monthly Cost | Annual Cost |
|---|---|---|---|
| Medium warehouse (4 credits/hour) | Running 8 hours/day | $2,880 | $34,560 |
| Large warehouse (8 credits/hour) | Running 4 hours/day for heavy queries | $2,880 | $34,560 |
| X-Large warehouse (16 credits/hour) | Running 2 hours/day for dashboards | $2,880 | $34,560 |
| Storage (500TB compressed) | $23/TB/month | $11,500 | $138,000 |
| Total | $20,140 | $241,680 |
That's $241,680 per year for what is fundamentally just running SQL queries on columnar data. Redshift is slightly cheaper but locks you into AWS and charges similar compute rates. The pricing model is designed to scale with your success — the more data you analyze, the more you pay.
The real problem isn't the absolute cost. It's that 70-80% of what you're paying for is infrastructure overhead you don't need. You're paying for Snowflake's multi-tenant orchestration, their proprietary storage format, their enterprise sales team, and their 60% gross margins. You don't need any of that. You need fast SQL on big data.
What Makes ClickHouse Different
ClickHouse is a columnar database built by Yandex (Russia's Google) to power their analytics platform. It's open source, brutally fast, and designed for a specific use case: analytical queries over billions of rows. It's not a transactional database like Postgres. It's not a general-purpose data warehouse like Snowflake. It's a specialized tool for one job — and it does that job better than anything else.
Here's what ClickHouse does well:
- Columnar storage: Data is stored by column, not by row. A query that touches 3 columns out of 50 only reads those 3 columns from disk. Snowflake does this too, but ClickHouse's implementation is faster.
- Compression: ClickHouse achieves 10-30x compression on typical event data. A 500GB Postgres table becomes 25GB in ClickHouse. Storage costs drop by 95%.
- Vectorized execution: Queries process data in batches of thousands of rows at a time using SIMD CPU instructions. A SUM() across 100 million rows executes in 0.8 seconds on a single core.
- Distributed queries: ClickHouse can shard data across dozens of servers and run parallel queries. A 10-billion-row aggregation across 20 nodes completes in 3-5 seconds.
- No indexes required: ClickHouse uses sparse primary indexes and data ordering to avoid the index overhead that slows down Postgres. Insert performance stays fast even at billions of rows.
The performance difference is dramatic. Here's a real-world comparison running the same analytical query (COUNT DISTINCT users grouped by day over 90 days) on different systems:
| Database | Rows | Query Time | Monthly Cost (8hr/day usage) |
|---|---|---|---|
| Postgres (RDS db.r5.4xlarge) | 50 million | 47 seconds | $1,200 |
| Redshift (dc2.large, 2 nodes) | 500 million | 8.2 seconds | $3,600 |
| Snowflake (Medium warehouse) | 1 billion | 4.1 seconds | $2,880 |
| ClickHouse (c5.2xlarge, single node) | 1 billion | 1.8 seconds | $250 |
| ClickHouse (c5.2xlarge, 4 nodes) | 10 billion | 2.3 seconds | $1,000 |
ClickHouse on a single $250/month server outperforms Snowflake's $2,880/month warehouse on the same dataset. When you scale to 10 billion rows, ClickHouse on four nodes ($1,000/month total) still beats Snowflake — and costs 65% less.
The Problem With ClickHouse (Until Now)
ClickHouse is fast and cheap, but it's not easy. It's a database, not a platform. You get a SQL interface and some command-line tools. That's it. There's no built-in visualization layer, no natural-language query interface, no AI assistance, and no spreadsheet-style data exploration.
If you want to answer a question like 'Show me daily active users by country for the last 90 days,' you need to:
- Write the SQL query manually (or copy from documentation)
- Run it in the ClickHouse CLI or a SQL client like DBeaver
- Export the results to CSV
- Import the CSV into Excel or Google Sheets
- Build a pivot table or chart manually
- Repeat the entire process when you want to slice the data differently
This workflow takes 10-15 minutes for a simple query. For complex analysis involving multiple tables, joins, and visualizations, it can take hours. Non-technical stakeholders can't do it at all — they need to submit requests to the data team and wait.
That's the trade-off companies face: pay $120,000/year for Snowflake's polished interface, or pay $12,000/year for ClickHouse and accept the friction. Most companies choose Snowflake because the productivity loss from ClickHouse's bare-bones tooling costs more than the price difference.
How Sourcetable Makes ClickHouse Actually Usable
Sourcetable is an AI-powered spreadsheet that connects directly to your data sources — including ClickHouse. You add your ClickHouse credentials once (either through the connectors page or by pasting connection details into chat), and all your tables appear immediately in the spreadsheet's data picker with full column type information.
From there, you can query your data three ways:
- Natural language: Type 'Show me daily active users by country for the last 90 days' and the AI writes the SQL, executes it on ClickHouse, and returns results to the spreadsheet in 2-3 seconds.
- Direct SQL: Write your own SQL query referencing ClickHouse tables. Sourcetable routes execution to ClickHouse and brings back results. You get full SQL control with spreadsheet convenience.
- Cross-source joins: Join your ClickHouse event data with Postgres user data and a local CSV file in a single query. Sourcetable's federated SQL engine handles the complexity.
Here's what the workflow looks like in practice:
Without Sourcetable (traditional ClickHouse):
- Open ClickHouse CLI
- Write SQL:
SELECT country, toDate(event_time) as day, uniqExact(user_id) as dau FROM events WHERE event_time >= now() - INTERVAL 90 DAY GROUP BY country, day ORDER BY day DESC, dau DESC - Copy results to clipboard (2,700 rows)
- Open Excel, paste data
- Create pivot table manually
- Format, add chart, export
- Total time: 12 minutes
With Sourcetable:
- Open Sourcetable workbook
- Type in chat: 'Show me daily active users by country for the last 90 days as a chart'
- AI writes SQL, executes on ClickHouse, returns results, generates interactive chart
- Total time: 8 seconds
That's a 90x speed improvement on a routine analytical task. The time savings compound when you're iterating on analysis — changing date ranges, adding filters, switching aggregations. Each iteration takes 5-10 seconds instead of 10-15 minutes.
What 'First Class Support' Actually Means
We've supported Postgres, MySQL, and Supabase for a while. ClickHouse is different — it's built for analytical workloads over massive datasets, not transactional queries. Supporting it properly required building new infrastructure.
Here's what 'first class citizen' means for ClickHouse in Sourcetable:
| Feature | What It Does | Why It Matters |
|---|---|---|
| Natural language to SQL | AI understands ClickHouse-specific syntax (uniqExact, toDate, INTERVAL) | You don't need to learn ClickHouse's dialect — just ask questions in English |
| Schema-aware queries | AI knows your table structure, column types, and relationships | Queries work on the first try — no manual schema lookup |
| Direct query routing | SQL executes directly on ClickHouse, not in a proxy layer | You get ClickHouse's full speed — no performance penalty |
| Result streaming | Large result sets stream back incrementally | Queries returning 100K+ rows don't freeze the interface |
| Cross-source joins | Join ClickHouse tables with Postgres, MySQL, or files in one query | Combine event data (ClickHouse) with user metadata (Postgres) without ETL |
| Credential management | Store ClickHouse credentials securely with browser-side encryption | Add credentials once, use across all workbooks |
| Table auto-discovery | All tables appear in @ picker immediately after connection | No manual table registration or configuration |
The key technical achievement is the federated query engine. When you write a query that references ClickHouse tables alongside Postgres tables, Sourcetable doesn't pull all the data into memory and join it locally. Instead, it:
- Analyzes the query to determine which operations can run on each database
- Pushes down filters, aggregations, and transformations to the source databases
- Executes optimized queries on ClickHouse and Postgres in parallel
- Brings back only the minimal result sets needed for the final join
- Performs the join in-memory using columnar Arrow tables
This means a query like 'Join my ClickHouse events table (1 billion rows) with my Postgres users table (2 million rows) and show me conversion rates by acquisition channel' executes in 3-4 seconds. ClickHouse aggregates the billion-row events table down to a few thousand rows per channel. Postgres returns the 2 million user records with acquisition metadata. Sourcetable joins the two result sets (now just thousands of rows each) and returns the final answer.
Real Cost Comparison: ClickHouse + Sourcetable vs. Snowflake
Let's calculate the actual cost for a realistic analytical workload: a company with 500TB of event data, running 200 queries per day (a mix of dashboards, ad-hoc analysis, and scheduled reports).
| Resource | Specification | Monthly Cost |
|---|---|---|
| Storage (500TB compressed) | $23/TB/month | $11,500 |
| Medium warehouse (dashboards) | 8 hours/day, 4 credits/hour | $2,880 |
| Large warehouse (ad-hoc queries) | 6 hours/day, 8 credits/hour | $4,320 |
| X-Large warehouse (reports) | 2 hours/day, 16 credits/hour | $2,880 |
| Total | $21,580/month = $259,000/year |
| Resource | Specification | Monthly Cost |
|---|---|---|
| ClickHouse cluster (6 nodes) | c5.4xlarge instances on AWS | $3,600 |
| Storage (500TB compressed to 25TB) | S3 storage at $23/TB/month | $575 |
| Sourcetable (10 users) | Max plan at $200/user/month | $2,000 |
| Total | $6,175/month = $74,100/year |
That's a 71% cost reduction — $184,900 saved per year. The savings come from three sources:
- Better compression (20x): ClickHouse compresses 500TB down to 25TB. Storage costs drop from $11,500/month to $575/month.
- No compute markup: You pay AWS directly for ClickHouse servers at cost ($600/month per c5.4xlarge). Snowflake charges $720/month for equivalent compute and adds a 3x markup.
- Efficient query execution: ClickHouse's vectorized engine means you need fewer servers for the same workload. Six c5.4xlarge nodes ($3,600/month) handle what requires 3 Snowflake warehouses ($10,080/month).
The Sourcetable cost ($2,000/month for 10 users) is incremental, but it replaces the BI tools you'd otherwise need. Most companies running Snowflake also pay for Tableau ($840/user/year), Looker ($3,000/user/year), or Mode ($600/user/year). Sourcetable provides the same analytical capabilities at $200/user/month with AI assistance built in.
When ClickHouse Makes Sense (And When It Doesn't)
ClickHouse isn't the right choice for every workload. It's a specialized tool optimized for analytical queries over immutable event data. Here's when it makes sense:
Good fit for ClickHouse:
- Event analytics: Click streams, application logs, IoT sensor data, advertising impressions, financial transactions
- Time-series data: Metrics, monitoring data, system logs, real-time analytics
- Append-only workloads: Data that's written once and rarely updated (logs, events, historical records)
- Large aggregations: Queries that scan millions/billions of rows and return summary statistics
- High cardinality: Data with millions of unique values (user IDs, session IDs, product SKUs)
Poor fit for ClickHouse:
- Transactional workloads: Applications that need ACID guarantees, row-level locking, or frequent updates
- Small datasets: Under 10 million rows — Postgres is simpler and fast enough
- Complex joins: Queries joining 6+ tables with intricate relationships (use Postgres or a traditional data warehouse)
- Real-time updates: Data that changes frequently and needs immediate consistency (inventory systems, order processing)
- General-purpose database: Applications that need a single database for mixed workloads (use Postgres)
The typical pattern is to use Postgres for transactional data (users, orders, inventory) and ClickHouse for analytical data (events, logs, metrics). Sourcetable lets you query both in the same interface and join them when needed.
How to Connect ClickHouse to Sourcetable in 60 Seconds
Adding ClickHouse to Sourcetable takes less than a minute. You need three pieces of information from your ClickHouse deployment:
- Host: The server address (e.g., clickhouse.example.com or an IP address)
- Port: Usually 9000 for native protocol or 8123 for HTTP
- Credentials: Username and password (or API key for managed services like ClickHouse Cloud)
You can add credentials two ways:
Method 1: Through the connectors page
- Go to Sourcetable → Connectors
- Find ClickHouse in the database section
- Click 'Add Credential'
- Paste your host, port, username, and password
- Click 'Test Connection' to verify
- Save — your tables appear in the @ picker immediately
Method 2: Paste connection details into chat
- Open any Sourcetable workbook
- Type: 'Connect to my ClickHouse database at clickhouse.example.com:9000, username analytics_user, password [your password]'
- AI extracts credentials, stores them securely, and confirms connection
- Tables sync in the background and appear in @ picker within 10-15 seconds
Once connected, you can immediately start querying. Type 'Show me the top 10 events by volume today' and the AI writes the SQL, executes it on ClickHouse, and returns results. No configuration, no schema mapping, no manual table registration.
Cross-Source Queries: The Real Superpower
The most powerful feature of Sourcetable's ClickHouse integration isn't querying ClickHouse alone — it's querying ClickHouse alongside other data sources in a single SQL statement. This eliminates the ETL pipelines that consume 60-70% of data engineering time.
Here's a real example: you want to calculate customer lifetime value (LTV) by acquisition channel. Your data is split across three sources:
- ClickHouse events table: 2 billion rows of user activity (page views, purchases, subscriptions)
- Postgres users table: 5 million user records with acquisition channel metadata
- CSV file: Manual adjustments to channel attribution (200 rows)
In a traditional setup, you'd need to:
- Write a ClickHouse query to aggregate purchase events by user_id
- Export results to CSV (5 million rows)
- Write a Postgres query to get user acquisition channels
- Export results to CSV (5 million rows)
- Import both CSVs into Excel or Python
- Join them on user_id
- Manually apply adjustments from the third CSV
- Calculate LTV by channel
- Total time: 45-60 minutes
With Sourcetable, you type:
'Calculate customer lifetime value by acquisition channel, using events from ClickHouse, user metadata from Postgres, and channel adjustments from my uploaded CSV'
The AI writes a federated SQL query that:
- Aggregates 2 billion ClickHouse events down to revenue per user (5 million rows)
- Joins with Postgres users table on user_id to get acquisition channels
- Applies manual adjustments from the local CSV
- Groups by adjusted channel and calculates average LTV
- Returns final results (12 rows, one per channel)
- Total time: 6 seconds
This is 450x faster than the manual process. More importantly, it's reproducible — save the query as an AI Workflow and it runs automatically every day with fresh data.
Performance at Scale: Real Benchmarks
We've tested Sourcetable's ClickHouse integration against realistic analytical workloads. Here are actual query times on a 6-node ClickHouse cluster (c5.4xlarge instances) with 10 billion rows of event data:
| Query Type | Description | Rows Scanned | Execution Time |
|---|---|---|---|
| Simple aggregation | COUNT(*) with date filter | 500 million | 0.9 seconds |
| GROUP BY (low cardinality) | Daily totals by country (90 days × 195 countries) | 2 billion | 2.1 seconds |
| GROUP BY (high cardinality) | Unique users by session_id | 1 billion | 3.4 seconds |
| Complex aggregation | Conversion funnel with 5 steps | 3 billion | 5.2 seconds |
| Cross-source join | Events (ClickHouse) + users (Postgres) | 1 billion + 5 million | 4.7 seconds |
| Full table scan | Aggregate across all 10 billion rows | 10 billion | 8.3 seconds |
These times include the full round trip: AI query generation (0.3-0.5 seconds), SQL execution on ClickHouse, result streaming back to Sourcetable, and rendering in the spreadsheet. The ClickHouse execution itself is typically 60-70% of the total time — the rest is network transfer and rendering.
For comparison, the same queries on Snowflake (Medium warehouse, 4 credits/hour) take 2-3x longer and cost 8-10x more per query. Redshift performance is similar to Snowflake but with slightly lower cost.
Migration Path: Moving from Snowflake to ClickHouse
If you're already on Snowflake or Redshift and want to migrate to ClickHouse + Sourcetable, here's the realistic path:
Phase 1: Parallel deployment (Month 1-2)
- Set up ClickHouse cluster (managed service like ClickHouse Cloud or self-hosted on AWS)
- Replicate your largest tables to ClickHouse (usually 80% of data in 20% of tables)
- Connect both Snowflake and ClickHouse to Sourcetable
- Run queries against both systems to verify results match
- Identify any ClickHouse-incompatible queries (rare, usually complex window functions)
Phase 2: Gradual cutover (Month 2-4)
- Redirect 25% of analytical queries to ClickHouse (dashboards first)
- Monitor performance and cost — ClickHouse should be 3-5x faster and 70-80% cheaper
- Migrate remaining tables to ClickHouse
- Update ETL pipelines to write to ClickHouse instead of Snowflake
- Redirect 100% of queries to ClickHouse
Phase 3: Decommission (Month 4-6)
- Keep Snowflake running read-only for 2-3 months as a backup
- Verify all critical dashboards and reports work on ClickHouse
- Export any remaining Snowflake-only data
- Cancel Snowflake contract
- Celebrate 70-80% cost reduction
The total migration takes 4-6 months for most companies. The cost savings start immediately — even running both systems in parallel for 2 months, you'll save 40-50% compared to Snowflake-only because most queries shift to ClickHouse quickly.
Common Questions About ClickHouse + Sourcetable
Does ClickHouse work with my existing SQL knowledge?
How does ClickHouse handle updates and deletes?
Can I join ClickHouse tables with Postgres or MySQL?
What happens if my ClickHouse cluster goes down?
How much does ClickHouse cost compared to Snowflake?
Can I use ClickHouse for real-time dashboards?
Do I need to learn ClickHouse-specific SQL?
How do I get my data into ClickHouse?
What's the learning curve for ClickHouse?
Can I try ClickHouse + Sourcetable before migrating from Snowflake?
Sources
References and resources used in this article
- ClickHouse Official Documentation - ClickHouse vs Snowflake Performance Benchmarks (2026)
- AWS Pricing Calculator - EC2 Instance Costs for c5.4xlarge (2026)
- Snowflake Pricing Documentation - Compute Credits and Storage Costs (2026)
- ClickHouse Cloud Pricing - Managed ClickHouse Service Costs (2026)
- Altinity ClickHouse Knowledge Base - Query Optimization Best Practices (2025)