The data warehouse landscape has evolved dramatically, with the global market projected to reach $63.9 billion by 2032.
Snowflake has become a leading cloud data warehouse thanks to its fully-managed, multi-cloud architecture and features like zero-copy cloning and time-travel.
However, its usage-based pricing and specialized design can be limiting: businesses with constant heavy workloads may face high costs and must contend with Snowflake’s focus on structured data and cloud-only deployment. As a result, many B2B SaaS companies look to Snowflake alternatives – other cloud data platforms or all-in-one data stacks that offer different trade-offs.
Snowflake transformed cloud data warehousing with its innovative architecture, many organizations are discovering significant challenges:
- Complex setup and configuration requirements
- Unpredictable, usage-based cost escalation
- Need for 5 -10 additional tools to complete the data stack
- Barriers to data democratization for business users
Modern data teams need solutions that deliver 10X faster implementation, predictable pricing, and complete functionality in a single platform – capabilities that next-generation alternatives like Peliqan are pioneering through all-in-one architectures.
Why consider alternatives to Snowflake?
Snowflake excels at scalable analytics, but it isn’t one-size-fits-all. Its pay-per-use model can become expensive at scale, especially if you forget to pause idle virtual warehouses. It also has relatively limited native support for truly unstructured data types (images, video, logs, etc.)
Moreover, Snowflake’s fully cloud-based nature (AWS/Azure/GCP only) means on-premises or hybrid deployments are not supported, raising concerns about vendor lock‑in. In contrast, some alternatives offer on-premises options or flat‑rate pricing, and others bundle ETL, BI, or data activation tools into the platform.
The right Snowflake alternative depends on your data volume, existing cloud strategy, and workload patterns. Below we examine leading options in detail.
Snowflake competitors & alternatives in 2025
Snowflake alternatives: Top 12
1. Peliqan – The complete modern data stack in one platform
Peliqan is the best snowflake alternative that disrupts traditional data warehousing by consolidating the entire modern data stack into a single, user-friendly platform that enables data warehouse deployment in under 5 minutes. Founded in 2022 by industry veterans, Peliqan addresses the fundamental problem that solutions like Snowflake are too complex, expensive, and fragmented for most organizations.
Core Capabilities and Differentiators
The platform combines comprehensive functionality in one solution:
- Built-in data warehousing with automatic optimization
- ETL/ELT pipelines with 250+ pre-built connectors
- AI-assisted data transformation and “Magical SQL” features
- Complete BI tools with visual analytics and dashboards
- Advanced data activation including reverse ETL and API publishing
Unlike Snowflake’s data warehouse-only approach requiring 5-10 additional tools, Peliqan provides complete functionality including automated alerts and spreadsheet-like data exploration.
Revolutionary Speed to Value
Peliqan delivers 10X faster deployment compared to traditional implementations:
- Under 5 minutes to fully operational data warehouse
- Automatic schema discovery eliminates manual configuration
- Zero-maintenance pipelines reduce ongoing operational overhead
- AI assistance enables business users to work independently
Transparent, Predictable Pricing
Cost structure designed for budget predictability:
- Starter tier: $380/month
- Pro tier: $550/month
Note: “Peliqan offers transparent pricing – no surprises – unlike Snowflake’s unpredictable costs.”
Technical Architecture
- Cloud-native SaaS foundation with SOC 2 Type II certification
- Trino-powered federated query engine for real-time cross-source queries
- Built-in PostgreSQL warehousing plus “Bring Your Own” integration
- Multi-cloud support including AWS, GCP, and Azure
- Enterprise security with role-based access and audit logging
2. Google BigQuery – Serverless analytics at scale
BigQuery is the strongest competitor to snowflake as it is a serverless, fully managed data warehouse built on Google’s Dremel engine, offering exceptional performance for analytical workloads without infrastructure management overhead.
Key Strengths
- 11.18 seconds average runtime on TPC-DS benchmarks
- Petabyte-scale dataset support with automatic scaling
- BigQuery ML provides built-in machine learning capabilities
- Real-time streaming ingestion enables immediate analytical insights
- Serverless architecture automatically handles scaling and optimization
Pricing Structure
- On-demand pricing: $5 per TB of data processed
- Reserved slots: Starting at $2,000/month for dedicated capacity
- Storage costs: $0.02 per GB monthly for active data
- Cost predictability for variable analytical workloads
Ideal Use Cases
- Teams heavily invested in Google Cloud ecosystem
- Real-time analytics requirements with streaming data
- Ad-hoc analytical queries on large datasets
- Variable workload patterns requiring serverless scaling
3. Amazon Redshift – AWS-native analytical processing
Redshift provides mature MPP architecture with deep AWS ecosystem integration, delivering strong performance for traditional business intelligence workloads.
Performance Characteristics
- 8.24 seconds average runtime on TPC-DS benchmarks
- RA3 nodes provide separated storage and compute architecture
- Redshift Spectrum enables querying data directly in S3
- Mature optimization for traditional BI and analytical workloads
Cost Structure
- On-demand pricing: Starting at $0.25 per hour per node
- Reserved instances: Up to 75% savings for predictable workloads
- Redshift Serverless: Pay-per-RPU pricing for variable usage
- Predictable costs for organizations with steady analytical needs
Strategic Positioning
- AWS-centric organizations requiring deep ecosystem integration
- Cost-effective analytical processing needs
- Predictable usage patterns and traditional BI workflows
- Teams comfortable with AWS operational model
4. Databricks – Unified lakehouse platform
Databricks combines data lakes and warehouses in a unified platform optimized for data science, machine learning, and advanced analytics workflows.
Technical Advantages
- Built on Apache Spark with Delta Lake format for reliability
- Significant performance improvements in recent benchmarks vs Snowflake
- MLflow integration enables complete ML lifecycle management
- Delta Sharing facilitates secure data collaboration across organizations
- Unified lakehouse architecture supports both structured and unstructured data
Usage-based Pricing Model
- Databricks Units (DBUs) charged per second of usage
- Pricing tiers: From $0.15 to $0.55 per DBU depending on features
- Average business user cost: Approximately $99/month
- Scaling costs based on compute and storage requirements
Optimal Applications
- Data science teams requiring advanced ML capabilities
- Machine learning projects with complex model development
- Real-time streaming analytics and data processing
- Organizations requiring data lake functionality alongside warehousing
5. Microsoft Azure Synapse Analytics – Enterprise analytics platform
Azure Synapse unifies data warehousing and big data in an integrated platform with deep Microsoft ecosystem integration.
Core Capabilities
- MPP data warehousing combined with built-in Apache Spark
- Serverless and dedicated compute options for flexible scaling
- Deep Power BI integration for seamless business intelligence
- Azure ML integration provides comprehensive analytical capabilities
- Enterprise-grade security with Microsoft compliance standards
Pricing Structure
- Dedicated SQL pools: $1,200 to $30,000+ monthly based on DWUs
- Serverless SQL: $5.00 per TB processed for ad-hoc queries
- Apache Spark pools: $0.261 per vCore hour for big data processing
- Integrated billing with other Microsoft services
Target Market
- Microsoft-centric enterprises with Office 365 investments
- Teams requiring seamless Azure ecosystem integration
- Enterprise-grade analytics with governance requirements
- Mixed workload environments needing flexible compute options
6. Firebolt – Performance-optimized analytics
Firebolt delivers exceptional query performance through advanced indexing technology and cloud-native architecture optimized for speed.
Performance Leadership
- 4-6000x performance improvements vs traditional warehouses (customer benchmarks)
- Sub-second response times for complex analytical queries
- Advanced indexing: Sparse, aggregate, and join indexes enable data pruning
- Dedicated tenant architecture ensures consistent performance
- Real-time ingestion with immediate query availability
Cost and performance benefits
- Engine-based pricing with pay-per-use models
- 10x+ better price-performance than competitors typically
- Transparent cost structure without hidden scaling fees
- Optimized for performance-critical applications
Use Case Focus
- Customer-facing data applications requiring low latency
- Operational analytics with real-time performance needs
- Performance-critical analytical workloads where speed is paramount
- Interactive dashboards and data applications
7. ClickHouse – Open-source analytical database
ClickHouse leads OLAP performance benchmarks as an open-source columnar database specialized for analytical workloads.
Technical Excellence
- Vectorized query execution with advanced compression
- Consistently superior performance in analytical benchmarks
- Multiple specialized engines optimize different data types
- Columnar storage with bitmap indexing for fast aggregation
- Real-time data ingestion with immediate query availability
Cost Advantages
- Open-source model eliminates licensing costs entirely
- Cloud options available: ClickHouse Cloud and Altinity managed services
- Self-hosted deployments require only infrastructure costs
- Significant cost savings for organizations with technical expertise
Requirements and limitations
- Significant engineering expertise required for setup and management
- Complex operational requirements for production deployments
- Best suited for technical teams capable of handling complexity
- Limited vendor support compared to commercial alternatives
8. Teradata – Enterprise-grade analytics
Teradata provides proven enterprise performance with advanced workload management and comprehensive analytical capabilities.
Enterprise Features
- Proven MPP architecture delivers reliable performance for complex workloads
- Advanced workload management enables mixed workload optimization
- Comprehensive resource allocation and performance tuning
- Enterprise-grade reliability with decades of proven deployment
- Hybrid cloud capabilities for complex enterprise environments
Pricing:
- Compute price as low as $4.80/hour
- Block storage as low as $1,445/TB per year
- VantageCloud Lake+ includes Priority Service
Market Position
- Higher costs compared to modern cloud alternatives
- Proven reliability for large enterprise deployments
- Complex licensing and deployment requirements
- Strong performance for traditional analytical workloads
- Mature ecosystem with extensive partner integrations
9. Vertica – Columnar analytics platform
Vertica combines columnar storage with machine learning capabilities for analytical workloads requiring advanced compression and performance.
Technical Benefits
- High compression ratios reduce storage costs significantly
- Columnar storage maintains query performance while compressing data
- Eon Mode provides separated compute and storage for cloud-native deployment
- Built-in machine learning capabilities for advanced analytics
- Hybrid deployment options support both cloud and on-premises
Pricing Advantage
- Community Edition offers free usage up to 1TB of data
- Enterprise pricing provides good value for analytical workloads
- Flexible licensing based on data volume and features required
- Predictable costs for organizations with defined data volumes
10. Apache Druid – Real-time analytics database
Druid specializes in real-time analytics for time-series data with sub-second query performance.
Performance Characteristics
- Column-oriented storage with bitmap indexing
- Fast aggregation and filtering for time-series queries
- Real-time indexing supports streaming data ingestion
- Sub-second query performance for time-series analytics
- Immediate query availability for streaming data
Limitations and Considerations
- Optimized primarily for time-series use cases
- Limited support for ad-hoc analytical queries outside time-series
- Performance has lagged behind newer alternatives in recent benchmarks
- Complex setup and operational requirements
- Specialized use case focus limits broader analytical applications
11. TimescaleDB – PostgreSQL time-series extension
TimescaleDB extends PostgreSQL with time-series optimizations while maintaining full SQL compatibility.
Key Advantages
- Automatic time-based partitioning optimizes time-series workloads
- Columnar compression reduces storage requirements
- Full PostgreSQL compatibility ensures familiar development experience
- Standard SQL support with time-series specific functions
- Mature ecosystem leveraging PostgreSQL tooling and expertise
Pricing Model
- Open-source version is completely free
- Timescale Cloud managed service starting at $0.25/hour
- Flexible deployment options including self-hosted and cloud
- Cost-effective for organizations already using PostgreSQL
12. DuckDB – Embedded analytical database
DuckDB provides SQLite-like simplicity for analytical workloads with columnar storage and vectorized execution.
Unique Positioning
- Zero-configuration embedded database eliminates server setup
- No maintenance overhead or operational complexity
- Excellent performance for single-machine analytical workloads
- Perfect for data science applications and local analytics
- Columnar storage with vectorized execution for speed
Cost Structure and Options
- Completely free and open-source with no licensing costs
- MotherDuck offers managed cloud service for hybrid architectures
- Local deployment requires no infrastructure costs
- Ideal for development and smaller-scale analytical workloads
Snowflake competitors comparison
Selecting the optimal Snowflake alternative depends on your specific organizational needs, technical requirements, and strategic priorities. Here’s the quick comparison of top Snowflake alternatives.
Peliqan vs. Snowflake: Quick Comparison
Peliqan provides a broader range of features, greater flexibility, and advanced tools that Snowflake simply doesn’t offer. Here’s how Peliqan stack up against Snowflake:
 |
 |
|
|---|---|---|
| ETL built-in Wide range of connectors for SaaS, files and databases |
Yes | No |
| New connector service New connectors built in 5 business days |
Yes | No |
| Data warehouse Built-in or use your own (Snowflake, BigQuery…) |
Yes | Yes |
| Transformations Combine SQL and low-code Python. |
Yes | Yes |
| Reverse ETL Sync data into business applications. |
Yes | No |
| Data activation Custom reporting using Excel, publish data APIs, build LLM chatbots, automations. |
Yes | No |
| iPaaS automations Implement automations for data sync, app integrations. |
Yes | No |
| Market place Deploy Airflow, Metabase, and a wide range of other solutions with one click. |
Yes | No |
| White-label & multi-customer management Manage data projects for end-customers at scale. |
Yes | No |
| Encryption In transit and at rest |
Yes | Yes |
| Customer support & onboarding Personalized onboarding, training and support |
Yes | Yes |
| Transparent pricing No surprises, clear budget upfront |
Yes | No |
Conclusion
In summary, Snowflake competitors span a range of cloud data solutions. Peliqan stands out as a #1 alternative by packaging ETL, storage, and analytics together. The major cloud warehouses – Amazon Redshift, Google BigQuery, and Azure Synapse – each excel when paired with their respective cloud ecosystems.
Databricks provides a unified lakehouse for data engineering and ML. Other niche platforms (ClickHouse, Trino, etc.) may suit specialized needs. Use the comparison table and factors above to identify the best fit for your workload. Choosing the right platform will enable your business teams to get actionable insights from data, without unnecessary complexity or cost.
By carefully evaluating features (e.g. multi-cloud support, built-in ETL, real-time BI) and pricing models, you can pick a Snowflake alternative that accelerates your analytics. For B2B SaaS companies in particular, Peliqan’s all-in-one approach is worth exploring, and demos or trials are highly recommended to compare hands-on.
The Next Generation Advantage
While Snowflake pioneered cloud data warehousing, the next generation prioritizes eliminating barriers to data democratization through complete platforms that business teams can deploy and use without extensive technical dependencies.
Peliqan represents this evolution most clearly:
- Entire modern data stack in a single platform
- Unprecedented speed with under 5-minute deployment
- Simplicity that enables business user independence
- Cost predictability that eliminates budget surprises
For teams seeking maximum speed, simplicity, and value, Peliqan’s unified approach offers compelling advantages over traditional multi-vendor strategies that require extensive integration, ongoing maintenance, and unpredictable scaling costs.
