Choosing between Snowflake and Redshift impacts enterprise success: the wrong data warehouse decision costs organizations an average of $2.3M annually in wasted compute resources, delayed analytics insights, and complex integration overhead, according to a recent enterprise IT spending analysis.
The modern data warehouse decision represents a strategic architecture choice spanning multiple competitive options, including Snowflake vs. Redshift vs. Databricks, and emerging players like MotherDuck, as well as specialized solutions like ClickHouse. The implementation typically demands 2-5 year commitments, with a total cost of ownership (TCO) ranging from $500K to $5M+ annually for infrastructure, staffing, and operational expenses.
This analysis provides a structured framework for evaluating both platforms across architecture, performance, cost, integration, and implementation considerations.
Why your data warehouse choice defines your data strategy and ROI
Enterprise data volumes have reached enormous scale, driven by IoT telemetry, clickstreams, and AI model training requirements.
The Global Cloud Data Warehouse market is projected to grow from $12.7 billion in 2025 to $41.5 billion by 2033, with Snowflake, Databricks, and Amazon Redshift capturing a significant market share.
Enterprise executives consistently have the same frustrations: infrastructure costs eating up IT budgets without proportional ROI, analytics teams waiting hours for query results that should take minutes, and AI/ML initiatives delayed by months due to inadequate data platform capabilities.
Organizations face four critical pressure points:
Data volume growth. Petabyte-scale analytics have become standard, requiring platforms that scale without performance degradation.
Cost management. Cloud spend increasingly outpaces budgets, demanding precise resource allocation, automated cost controls, and consumption-based pricing models that align with actual usage patterns.
Real-time insights demand. Market leadership depends on immediate, evidence-based decisions rather than batch processing.
Integration complexity. Modern enterprises connect 100+ data sources across multi-cloud, on-premises, and SaaS systems, requiring cloud-agnostic platforms with robust API ecosystems and pre-built connectors.
The consequences of poor platform selection:
- budget overruns and project stagnation from ballooning infrastructure costs without matching ROI
- missed opportunities for real-time decisions due to delayed analytics and performance bottlenecks
- broken workflows when rigid platforms cannot handle diverse data types and scaling requirements
- excessive operational overhead consuming 30-40% of engineering resources on platform maintenance
Architecture differences between Snowflake and AWS Redshift
Snowflake and Redshift are both mature, battle-tested, and enterprise-grade data warehouses processing trillions of queries monthly across global cloud infrastructures.
Snowflake cloud-native separation model
Snowflake separates storage, compute, and cloud services into distinct layers, enabling independent scaling and workload isolation.
Key characteristics:
- Virtual warehouses auto-scale with workload demand and pause during idle periods, reducing compute costs
- Independent compute clusters ensure complete workload isolation, preventing resource contention when BI teams run dashboards simultaneously with data engineering ETL processes.
- Multi-region deployment spans cloud regions across AWS, Azure, and GCP, supporting data sovereignty requirements and sub-100ms disaster recovery RPOs.
Performance profile: Snowflake performs best in environments with mixed, concurrent workloads. Multiple virtual warehouses handle simultaneous BI dashboards, ETL processes, and analytical queries without performance degradation.
The platform processes semi-structured data natively, eliminating preprocessing requirements for JSON, logs, and event data, which allows it to manage large-scale analytics and complex enterprise workloads.
Redshift AWS-optimized shared architecture
Amazon Redshift combines compute and storage within massively parallel processing (MPP) clusters, deeply optimized for AWS ecosystem integration.
Key characteristics:
- Managed storage layers enable independent compute and storage scaling while maintaining tight integration for performance
- Serverless and provisioned deployment options provide flexibility between predictable capacity and elastic, on-demand scaling
- Native AWS integration streamlines data pipelines across S3, Lambda, and other AWS services with minimal configuration
Performance profile: Redshift performs optimally with predictable, batch-oriented workloads analyzing large datasets. The platform’s columnar storage and query optimization techniques accelerate analytical queries on structured data.
Concurrency scaling automatically adds capacity during demand spikes, but the tightly coupled architecture means workload isolation requires more deliberate cluster configuration.
Snowflake vs Redshift architecture relevance for enterprise needs
| Enterprise dimension | Snowflake | Redshift | Enterprise implication |
|---|---|---|---|
| Multi-cloud strategy & exit flexibility | Runs across AWS, Azure, GCP; multi-cloud posture provides flexibility in switching costs | AWS-exclusive; deep ecosystem integration increases switching costs but provides cost optimization within AWS | Strategic positioning: Snowflake enables cloud diversification and reduces dependency risk; Redshift optimizes for AWS-centric strategies with potential lock-in trade-offs |
| Compute-storage architecture | Fully decoupled storage and compute; independent scaling of resources | Semi-coupled architecture; RA3 nodes offer some separation, older node types couple compute-storage | Resource optimization: Snowflake provides granular resource scaling; Redshift requires architectural planning for efficient resource utilization |
| Scaling mechanisms | Instant elastic scaling (seconds); automatic resource adjustment | Manual cluster scaling (15-60 minutes); concurrency scaling available with additional costs | Operational agility: Snowflake handles demand spikes automatically; Redshift requires capacity planning and manual intervention |
| Performance management | Multi-cluster shared-data architecture automatically isolates workloads; zero-tuning concurrency handling | Shared cluster resources require workload queue management; manual performance tuning for optimal throughput | Service level consistency: Snowflake delivers predictable performance without specialized expertise; Redshift demands ongoing DBA investment for performance |
| Data collaboration & monetization | Native secure data sharing without data movement; supports external data marketplace participation | Data sharing through AWS Data Exchange or S3 replication; traditional ETL processes required | Revenue opportunities: Snowflake enables data-as-a-product business models; Redshift collaboration requires traditional data integration approaches |
| Operational complexity | Fully managed service; minimal tuning required; automatic optimization | Hands-on management; requires tuning, service integration, distribution key optimization | Platform management demands:: Snowflake reduces operational overhead; Redshift may require dedicated engineering expertise |
Performance benchmarks and enterprise workloads
Standard industry benchmarks, such as TPC-DS, show marginal differences between platforms under idealized conditions. However, research analyzing production Amazon Redshift deployments reveals significant discrepancies between these standardized benchmarks and actual cloud data warehouse workloads. Workload patterns, concurrency demands, and data characteristics significantly influence which platform delivers better results for your specific use cases.
Snowflake for consistency and concurrency
Snowflake offers consistent performance during peak usage periods, faster time to insight, and fewer slowdowns that derail revenue-critical reporting and business decisions.
Capital One slashed query times by 43% while handling 50 petabytes of data used by 6,000 analysts, which is a clear win for Snowflake’s elastic, multi-cluster design. With Snowflake, their data warehouse doesn’t choke when demand spikes, helping to speed up empirical decision-making.
Organizations with unpredictable traffic patterns benefit most from Snowflake’s architecture. When BI dashboard refreshes overlap with ETL processes and ad-hoc data exploration, Snowflake’s per-warehouse isolation and instant scale-out capabilities maintain stable response times. It is good at handling mixed workload concurrency that would overwhelm traditional shared-resource systems.
Redshift for stability and scale
Redshift delivers consistent throughput for large-scale analytical workloads through intelligent query optimization, advanced caching, and tight integration with AWS services, particularly effective for steady and predictable data processing patterns.
If your loads are steady and predictable, Redshift’s cluster-based design and Redshift Spectrum capabilities allow cost-effective querying of extensive data archives stored in S3 without duplication. Predictable capacity planning enables consistent SLA achievement for scheduled analytical processes.
Snowflake vs Redshift for enterprise data performance
| Enterprise consideration | Snowflake | Redshift | What it means for enterprises |
|---|---|---|---|
| Workload variability | Consistent scaling & isolation (separate virtual warehouses) keep mixed, bursty workloads responsive | Cluster-based design is well-suited to steady, batch-oriented workloads with known peaks | Choose Snowflake for variable demand; Redshift works best for consistent, planned workloads |
| Concurrent user performance | Dedicated compute per workload reduces contention, so one job rarely slows another | Shared cluster resources can contend; extra capacity can be added, with the need to plan/size for peaks | Better isolation leads to fewer incidents when multiple teams hit the system at once |
| Performance predictability | Scales out quickly to keep response times stable as concurrency rises | Predictable when peaks are planned; over-provisioning/tuning needed for unpredictable spikes | Snowflake suits better for unpredictable SLAs; Redshift- for well-defined performance requirements |
| SLA alignment | Architecture aligns well with SLAs that require agility across mixed workloads | Meets SLAs for steady pipelines; bursty, interactive scenarios may require extra capacity/tuning | Map SLAs to a specific workload shape: Snowflake is stronger for mixed or variable demand; Redshift fits steady, predictable demand |
Snowflake vs Redshift cost optimization: Consumption-based vs predictable pricing strategies
This is where technical decisions become CFO conversations. Understanding how each platform’s pricing structure aligns with your usage patterns determines the total cost of ownership and budget predictability.
Redshift pricing: Predictable costs with reserved capacity advantages
Redshift offers two primary pricing models designed for different enterprise scenarios, from steady analytical workloads to variable demand patterns.
Redshift Provisioned node-hour billing reserves predictable monthly expenses but risks overpayment during idle periods. Reserved instances offer up to 75% savings for steady analytical workloads through 1-3 year commitments, making Redshift an attractive option for predictable, high-utilization scenarios.
Redshift Serverless, recently introduced by Amazon for variable demand patterns with pay-per-query pricing (billing by the RPU-second with a 60-second minimum), provides cost savings for fluctuating workloads.
Snowflake pricing: Pay-per-use with automatic cost controls
Snowflake bills compute and storage separately.
Virtual warehouse compute is billed per second with a 60-second minimum each time it starts or resumes. When idle, the warehouses automatically pause, eliminating the classic problem of right-sizing and paying for unused capacity.
For organizations with variable or seasonal workloads, this consumption-based model aligns expenses directly with usage, yielding about 20% cost reduction.
Instacart cut their infrastructure costs by 50% while slashing complex query times from over 10 minutes to just 5 seconds. They also eliminated costly weekly maintenance operations, freeing their data teams to focus on innovation and real-time analytics instead of firefighting.
Cost optimization best practices
| Platform | Practices | Enterprise implications |
|---|---|---|
| Amazon Redshift | Reserved Instances can yield up to 75% savings for steady workloads (1–3 year commitments) | For predictable, high-utilization pipelines, reservations materially lower run-rate versus on-demand |
| Use Redshift Spectrum to query data in S3 in place (pay-per-byte scanned; minimum query size is 10 MB). | Avoid duplicating lake data into the warehouse; lower storage footprint and pay only for what you scan. | |
| Right-size nodes & tune (distribution/sort keys; Auto WLM) to control compute and storage. | Better table design and workload management reduce query time and wasted capacity, improving cost/performance | |
| Snowflake | Enable auto-suspend / auto-resume; compute bills per second with a 60-second minimum | Eliminate idle spend and match capacity to demand; savings hinge on sensible suspend/resume settings |
| Monitor usage with Resource Monitors and credit controls | Prevent overages; alert/suspend before budgets are exceeded (governance that finance can trust) | |
| Optimize storage via Time Travel/Fail-safe retention; use transient/temporary tables when appropriate | Keep rollback where needed and trim retention elsewhere to control storage costs | |
| Use multi-cluster warehouses selectively for bursts (concurrency) | Maintain performance under spikes without paying for always-on extra clusters |
Hidden costs that impact total ownership expenses
Enterprise data warehouse costs extend beyond headline pricing through data movement, storage retention, operational overhead, and compliance requirements.
- Data movement economics. Both platforms charge for cross-region data transfers. Snowflake charges for cross-cloud transfers, while Redshift follows standard AWS data transfer rates. Strategic data placement significantly reduces these costs.
- Storage and retention. Snowflake storage runs approximately $23 per TB monthly, with automatic Time Travel and Fail-safe. Redshift storage varies by node type, with RA3 nodes offering managed storage at $0.024 per GB per month.
- Operational overhead. Snowflake’s automatic clustering consumes compute credits but eliminates manual maintenance. Redshift’s automatic optimization runs during maintenance windows, using cluster resources while reducing administrative burden.
- Security compliance fees. Redshift’s AWS-integrated compliance tools often reduce the costs associated with third-party security tooling. Snowflake’s built-in governance features eliminate the need for custom compliance automation development.
Integration capabilities of Redshift and Snowflake
The decision to integrate a platform reflects a broader organizational technology strategy.
AWS ecosystem depth of Redshift
Redshift’s power lies in its integration with the AWS ecosystem. If your enterprise runs predominantly on AWS services, with the business objective of maximum consistency, reduced latency, and risk, while leveraging existing investments, Redshift integrations may deliver faster time to value.
Direct connectivity with S3, Glue, RDS, and EMR eliminates complex data movement processes.
Redshift Spectrum enables in-place S3 querying without data duplication, reducing storage costs and maintaining a single source of truth.
Native IAM integration and VPC deployment align with existing AWS security models, streamlining governance and compliance processes.
Advanced workload integration: SageMaker orchestration enables integration of enterprise machine learning workflows within the AWS environment.
Redshift ML allows SQL-based model training and inference without data movement to external services.
Zero-ETL integrations with Aurora, DynamoDB, and other AWS databases provide real-time analytics capabilities with minimal operational overhead.
Multi-cloud flexibility of Snowflake
Snowflake’s cloud-agnostic design supports frictionless operations across providers and partner ecosystems. For enterprises pursuing multi-cloud strategies or vendor independence, Snowflake likely delivers a stronger ROI.
A single operating model across AWS, Azure, and GCP clouds enables consistent governance and security frameworks.
Cross-cloud data sharing and replication (Snowgrid) guarantee collaboration without copying data, opening new partner and product models.
Western Union reduced infrastructure costs by 50% while achieving a single source of truth by adopting a multi-cloud approach. With Snowflake’s vendor-neutral architecture, they migrated 34 data warehouses seamlessly, powering insights for 250+ million customers worldwide without compromise.
Ecosystem breadth advantages: Native connectivity with leading BI tools (Tableau, Power BI), data integration platforms (Fivetran, dbt), and analytics frameworks provides operational flexibility.
Snowpark enables Python, Java, and Scala execution directly within the data warehouse, minimizing data movement for advanced analytics and machine learning workflows.
Enterprise integration decision matrix
| Integration dimension | Redshift | Snowflake | Enterprise implications |
|---|---|---|---|
| Cloud strategy alignment | AWS-centric; designed to work indigenously with AWS services | Supports multi-cloud strategies and vendor independence | Choose Redshift to maximize an all-AWS strategy; Choose Snowflake to support multi-cloud or avoid lock-in |
| Data & pipelines | Simplified AWS-native data movement with S3, Glue, and Lambda | Native support for semi-structured & unstructured data; external tables and objects blend warehouse and data lake patterns | Redshift lowers integration overhead inside AWS; Snowflake simplifies mixed lake/warehouse patterns across clouds |
| ML/AI workflow integration | SageMaker and Redshift ML for AWS-native machine learning stacks | Snowpark runs Python/Java/Scala next to the data for ML and data engineering | Redshift fits AWS-native ML stacks; Snowflake minimizes data movement for in-platform ML/engineering |
| Security & governance | VPC integration and IAM controls align with AWS security models | Cross-cloud governance is consistent; features like Time Travel, zero-copy cloning, and data sharing enhance control | Redshift streamlines security for AWS shops; Snowflake enables standardized governance across providers |
| BI & partner ecosystem | Strong with AWS tools and common BI; benefits most when the stack is AWS-first | A broad partner ecosystem (Tableau, Power BI, dbt, Fivetran, etc.) and data sharing enable advanced collaboration | Redshift reduces friction in AWS-standardized ops; Snowflake accelerates cross-team and external collaboration |
Security and compliance: Enterprise-grade protection
Redshift leverages AWS’s infrastructure-level security foundation with deep ecosystem integration, while Snowflake provides granular, adaptive controls designed for multi-cloud governance and cross-organizational data sharing scenarios.
Regulatory compliance landscape
AWS’s shared responsibility model automatically extends 143+ security certifications to Redshift deployments:
- Foundational frameworks (SOC 1/2/3, ISO 27001, PCI DSS Level 1)
- Specialized certifications for healthcare (HIPAA), financial services, and government sectors (FedRAMP).
This inheritance approach means organizations can achieve compliance faster because foundational security controls are pre-certified and continuously monitored by AWS’s dedicated compliance teams.
Snowflake maintains consistent compliance standards across all major cloud providers (AWS, Microsoft Azure, GCP), offering organizations flexibility without fragmentation.
This approach guarantees the same security controls, governance standards, and audit capabilities for any underlying infrastructure.
The platform’s ISO/IEC 42001 certification covers AI system lifecycle management, risk assessment protocols, and algorithmic transparency requirements that traditional data warehouses often overlook. At the same time, granular data protection capabilities extend beyond basic encryption to support complex regulatory scenarios.
Industry-specific security
Organizations with established AWS security frameworks benefit from Redshift’s native integration with Key Management Service (KMS), CloudTrail auditing, and Identity and Access Management (IAM).
This unified approach creates compliance postures that regulators recognize and trust, particularly valuable for government and highly regulated sectors.
Snowflake’s multi-tenant isolation and secure data sharing features address complex scenarios common for cross-industrial collaborations, service partnerships, and consumer-facing sectors.
The zero-copy cloning and Time Travel features provide immutable audit trails that satisfy privacy requirements for data lineage and change tracking without compromising security boundaries.
Industry-specific security provisions
| Industry | Redshift fit & advantages | Snowflake fit & advantages |
|---|---|---|
| Financial services & Banking | Strong AWS-native controls: fine-grained IAM access, private networking, full audit logs for regulators | Strong data safeguards: column-level encryption, dynamic masking, and Time Travel for a clear audit history |
| Healthcare & Life Sciences | Unified governance for S3 data via Lake Formation ( for clinical/EHR data managed inside AWS) | HIPAA-friendly sharing: object-level controls, cross-cloud governance, BAAs for multi-provider networks |
| Government & Defense | Built for regulated AWS environments: FedRAMP High and AWS GovCloud support with continuous compliance tooling | Run multiple classification levels on one platform using isolated virtual warehouses with less infrastructure and clear separation |
Advanced data security features
Snowflake’s data protection features include end-to-end encryption with customer-managed key support, role-based dynamic masking, column-level access policies, and zero-copy cloning, which maintains inherited security controls.
Enterprise SSO integration supports multi-factor authentication and will block single-factor password logins by November 2025.
Workload isolation reduces blast radius during incidents; automatic OS patching and security updates eliminate infrastructure management overhead while maintaining consistent protection.
Multi-cloud architecture distributes vendor risk across AWS, Azure, and Google Cloud, with built-in sovereignty controls that enable secure cross-regional data sharing, ensuring compliance with local regulations.
Database encryption in Redshift is enabled by default, with support for AWS KMS customer-managed keys. VPC endpoints eliminate public internet exposure while dynamic data masking and column-level security protect sensitive data.
Native AWS IAM integration offers unified identity management, featuring role-based access control and time-bound permissions. Automated credential rotation reduces administrative overhead for AWS-standardized organizations.
Serverless mode handles automatic security updates without maintenance windows; integration with AWS monitoring stack (CloudWatch, GuardDuty, CloudTrail) provides centralized security visibility.
Advanced data security features
| Security dimension | Snowflake key points | Redshift key points | Enterprise takeaway |
|---|---|---|---|
| Data protection at scale | E2E encryption (CMKs supported), role-based masking, column policies, zero-copy cloning | VPC endpoints keep traffic off the public internet; unified, fine-grained controls | Snowflake enables safer analytics and fast test environments; Redshift provides native controls and a smaller attack surface |
| Authentication & access | Enterprise SSO; blocking password-only sign-ins by Nov 2025; scalable RBAC | IAM with privileged access, time-bound grants, automated rotation | Both reduce insider/credential risk; AWS-standardized orgs see lower admin overhead on Redshift |
| Operational security & monitoring | Managed isolation reduces blast radius and OS patching burden | Centralized AWS monitoring; Serverless handles updates automatically | Snowflake delivers faster containment without re-architecture Redshift provides unified monitoring, fewer maintenance windows. |
| Vendor concentration & risk | Cross-cloud flexibility; requires coordinated policies | Single-provider governance; higher vendor dependency | Snowflake guarantees portability & leverage; Redshift offers simpler governance, concentrated risk |
| Data sovereignty & residency | Residency in 50+ regions (mid-2025); built-in sovereignty controls for cross-cloud sharing | Region-anchored residency; Local Zones; AWS DPAs and Standard Contractual Clauses. | Snowflake helps meet regional rules without redesign. Redshift offers straightforward single-cloud residency |
Migration and implementation strategies and considerations
When migrating data warehouses, the architectural differences between Snowflake and Amazon Redshift shape the approach, timeline, and complexity.
In every case, successful migrations hinge on thorough planning, incremental validation, and realistic expectations.
A proof of concept (POC) typically takes 2 to 4 weeks across both platforms, with full production migrations lasting 8 to 16 weeks, depending on data volume and integration complexity.
Complete adaptation, including optimization and tuning, often requires several months as teams adjust workflows to the platform’s unique capabilities and operational model.
Redshift migration strategies for AWS-centric organizations
For enterprises embedded in the AWS ecosystem, migrating to Redshift is a relatively straightforward process.
With much data already on Amazon S3, native integrations and tools like AWS Database Migration Service streamline data transfer. Redshift’s PostgreSQL compatibility means existing SQL code requires fewer changes, reducing development and testing time.
Recent AWS enhancements (automated schema conversion and parallel data loading) can cut migration time by about 30% for AWS-centric organizations.
Snowflake migration framework for portability-focused companies
Snowflake enables parallel operation of legacy and new systems, lowering cutover risks by allowing simultaneous data access and validation.
Its automatic schema detection and native support for semi-structured data accelerate initial loads and integration, making it perfect for complex or fast-changing datasets.
The Time Travel feature adds rollback and recovery options, enhancing safety during transitions.
Enterprise platform selection framework
The choice between Snowflake and Redshift is objectively about which platform aligns with your enterprise’s specific requirements, existing investments, and strategic direction.
Strategic decision criteria
Architecture drives operations and cost
If your business model needs portability and partner distribution, Snowflake makes it easy to say “yes” without a migration party every quarter.
If your mandate is to stay tight in AWS and you value one toolchain, one security stack, one bill, Redshift keeps the blast radius (and the meetings) smaller.
Proof of concept is essential
Test your choice first. Run your real workloads, steady and complex, check security controls, and mirror true TCO (including admin effort). Measure performance, cost, and operational complexity objectively to gain a clear picture.
Evaluation methodology
Evaluate each platform against your specific technical needs. Assess architecture, workload patterns (batch vs real-time), data volumes and types (structured vs semi-structured), query complexity, and integration requirements. Document actual usage patterns to inform future planning.
Weight factors based on business impact, like high-frequency BI queries, deserve more consideration than occasional ad-hoc analysis.
Build realistic TCO models. Include compute infrastructure budget, storage, data growth, transfer, and retention, user expansion, and workload evolution across 3-year growth projections.
Factor in the hidden costs of backup, audit, vendor management, and administrative overhead.
Analyze your organization’s readiness. Estimate your teams’ capabilities, existing cloud expertise, database admin resources, change management, and alignment with the broader technology strategy and success metrics.
Validate training requirements, need for capacity scale, and operational model changes.
Evaluate vendor concentration risk. Include validation frameworks for compliance requirements, rollback procedures, and optimization timelines in the roadmap. Plan for exit cost scenarios and strategic cloud positioning.
Consider how each platform fits into your enterprise risk framework and long-term technology independence goals.
Choose Redshift for AWS-centric operations
Unified AWS operations
Redshift plugs into core AWS services, so you reuse existing governance, skills, and security patterns rather than building parallel processes.
Predictable cost management
If analytics usage is relatively stable, reserved capacity offers significant savings compared to on-demand and simplifies budgeting.
Data lake integration
Redshift Spectrum queries S3 data directly, eliminating duplication and reducing storage costs and maintenance overhead for analytics on historical data.
Regulatory compliance
Built-in support for AWS compliance programs, including GovCloud and FedRAMP pathways for government and highly regulated industries.
Choose Snowflake for multi-cloud flexibility
Cross-cloud operations
Unified platform on AWS, Azure, and Google Cloud, with mechanisms to share data across regions/clouds for partner and product use cases.
Usage-based economics
Per-second billing aligns costs with actual demand, particularly valuable for seasonal workloads, development environments, and unpredictable usage patterns.
Collaborative capabilities
Secure data sharing and zero-copy cloning enable development/testing and external access without duplicating datasets.
Operational simplicity
A fully managed platform with automatic optimization eliminates infrastructure management complexity, allowing you to focus on business processes.
The Xenoss perspective: Proven approach for enterprise success
After helping organizations with over 200 data platform projects, the choice between Snowflake and Redshift usually comes down to how well each platform fits your organization, rather than which has better features.
Companies that choose Snowflake typically reduce their operational costs by 30-40%, while those opting for Redshift prefer the predictable costs and tight AWS integration.
Platform choice now significantly impacts how quickly you can innovate. Today’s companies require their data platforms to handle AI training, make real-time decisions, and share data with partners.
Next steps: Understand your current workloads and usage patterns first. Run tests with your actual data and scenarios. Plan for the entire journey, from evaluation to team training.
