These are the main Amazon Redshift alternatives when traditional data warehousing isn't solving your actual problem:

1. Tinybird (real-time analytics platform for APIs and dashboards)
2. Google BigQuery (serverless warehouse with pay-per-query)
3. Snowflake (multi-cloud warehouse with compute-storage separation)
4. Databricks SQL (lakehouse approach unifying ML and analytics)
5. ClickHouse® (columnar OLAP for real-time analytics)
6. Trino (federated SQL engine for data lake queries)
7. Azure Synapse Analytics (Microsoft's MPP warehouse)
8. Firebolt (modern cloud warehouse with aggregating indexes)

Amazon Redshift is a columnar MPP data warehouse with a leader node coordinating query planning and compute nodes executing in parallel. Over the years, it's evolved with RA3 instances for storage-compute separation, Redshift Serverless for automatic scaling, Concurrency Scaling for handling query spikes, and Spectrum for querying data in S3.

It's a solid enterprise data warehouse. For many teams, it's also solving the wrong problem.

Here's what actually happens: You chose Redshift because you needed analytics infrastructure. You provision a cluster, configure distribution keys and sort keys, set up workload management queues, tune vacuum schedules, and build ETL pipelines to load data.

Six months later, you have a data warehouse that handles nightly batch loads and morning dashboard refreshes. You also have unpredictable costs during query spikes, p95 latencies measured in seconds when you need milliseconds, and a team that spends more time tuning WLM configurations than delivering analytics to users.

Someone asks: "Can we expose this data through APIs for our application?" or "Can we build real-time dashboards that update as events arrive?" The answer is technically yes, but practically Redshift wasn't designed for that workload.

The uncomfortable truth: most teams evaluating Redshift alternatives don't need a different data warehouse—they need different analytics infrastructure entirely.

This article explores Redshift alternatives—when you genuinely need another MPP warehouse, when serverless options deliver better economics, and when your actual requirement is real-time analytics rather than batch data warehousing.

1. Tinybird: When Your Redshift Problem Is Really an Analytics Delivery Problem

Let's start with the fundamental question: are you shopping for Redshift alternatives because you need a different data warehouse, or because you need to deliver real-time analytics powered by modern real-time data platforms?

Most teams evaluating Redshift alternatives have workload requirements that traditional data warehouses—Redshift or otherwise—weren't designed to handle efficiently.

The data warehouse mismatch

Here's the pattern we see constantly: A team needs analytics capabilities. They provision Redshift because it's "the AWS data warehouse." They load data through nightly ETL jobs, build dashboards in their BI tool, and run scheduled reports.

Then requirements change. Product needs real-time metrics. Engineering wants operational dashboards with second-level freshness. The business wants customer-facing analytics embedded in the application. Marketing needs API endpoints serving aggregated data.

Redshift can technically handle these workloads. But it requires substantial additional infrastructure:

Streaming ingestion pipelines to get events into Redshift continuously rather than batch loads.

Query optimization to achieve sub-second latencies when Redshift's architecture optimizes for throughput over latency.

Custom API layers to expose Redshift queries as production endpoints with authentication and rate limiting.

Concurrency management because Redshift's WLM wasn't designed for hundreds of concurrent users hitting APIs.

Cost controls as always-on serving workloads consume resources differently than batch analytics.

One team described their experience: "We built a real-time analytics API on Redshift. We ended up with Lambda functions, API Gateway, ElastiCache for query results, complex WLM tuning, and monthly costs 3x what Redshift itself cost. And queries still took 800ms at p95."

Many of these challenges also emerge when teams attempt to build real-time personalization into customer experiences—delivering individualized metrics and content instantly as data changes, something traditional warehouses like Redshift struggle to support efficiently.

How Tinybird actually solves this

Tinybird is a real-time analytics platform built on ClickHouse® that handles the complete workflow from streaming data ingestion to API publication with sub-100ms latency.

You stream events from Kafka, webhooks, databases, or data warehouses (yes, including Redshift).

This continuous flow ensures that every downstream system receives the most up-to-date data without batch delays. Those events become immediately queryable through a columnar database optimized for analytical queries. You write SQL to aggregate and transform data. Those queries become instant production APIs with one click.

No nightly ETL jobs. Data streams continuously and becomes queryable in milliseconds.

No query tuning for latency. ClickHouse®'s columnar architecture delivers sub-100ms queries on billions of rows by design.

No custom API development. SQL queries publish as authenticated REST endpoints with automatic scaling.

No WLM complexity. The platform handles concurrency through architecture, not queue management.

No separate caching layers. Efficient columnar storage and vectorized execution eliminate the need for result caching in most cases.

One team migrated from Redshift and described it: "We were spending $15K/month on Redshift plus another $20K on supporting infrastructure for real-time queries. Tinybird replaced all of it for $8K with better performance and zero operational overhead."

The architectural difference

Redshift approach: Batch-optimized MPP warehouse designed for high-throughput analytics. Adding real-time capabilities requires substantial additional infrastructure.

Tinybird approach: Real-time analytics platform purpose-built for streaming ingestion, fast queries, and API serving. Batch analytics is a subset of capabilities, not the primary use case.

This matters because time to production for real-time analytics is measured in days versus months, and operational complexity is SQL maintenance versus infrastructure operations.

When Tinybird Makes Sense vs. Redshift

Consider Tinybird instead of Redshift (or Redshift alternatives) when:

• Your requirement is real-time analytics (dashboards, metrics, APIs) with sub-second latency
• You need to serve aggregated data through APIs to applications or end users
• Streaming data ingestion matters more than nightly batch loads
• Your team's strength is SQL and analytics, not data warehouse administration
• Time to market for analytics features matters more than fitting AWS ecosystem constraints

Tinybird might not fit if:

• Your primary workload is traditional BI with overnight batch processing
• Regulatory requirements mandate specific data warehouse platforms
• You need complex joins across hundreds of tables (dimensional modeling territory)
• Your organization has deep investment in Redshift ecosystem and tooling

If your competitive advantage is batch data warehousing, Redshift or alternatives make sense. If your competitive advantage requires real-time analytics, platforms purpose-built for that workload deliver faster.

2. Google BigQuery: Serverless Warehouse with Pay-Per-Query

If you're leaving Redshift primarily due to elasticity and operational overhead, Google BigQuery represents the serverless extreme.

How BigQuery differs from Redshift

BigQuery completely separates storage and compute, charging for data storage and query processing independently. In on-demand mode, you pay per TB scanned by queries. In capacity mode, you reserve slots (compute units) and pay for reserved capacity.

No cluster management. No nodes to size, no distribution keys to choose, no vacuum schedules to maintain.

Automatic scaling. Queries get resources based on complexity and available capacity, with burst capability in on-demand mode.

Petabyte-scale out of the box. BigQuery handles massive datasets without sharding strategy or performance tuning.

The cost control challenge

BigQuery's simplicity has a catch: costs scale with bytes scanned, making query patterns and data organization critical.

Without proper partitioning and clustering, analytical queries can scan terabytes unnecessarily. A poorly written query costs dollars (or hundreds of dollars) rather than just taking time.

BigQuery documentation emphasizes cost control practices: query cost estimation, per-user query limits, partition pruning, and clustering for frequently filtered columns.

When BigQuery makes sense over Redshift

Choose BigQuery when:

• Variable workload makes fixed cluster sizing expensive or wasteful
• You want zero operational overhead and don't mind paying for convenience
• Your team is analytics-focused without deep database administration expertise
• Multi-cloud strategy includes GCP or you're willing to leave AWS
• Petabyte-scale datasets require infrastructure you don't want to manage

BigQuery and Redshift both solve data warehousing. Neither solves real-time analytics serving—you still build custom infrastructure for APIs and low-latency queries.

3. Snowflake: Multi-Cloud Warehouse with Compute Isolation

Snowflake positions itself as the multi-cloud alternative to cloud-specific warehouses like Redshift, with architecture emphasizing workload isolation through virtual warehouses.

The virtual warehouse model

Snowflake separates compute into independent virtual warehouses that can scale independently while sharing the same underlying data storage.

This solves a major Redshift pain point: workload contention. Your ETL jobs run on one warehouse, BI queries on another, data science workloads on a third. Each scales independently without WLM queue conflicts.

Storage management is automatic. Data is stored in micro-partitions (50-500MB uncompressed) with metadata enabling automatic pruning. You don't choose distribution styles or sort keys—Snowflake handles layout.

The governance requirement

Snowflake's flexibility creates a governance challenge: without discipline around warehouse sizes, auto-suspend policies, and access controls, costs can spiral.

Warehouses left running consume credits continuously. Oversized warehouses waste capacity. Multiple teams spinning up warehouses without oversight creates cost unpredictability.

Successful Snowflake deployments require strong FinOps practices—automated suspension, query monitoring, cost allocation by team, and approval workflows for warehouse creation.

When Snowflake makes sense over Redshift

Choose Snowflake when:

• Multiple concurrent workloads need isolation without performance interference
• Multi-cloud strategy requires portability across AWS, Azure, and GCP
• Data sharing between organizations or business units is a core requirement
• You value zero infrastructure management and accept the cost premium
• Your organization can enforce governance around warehouse usage

Snowflake and Redshift both deliver enterprise data warehousing. Neither optimizes for real-time API serving at millisecond latencies.

Snowflake’s architecture also illustrates how the evolution of cloud computing has transformed data infrastructure—making elasticity, scalability, and cross-cloud portability essential for modern analytics workloads.

4. Databricks SQL: Lakehouse Approach Unifying ML and Analytics

Databricks SQL represents a different architectural philosophy: build your data warehouse directly on your data lake using open formats.

The lakehouse advantage

Databricks SQL runs queries on SQL warehouses (compute clusters) against data stored in Delta Lake format on object storage. This unifies data engineering, ML, and BI workloads on the same data without copying.

Your data scientists access the same tables through Spark. Your ML engineers train models on the same data. Your analysts query through SQL. One copy, multiple engines, all with ACID guarantees through Delta Lake.

The Photon engine provides vectorized execution for fast SQL performance without changing Spark APIs.

The complexity trade-off

Lakehouse architecture offers flexibility at the cost of more decisions:

Table format selection (Delta, Iceberg, Hudi) affects features and compatibility.

Catalog management for metadata and schema evolution.

Partitioning and optimization strategies for query performance.

Compute sizing for SQL warehouses based on workload patterns.

This isn't necessarily bad—it's architectural control. But it's more complex than "provision a Redshift cluster and load data."

When Databricks makes sense over Redshift

Choose Databricks SQL when:

• You're already invested in Spark for data engineering or ML pipelines
• Unifying analytics and ML on shared data reduces duplication and complexity
• Open table formats and vendor portability matter strategically
• Your team has data engineering expertise to manage lakehouse architecture

Databricks solves unified analytics but still requires building custom infrastructure for real-time API serving.

5. ClickHouse®: Columnar OLAP for Real-Time Analytics

ClickHouse® is a columnar database purpose-built for OLAP with emphasis on real-time ingestion and low-latency queries.

Why ClickHouse® differs from Redshift

While Redshift optimizes for batch throughput, ClickHouse® optimizes for query latency and continuous ingestion:

Sparse primary index using physical ordering on disk enables fast lookups on billions of rows without traditional B-tree overhead.

Projections allow multiple physical orderings of the same data for different query patterns without view maintenance overhead, improving query efficiency and flexibility in analytical workloads.

Incremental materialized views update automatically as data arrives, maintaining pre-aggregated results for instant queries.

Efficient compression (often 10-100x) reduces I/O and storage costs dramatically.

The platform consideration

ClickHouse® is an OLAP database, not a complete analytics platform. You need infrastructure for:

• Data ingestion from sources
• Schema and data model management
• Query serving and API layers
• Multi-tenancy and security

Managed ClickHouse® providers (ClickHouse® Cloud, Altinity, Aiven) solve database operations. Tinybird solves the complete platform including ingestion, transformations, and API publication on ClickHouse®.

When ClickHouse® makes sense over Redshift

Choose ClickHouse® (managed or platform) when:

• Sub-second query latency matters more than batch throughput
• Continuous data ingestion is core to your use case
• Event analytics (product analytics, observability, user behavior) is your domain
• Cost efficiency at scale matters—ClickHouse® often delivers 10x better price-performance

ClickHouse® solves fast analytical queries. Tinybird packages it into a complete real-time analytics platform.

6. Trino: Federated SQL Engine for Data Lake Queries

Trino (formerly Presto) isn't a data warehouse—it's a distributed SQL query engine for analytics across multiple data sources without moving data.

The federation value proposition

Trino's architecture enables querying data where it lives:

• Data lakes in S3 (Parquet, ORC, Iceberg, Delta)
• Relational databases (PostgreSQL, MySQL, SQL Server)
• Other warehouses (Redshift, Snowflake, BigQuery)
• NoSQL systems (Cassandra, MongoDB)

One SQL query can join data across these sources without ETL pipelines duplicating data into a central warehouse.

The operational requirements

Trino requires careful architectural planning:

Storage and compute separation is architectural—Trino provides compute, you provide storage (S3, HDFS, etc.).

Table formats matter for performance—Iceberg and Delta enable better pruning and statistics than plain Parquet.

Catalog/metastore management for table metadata and schema evolution.

Memory management and spilling to handle queries exceeding available memory, with performance degradation trade-offs.

When Trino makes sense over Redshift

Choose Trino when:

• Your data is distributed across multiple systems and centralizing it is expensive or impractical
• Data lake querying is your primary use case with open table formats
• You want architectural flexibility to swap compute engines without data migration
• Your workload is exploratory analytics more than production serving

Trino solves federated querying but requires building serving infrastructure for production analytics APIs.

7. Azure Synapse Analytics: Microsoft's MPP Warehouse

Azure Synapse Analytics (formerly SQL Data Warehouse) is Microsoft's answer to Redshift for organizations in the Azure ecosystem.

Dedicated SQL pools architecture

Synapse uses hash-distributed, round-robin, or replicated tables with explicit control over data distribution—similar to Redshift's distribution keys.

You manage data movement in queries, optimizing joins by co-locating data and minimizing shuffles across compute nodes.

Workload management through workload groups and classifiers controls resource allocation—conceptually similar to Redshift's WLM.

When Synapse makes sense

Choose Azure Synapse when:

• Your infrastructure is committed to Azure for compliance or strategic reasons
• You need Microsoft ecosystem integration (Power BI, Azure ML, Purview)
• Your team has SQL Server expertise transferable to Synapse
• Azure-specific features (like integration with Cosmos DB or Fabric) matter

Synapse solves data warehousing in Azure but faces the same real-time analytics challenges as Redshift.

8. Firebolt: Modern Cloud Warehouse with Aggregating Indexes

Firebolt represents newer-generation cloud warehouses emphasizing extreme performance through indexing innovation.

The aggregating index approach

Firebolt's differentiator is aggregating indexes—pre-computed aggregations maintained automatically as base tables change.

Define an index with specific dimensions and metrics. Queries matching that pattern hit the index automatically, delivering sub-second performance on trillion-row tables.

Compute-storage separation with multiple engines accessing shared data enables workload isolation similar to Snowflake.

When Firebolt makes sense

Choose Firebolt when:

• Repetitive aggregation queries dominate your workload
• You need sub-second interactive analytics on massive datasets
• Your query patterns are predictable enough to benefit from aggregating indexes
• You're willing to evaluate newer platforms versus established vendors

Firebolt optimizes specific query patterns exceptionally well but still requires API infrastructure for production serving.

Decision Framework: Choosing Redshift Alternatives

Start with workload requirements

Batch analytics and traditional BI? Redshift, BigQuery, Snowflake, or Synapse all solve this—choose based on cloud preference and operational model.

Real-time analytics with APIs and low latency? Tinybird or ClickHouse®-based platforms solve this purpose-built. Data warehouses require substantial additional infrastructure.

Data lake analytics without ETL? Trino, Databricks SQL, or BigQuery (with external tables) enable querying data in place.

Mixed workloads needing isolation? Snowflake's virtual warehouses or BigQuery's workload management excel here.

Evaluate operational trade-offs

Zero operations priority? BigQuery and Snowflake maximize convenience at cost premium.

Want architectural control? Databricks lakehouse or self-managed ClickHouse® provide flexibility with complexity.

Need multi-cloud portability? Snowflake and Databricks both operate across clouds consistently.

Prefer AWS ecosystem? Consider Redshift Serverless or RA3 before migrating entirely.

Calculate total cost honestly

Include:

Direct platform costs (compute, storage, data transfer).

Engineering time for operations, optimization, and troubleshooting.

Infrastructure costs for supporting systems (ingestion, APIs, caching).

Opportunity cost of engineers on infrastructure versus product features.

A platform costing 2x in subscription might deliver 5x faster with 1/4 the engineering effort—dramatically lower total cost.

Frequently Asked Questions (FAQs)

What's the main reason to leave Redshift?

Common drivers include unpredictable costs during query spikes, inability to serve real-time analytics with acceptable latency, operational overhead of cluster management and tuning, and multi-cloud strategy requiring platform portability. Identify your specific pain point before choosing alternatives.

Is BigQuery cheaper than Redshift?

Depends entirely on query patterns. BigQuery on-demand can be cheaper for variable workloads and expensive for inefficient queries scanning unnecessary data. BigQuery capacity reservations vs. Redshift provisioned clusters have similar economics. Calculate based on your actual query patterns, not list prices.

Can I use Snowflake and avoid vendor lock-in?

Snowflake reduces infrastructure lock-in (no AWS dependency) but creates platform lock-in through proprietary features and data formats. Using standard SQL and open table formats (via external tables or Iceberg support) increases portability but reduces Snowflake-specific optimizations.

Should I migrate to Redshift Serverless instead of leaving entirely?

Redshift Serverless solves specific problems—eliminating cluster sizing, providing auto-scaling, and reducing operational overhead. If your issues are elasticity and ops burden but Redshift's architecture and ecosystem work otherwise, Serverless is worth evaluating before migrating platforms.

What about ClickHouse® vs. traditional data warehouses?

ClickHouse® optimizes for different workloads—real-time ingestion, low-latency queries, event analytics. Traditional warehouses optimize for batch processing, dimensional modeling, complex joins across many tables. Choose based on workload: real-time analytics favors ClickHouse®; traditional BI favors warehouses.

How does Tinybird differ from managed ClickHouse®?

Managed ClickHouse® (ClickHouse® Cloud, Altinity, Aiven) solves database operations. Tinybird solves the complete analytics platform—database plus data ingestion, transformation pipelines, API publication, and serving infrastructure. Choose managed ClickHouse® for database control; choose Tinybird for fastest time to production analytics.

Can I query Redshift and other sources together?

Yes, with federated query engines like Trino or through Redshift Spectrum (for S3), federated queries (for RDS), or Redshift Data Sharing. Performance and cost vary significantly by approach. Federation works well for exploration; materializing data works better for production analytics.

Most teams evaluating Redshift alternatives are asking the wrong question.

The question isn't "which data warehouse is better than Redshift?" The question is "what workload am I actually trying to solve?"

If your requirement is traditional batch analytics and BI, alternatives like BigQuery, Snowflake, or Databricks SQL offer different trade-offs around operations, elasticity, and cloud portability. Even Redshift Serverless might solve your specific pain points.

If your requirement is real-time analytics with API serving and sub-second latency, data warehouses—Redshift or alternatives—weren't designed for that workload. Tinybird solves this purpose-built with streaming ingestion, fast columnar queries, and instant API publication.

For unified data lake and ML workflows, Databricks lakehouse approach makes sense. For querying data across multiple systems without ETL, Trino federated queries work well. For multi-cloud enterprise BI, Snowflake delivers convenience at a premium.

The right choice isn't the newest or cheapest data warehouse. It's the platform that matches your workload requirements with the least total cost and operational burden.

Choose based on what you're actually trying to build, not which vendor has the best marketing.