6 Amazon Aurora alternatives for high-volume ingestion (2026 guide)

Amazon Aurora has been the go-to choice for scaling PostgreSQL workloads on AWS. But as your application grows and data ingestion volumes reach certain thresholds, you start to see cracks in the architecture.

For engineering teams managing high-velocity event streams, complex telemetry data, or heavy analytical aggregations, Aurora's I/O charges become unpredictable. And as your dataset grows into the multi-terabyte range, I/O-amplifying maintenance operations compound costs in ways that can feel like a pricing cliff. AI workloads sharpen the problem. Agent-driven requests collapse the old line between transactional and analytical queries, so a single row-store engine now must handle both unpredictable OLTP bursts and heavy aggregations simultaneously.

This guide helps you match your specific workload shape to the right managed database alternative. Depending on your constraints, you might stay on native AWS infrastructure, move to an Aurora-class competitor, or adopt a unified transactional and analytical stack.

The focus is on cost predictability, raw write throughput, and the architectural realities of scaling.

TL;DR #

• Aurora breaks down for high-ingest workloads due to unpredictable I/O costs and a single-writer bottleneck. Like every row-store Postgres, it also suffers when analytics queries compete with OLTP, but Aurora's per-I/O pricing makes that contention more expensive than on fixed-cost alternatives.
• If you have a narrow, well-defined read pattern and can redesign your data model, choose DynamoDB for AWS-native, horizontally scaled writes.
• If you must stay on AWS-native services and want predictable bills, choose RDS for PostgreSQL (io2).
• If you're willing to move to GCP for an Aurora-like experience with better analytics acceleration, choose AlloyDB.
• For high-performance OLTP and heavy analytics, choose ClickHouse Managed Postgres, available on AWS and GCP: local NVMe-backed Postgres handles transactions, while ClickPipes feeds a separate ClickHouse service for OLAP.

Why teams outgrow Amazon Aurora and how to choose an alternative (5 criteria) #

Outgrowing Aurora rarely means your database is failing completely. Most of the time, it's about architectural friction that builds gradually.

The system works great until your ingestion rates, storage volumes, or querying patterns trigger fundamental bottlenecks in Aurora's custom storage layer. You need to understand exactly how and why Aurora breaks under pressure before you can evaluate alternatives properly.

How Amazon Aurora I/O pricing creates unpredictable costs #

The most common reason teams migrate off Aurora is the billing shock from storage I/O operations.

Aurora Standard pricing uncouples storage volume from storage performance, charging $0.20 per million I/O requests. For read-heavy applications with predictable cache hit ratios, this adds negligible cost.

For high-volume ingestion workloads, heavy indexing, or analytical queries that sweep through tables sequentially, these per-request I/O charges generate unpredictable and often staggering monthly bills.

AWS tried to fix this with Aurora I/O-Optimized. This tier eliminates per-request I/O charges entirely, which sounds like a reprieve for write-heavy applications. The tradeoff is that I/O-Optimized requires significantly higher baseline compute and storage costs.

This creates a tricky cost-model situation. Amazon says I/O-Optimized only saves money when your I/O spend consistently exceeds 25%of your total Aurora bill. So your team has to predict whether your workload's I/O patterns will stay aggressive enough to justify the elevated fixed baseline.

The cost picture changes shape past 10 TB. At smaller volumes, per-I/O charges are an annoyance line item. At 10 terabytes and beyond, the operations that scale with data size all compound the same per-I/O cost, including snapshot creation, cross-region replica provisioning, routine vacuum and analyze passes, and point-in-time restores. Each one touches storage proportional to the dataset size. What teams report is a discontinuity where infrastructure spend jumps to a different order of magnitude once data crosses that threshold. The pricing cliff isn't a fixed dollar amount, but the point where I/O-amplifying maintenance work overtakes serving traffic in your bill.

Evaluation criterion: Cost predictability. You need to distinguish between solutions offering fixed, provisioned-resource pricing and variable, usage-based models that penalize burst ingestion.

Why Amazon Aurora hits a single-writer bottleneck on bulk inserts #

Aurora achieves its durability by replicating data six ways across three distinct Availability Zones. To acknowledge a write as durable, the system needs a quorum consensus of four out of six storage nodes.

This replication architecture provides exceptional fault tolerance, but it creates a bottleneck during massive bulk insert operations.

In a standard Aurora cluster, one primary writer instance accepts write traffic and performs all data modifications to the shared cluster volume. Under high-concurrency ingestion, especially with many small autocommit inserts, the writer can spend increasing amounts of time waiting for the Aurora storage subsystem to acknowledge transaction commits. In Aurora PostgreSQL, this pressure shows up as elevated IO:XactSync waits.

Unless you carefully batch transactions and group write operations into single commits, the quorum replication physics will throttle bulk-insert throughput, no matter how large you scale the primary compute node.

Evaluation criterion: Write throughput & bulk-insert behavior. Any viable alternative needs to handle high-velocity event streams and massive batch operations without artificially throttling concurrent connections.

How Amazon Aurora failover affects application availability #

High availability in a distributed database is only as valuable as your application's ability to survive the failover event.

When an Aurora primary node fails, the cluster promotes an existing read replica to the primary role. The underlying custom storage layer recovers rapidly, but the client application experience depends entirely on DNS propagation.

During an Aurora failover, applications routinely experience approximately 10 seconds of complete disruption as client connections wait for new routing to propagate. Adding an intermediate connection pooling layer like Amazon RDS Proxy can reduce this to roughly two seconds, but you're adding architectural complexity.

There's another catch. During a failover, the promoted reader keeps its page cache, but the original failed primary boots back up as a reader with a cold cache. That causes secondary performance degradation.

Evaluation criterion: High-availability behavior. Compare alternatives on their specific failover mechanics. How do they handle connection survival? What's the actual impact on application availability during infrastructure loss?

Why running analytics on Aurora OLTP degrades performance #

A significant portion of Aurora’s performance degradation is self-inflicted. Teams run complex analytical queries directly against their primary transactional database. Heavy aggregations, multi-table joins, and window functions on a row-oriented storage engine create massive resource contention.

Analytical queries rely on sequential scans that aggressively churn the shared buffer cache. They directly evict hot transactional data that your application needs to serve user requests.

Running analytical queries with heavy parallelism also increases lock contention inside the database engine, specifically within the lock manager and buffer manager. This directly degrades concurrent transactional throughput.

Throwing more compute at the problem via Aurora Serverless v2 or sharding data via Aurora Limitless Database doesn't solve the fundamental physical problem. You're running aggregations on a row-store.

Evaluation criterion: Workload shape fit. Does your application need a pure transactional database, or does the workload demand an architecture that can support mixed analytical and transactional queries without compromising latency?

Which PostgreSQL extensions are limited on Amazon Aurora #

PostgreSQL owes much of its dominance to its vast, open-source extension ecosystem. But because Aurora strips out and replaces the native PostgreSQL storage layer with a proprietary distributed system, Aurora can't safely support many low-level extensions.

This creates a walled garden where database administrators can't implement specialized extensions or access superuser-level configuration parameters for custom tuning.

Teams using complex spatial analytics, vector search components, or specialized background workers often find themselves blocked by Aurora's locked-down environment.

Evaluation criterion: Extension and configuration freedom. How compatible is the alternative with the broader open-source extension ecosystem? What level of administrative control do you actually get?

Note: High-volume ingestion funnels entirely through a single compute node, forcing sequential durability acknowledgments that throttle raw throughput.

How to read this guide #

Two questions determine which alternative fits:

1. Can you redesign your data model away from relational SQL? If yes, you have a narrow, well-defined read pattern, you don't need analytics, and you want to stay on AWS, DynamoDB fits. If no, you need a Postgres-compatible alternative.
2. Is your workload purely transactional, or also append-only and analytical? Pure OLTP fits standard managed Postgres alternatives. Event-heavy or analytics-heavy workloads are better served by Postgres-for-OLTP paired with a columnar engine, not by another OLTP database.

The comparison matrix at the end maps each alternative against both questions. The walkthrough below runs from AWS-native non-Postgres to a Postgres-plus-columnar architecture.

Top Amazon Aurora alternatives for high-ingest workloads (2026) #

The following managed database solutions represent the most viable engineering paths away from Aurora. They're ordered by their architectural difference from the standard Aurora model.

Each option forces a specific tradeoff: data-model rewrite (DynamoDB), slower failover and lost Aurora operational features (RDS), cold-start latency and a bespoke serverless storage engine (Neon), lost AWS-native integration (Crunchy Bridge), cross-cloud migration (AlloyDB), or the need to operate two best-of-breed services together: ClickHouse Managed Postgres and a separately provisioned ClickHouse service in ClickHouse Cloud.

The right pick depends on which tradeoff is acceptable for your workload. For high-ingest workloads with analytics requirements, the two-service architecture is usually the cleanest fit because it combines high-performance OLTP on Postgres with isolated analytical compute in ClickHouse.

Amazon DynamoDB as an Aurora alternative for key-value write throughput on AWS #

Best for #

Teams that can redesign their data model around key-value access patterns, have the engineering bandwidth to rewrite the data access layer, and want to stay on AWS.

Overview #

Amazon DynamoDB is AWS's native, fully managed NoSQL database service, operating entirely outside the relational paradigm. Because DynamoDB partitions data horizontally across storage nodes based on a partition key, there's no single primary writer to overwhelm. As ingestion scales, the underlying infrastructure automatically splits partitions to accommodate the load.

Key differences vs. Amazon Aurora #

DynamoDB eliminates the single-writer bottleneck and avoids Aurora's per-I/O storage model. Billing is highly predictable, calculated primarily in Write Capacity Units and Read Capacity Units. You can provision throughput in advance or use on-demand capacity, which bills at a flat, predictable rate per million requests. There are also strict physical limits. Individual items are capped at 400KB. At the partition level, a single logical partition can only support a maximum of 1,000 write capacity units per second. Amazon has introduced adaptive capacity to dynamically adjust to hot partitions, but a poorly designed partition key will still result in aggressive request throttling under high-volume ingestion.

Pros and cons #

Gain: Massive write scalability via partition-key horizontal scaling, predictable per-request billing when access patterns match the partition key design. Deviating from anticipated query patterns forces Global Secondary Index fan-out, which can generate its own cost surprises.

Give up: SQL, joins, ad-hoc analytical queries, and foreign key constraints. Adopting DynamoDB requires a total teardown and redesign of your data model. Data must be duplicated and heavily denormalized to support specific access patterns.

Pricing and migration #

WCU/RCU pricing model, on-demand or provisioned throughput.

Migration difficulty: Complex. The data-model rewrite typically requires a multi-quarter engineering effort to redesign the application's data access layer.

Amazon RDS for PostgreSQL as a cost-predictable Aurora alternative #

Best for #

Cost-sensitive teams that want standard PostgreSQL on AWS with fixed, provisioned bills, and can accept slower failover and manual storage scaling in exchange for that predictability.

Overview #

Amazon RDS for PostgreSQL is the standard, community-compatible managed database offering on AWS. It uses traditional block storage volumes rather than a proprietary distributed storage engine. Transitioning to RDS directly improves the Aurora variable cost problem by returning to a predictable, provisioned storage model.

Key differences vs. Amazon Aurora #

When paired with high-performance storage configurations, specifically io2 Block Express volumes, standard RDS becomes a formidable ingestion engine. The io2 Block Express architecture can deliver up to 256,000 provisioned IOPS with sub-millisecond latency and throughput scaling up to 4,000 MiB/s on supported instance types. Independent benchmarks from AWS indicate that io2 Block Express volumes can deliver over twice the transactions per minute and four times faster I/O response times compared to standard general-purpose storage. Because you pay a flat provisioned rate for this performance, heavy bulk inserts don't generate punitive variable billing charges.

One caveat: io2 Block Express remains network-attached, so every commit fsync crosses the network. The PostgresBench result cited here tested RDS on gp3, not io2, so it cannot show how much local NVMe outperforms io2. AWS's io2 figures above are the relevant evidence for that configuration.

Pros and cons #

Gain: Predictable provisioned-rate pricing, competitive performance via io2 Block Express, escape from Aurora's variable I/O charges.

Give up: Aurora's automated operational conveniences. RDS doesn't offer sub-second read replica replication lag, and the storage volume doesn't automatically grow and shrink with granular precision. Failover mechanics rely on traditional multi-AZ instance promotion, typically requiring between 60 and 120 seconds to fully promote the standby and redirect traffic, which is significantly slower than Aurora's DNS propagation window. RDS solves the variable I/O cost problem, but it is still a row-store. Aggregations on large datasets still evict transactional pages from cache, the same analytics-on-OLTP contention covered in the Aurora section above.

Pricing and migration #

Flat provisioned rate plus IOPS tier (io2 Block Express for the high-IOPS configurations described above).

Migration difficulty: Easy. RDS is the same engine family as Aurora, so logical replication moves cleanly, and AWS tooling supports zero-downtime cutover.

Neon as a serverless Postgres alternative to Aurora (scale to zero) #

Best for #

Serverless application stacks using frameworks like Vercel, massive fleets of isolated testing environments, and multi-tenant architectures where thousands of idle databases must exist without accumulating minimum baseline compute charges.

Overview #

Neon is an open-source, serverless PostgreSQL alternative designed from the ground up with completely decoupled compute and storage. Neon replaces the standard PostgreSQL storage layer with a bespoke engine consisting of Pageservers for data storage and Safekeepers for managing the write-ahead log stream. This ensures durability without requiring continuously running compute nodes. The Pageserver layer is network-attached storage. Like Aurora's distributed storage and unlike NVMe-backed Postgres, every page fault crosses the network. This is the right tradeoff for elastic scale-to-zero, but it adds latency under sustained write pressure.

Key differences vs. Amazon Aurora #

Neon solves the heavy baseline cost problem built into Aurora Serverless v2. While Aurora Serverless v2 claims instant scaling, it requires maintaining a minimum baseline of Aurora Capacity Units, so you're billed even when the database is completely idle. Neon offers better cost efficiency for intermittent or variable workloads by scaling compute capacity to zero during inactivity. Neon also relies heavily on integrated connection pooling via PgBouncer.

Pros and cons #

Gain: Potential scale-to-zero economics for intermittent or variable workloads. Instant database branching, useful for some development workflows.

Give up: Cold-start latency. By default, Neon spins down compute instances after five minutes of inactivity. When a new connection request arrives, activating the compute environment from an idle state requires time to initialize before the database can serve the query. This additional start up latency can often be unacceptable and require disabling scale-to-zero. Neon's Pageserver is network-attached storage. That storage path shows under sustained write load. In the roughly 500 GB PostgresBench test, Neon averaged 7,693 TPS versus 26,105 for local-NVMe ClickHouse Managed Postgres.

Pricing and migration #

Usage-based pricing by compute hours, storage volume, and WAL stream.

Migration difficulty: Easy. Standard Postgres logical replication moves data cleanly into Neon's storage engine.

Crunchy Bridge as a managed Postgres alternative with full extension support #

Best for #

Engineers looking for a vanilla PostgreSQL, with flexible system tuning capabilities.

Overview #

Crunchy Bridge is a fully managed PostgreSQL service. It can run across multiple cloud providers, including AWS. Crunchy Bridge runs on cloud-provider block storage with configurable IOPS, not collocated NVMe. At roughly 500 GB, Crunchy Bridge averaged 10,954 TPS, close to Aurora I/O-Optimized at 11,011 but below ClickHouse Managed Postgres at 26,105. At roughly 100 GB on 4 vCPUs and 16 GB RAM, Crunchy Bridge was slightly faster—6,320 TPS versus 6,013—showing why the exact configuration matters.

Key differences vs. Amazon Aurora #

Crunchy Bridge removes the Aurora extension walled garden. Since it runs standard, unmodified PostgreSQL rather than a proprietary fork, it delivers increased configuration freedom. Database administrators get superuser access, allowing unrestricted use of the open-source extension ecosystem, including advanced spatial analytics processing tools that AWS either restricts or version-locks. Crunchy Bridge also uses a stronger high-availability architecture than standard RDS. Instead of relying on DNS propagation for failover,, it maintains a completely hidden hot standby instance that continuously receives the same write stream as the primary, so promotion is near-instant.

Pros and cons #

Gain: Extension freedom, superuser access, near-instant failover via hot-standby HA.

Give up: Crunchy Bridge is a pure managed Postgres. There is no native analytics offload path, so analytics-heavy workloads still require you to build and maintain an external pipeline to a separate warehouse or columnar engine. For teams whose pain is analytics-on-OLTP contention, Crunchy doesn't address that problem out of the box.

Pricing and migration #

Predictable instance-size-based pricing.

Migration difficulty: Easy. Crunchy Bridge supports standard Postgres logical replication for zero-downtime cutover.

Google AlloyDB as the GCP-native alternative to Amazon Aurora #

Best for #

Organizations actively executing multi-cloud strategies, teams already planning a broader migration to GCP, or environments requiring an Aurora-like distributed storage experience completely detached from Amazon's specific I/O pricing.

Overview #

Google AlloyDB is GCP's fully managed, PostgreSQL-compatible database featuring a highly customized distributed storage engine tightly integrated with a proprietary columnar acceleration layer. The storage layer is Google Colossus, a distributed network-attached system. Like Aurora's storage, it's a sophisticated distributed design, but every I/O still crosses the network rather than hitting NVMe drives collocated with compute.

Key differences vs. Amazon Aurora #

AlloyDB improves upon what Aurora built. It improves Aurora's analytical query limitations by implementing an intelligent, memory-resident columnar engine that identifies heavily queried analytical data, organizes it into a columnar format in memory, and accelerates aggregate queries without requiring any SQL syntax changes. Official Google Cloud benchmarks emphasize this columnar caching ability, demonstrating substantial performance improvements on complex analytical queries compared to standard row-oriented PostgreSQL. Google also engineered the underlying storage layer to simplify and accelerate standard PostgreSQL vacuuming processes, preventing transaction ID wraparound issues that can plague massive ingestion workloads.

Pros and cons #

Gain: In-engine columnar acceleration for analytical queries, no per-I/O fees, accelerated vacuuming.

Give up: AlloyDB's columnar engine is an HTAP approach, where both transactional and analytical work run on the same engine. That delivers a single-system experience, but analytical and transactional workloads still contend on the same engine, and although read pool nodes can absorb some analytical reads, the columnar engine still shares the write-path instance. The contention mechanism differs from Aurora's row-store cache eviction, but the outcome is similar at scale. Heavy aggregations can pressure the resources available for OLTP. Decoupled OLTP + OLAP stacks avoid that contention by separating the engines entirely. The other primary tradeoff is infrastructural. Adopting AlloyDB requires physically migrating data storage and application compute out of AWS and into GCP, with significant cross-cloud latency constraints and egress charges if you split the application and database across clouds. If AlloyDB data needs to flow downstream to BigQuery,Google offers Datastream as a managed CDC path. For other downstream targets, the pipeline runs on Google Dataflow, a separate managed service with its own configuration and bill.

Pricing and migration #

GCP cloud consumption model, no per-I/O fees.

Migration difficulty: Moderate. Migration requires cross-cloud networking and configuration via Google's Database Migration Service.

ClickHouse Managed Postgres for high-performance OLTP plus OLAP offload #

Best for high-ingest OLTP plus OLAP offload #

Engineering teams running out of operational runway on traditional PostgreSQL due to high-volume event ingestion, user-facing analytics requirements, and heavy aggregation queries that continuously exhaust Aurora CPU limits and storage budgets. For teams whose Aurora deployments are hitting I/O cost unpredictability, single-writer write limits, or analytics-on-OLTP contention in production, ClickHouse Managed Postgres is the recommended path for high-performance OLTP with OLAP offload. It also fits AI-native applications, where agent-driven traffic generates unpredictable bursts that hit transactional and analytical paths at once and need both served from one data foundation.

Overview #

ClickHouse Managed Postgres, available on AWS and GCP, takes a different approach. Instead of forcing a single database engine to handle conflicting workload shapes, it keeps Postgres on local NVMe for high-performance OLTP and pairs it with a separately provisioned ClickHouse service for analytical aggregation. Both services are managed through one ClickHouse Cloud control plane, with independent compute and storage.

The Postgres side runs on local NVMe storage collocated with compute, delivering microsecond disk latency rather than the milliseconds you get from network-attached storage like Aurora's distributed storage layer or RDS's EBS volumes.

PostgresBench, ClickHouse's TPC-B-style benchmark, shows how those storage paths behave under sustained writes. At roughly 500 GB, ClickHouse Managed Postgres on local NVMe averaged 26,105 TPS; Aurora I/O-Optimized averaged 11,011; Crunchy Bridge averaged 10,954; and Neon averaged 7,693. The test used 16 vCPUs, HA disabled, provider-default settings, and three 600-second runs. Aurora had 128 GB RAM while the others had 64 GB, and pricing was not normalized.

Configuration still matters. At roughly 100 GB on 4 vCPUs and 16 GB RAM, Crunchy Bridge averaged 6,320 TPS versus 6,013 for ClickHouse Managed Postgres. Benchmark the workload and HA setup you plan to run.

ClickHouse Managed Postgres runs standard PostgreSQL with standard tooling. You get full superuser access for the default postgres user and more than 90 extensions, including PostGIS, pgvector, and pg_cron. That preserves the spatial, vector, background-worker, and tuning use cases that Aurora's managed constraints can block.

The pg_clickhouse extension is open source, and the Postgres CDC capability in ClickPipes is built on open-source PeerDB. The underlying integration foundation is open; the managed control plane and operations are part of ClickHouse Cloud.

On the operations side, the service includes configurable HA with up to two standbys, automatic failover, point-in-time recovery, database branching, dedicated read replicas, private networking on eligible tiers, Query Insights, Prometheus-compatible metrics, and lifecycle management through OpenAPI.

Note: When you split the workloads, ClickHouse Managed Postgres handles high-performance OLTP while a separately provisioned ClickHouse service handles analytical scans on its own compute and storage. ClickPipes keeps selected tables in the analytical service eventually consistent with Postgres.

Key differences vs. Amazon Aurora #

This architecture solves Aurora's dual problems of unpredictable I/O costs and analytical performance degradation by physically separating the workloads. Massive-volume data ingestion and heavy sequential analytical queries are fundamentally hostile to row-oriented transactional databases. By pairing a standard managed PostgreSQL instance with a purpose-built columnar ClickHouse engine, you stop burdening your transactional system with workloads it wasn’t designed for.

• Managed CDC: ClickPipes replicates selected tables into a separately provisioned ClickHouse service with seconds-level freshness. You choose the destination service and tables; because replication is asynchronous, the analytical copy is eventually consistent. Logical decoding, WAL retention, initial snapshots, and network transfer still use some source Postgres resources.
• Postgres query access: pg_clickhouse lets applications query ClickHouse tables through the PostgreSQL interface after you configure the foreign server and tables. Supported joins, filters, aggregations, and functions push down to ClickHouse; unsupported work runs in Postgres.
• One platform, separate resources: ClickHouse Managed Postgres and the ClickHouse service share one ClickHouse Cloud control plane and appear on a consolidated bill. Each service uses independently metered compute and storage, so analytics can scale without consuming transactional compute.

Pros #

In benchmark, pg_clickhouse fully pushed 14 of 22 TPC-H queries into ClickHouse. Those 14 ran more than 60x faster than standard PostgreSQL because the analytical work executed on the ClickHouse service instead of the Postgres primary. Queries with unsupported operations can still run partly or entirely in Postgres.

ClickPipes reduces pipeline work by managing the initial load and CDC, while you still choose the destination service, tables, and synchronization settings. The result is Postgres sized for high-performance OLTP and ClickHouse sized independently for analytics.

Common workloads effectively offloaded from Aurora to ClickHouse:

• Interactive user dashboards: Applications providing real-time, user-facing analytics require sub-second query latency. Serving these complex aggregation dashboards directly from an Aurora primary inevitably spikes compute usage. Offloading these queries to ClickHouse keeps the transactional database responsive to user requests.
• Real-time event funnel analysis: Ingesting high-velocity user behavior data, including clicks, page views, and conversion metrics, generates massive write volume. ClickHouse's columnar storage compresses this data exceptionally well, allowing product teams to query behavioral funnels in real time without generating punitive I/O charges on a transactional datastore.
• Log and telemetry analytics: Storing high-cardinality observability data, infrastructure metrics, and application logs quickly exhausts standard relational storage capacity. Moving this time-series-adjacent data to ClickHouse allows for massive scale retention and rapid forensic querying during operational incidents.

Cons #

The tradeoff is a two-service architecture. ClickPipes is asynchronous, so dashboards read an eventually consistent copy with seconds-level freshness; long-running transactions and replication backlog can increase lag. Logical decoding and WAL retention also consume some source resources, and pg_clickhouse only pushes supported operations to ClickHouse. For most dashboards and reporting, that is a practical tradeoff. Workloads that require every analytical query to reflect the latest committed transaction need a different path.

Pricing model #

ClickHouse Managed Postgres is available on AWS and GCP, with usage-based pricing by VM configuration and tier. More than 50 NVMe-backed configurations range from 1 vCPU / 8 GB RAM / 59 GB NVMe to 96 vCPUs / 768 GB RAM / 60 TB NVMe. Read replicas and any separately provisioned ClickHouse service are billed independently.

Migration from Amazon Aurora (logical replication + CDC) #

Migration difficulty: Workload-dependent. ClickPipes can copy the initial dataset from Aurora and keep changes flowing during cutover, reducing downtime. You still need to validate schema compatibility, coordinate unsupported DDL changes, monitor replication lag, and test the application cutover. Automatic schema transfer requires an empty target and is unavailable through an SSH-tunneled source.

Who has already moved off Aurora #

Several companies are running multi-terabyte, mission-critical production workloads on ClickHouse Managed Postgres today. Customers have migrated from RDS, Aurora, CloudSQL, Neon, and PlanetScale Postgres across cybersecurity, fintech, retail, real estate, and social media.

The two most relevant cases for this guide are direct Aurora migrations.

Sterling Labs migrated from Aurora and now runs roughly 8.5 TB of hot data on ClickHouse Managed Postgres. It highlights the low-latency performance of local NVMe storage.

DoControl moved multiple multi-terabyte workloads from RDS and Aurora to ClickHouse Managed Postgres. It cites reliability and price-performance as key requirements and highlights the ability to move Postgres data into ClickHouse with less operational complexity.

Which Amazon Aurora alternative fits your workload? (comparison matrix) #

Selecting the right path means mapping your application's fundamental constraints against the physical realities of the underlying database architecture.

Reading the matrix: ClickHouse Managed Postgres is the recommended path when high-volume writes and analytics-on-OLTP contention are both driving the move. Local NVMe supports high-performance OLTP; a separately provisioned ClickHouse service runs analytics on independent compute and storage. You gain workload isolation, but add asynchronous replication and a separate resource meter. AlloyDB keeps analytics inside an HTAP engine; the other four options are row-store-only.

Procurement scorecard guidance: Use this matrix for vendor evaluation conversations. If your primary pain point is unpredictable billing during steady-state operations, filter strictly for options with high cost predictability. If analytical dashboards are degrading transactional performance, rule out any purely row-oriented architectures and prioritize the columnar analytical support paths.

How to migrate off Amazon Aurora (logical replication playbook) #

Migrating away from Aurora requires precise execution because AWS blocks physical, file-level backup restorations to non-Aurora database targets. You can't detach an Aurora storage volume and attach it to standard PostgreSQL. Migration strictly requires logical replication.

Pre-migration diagnostics: what to measure on Aurora #

Before you move any data, audit your existing environment using the pg_stat_statements extension.

Determine whether your current Aurora I/O costs stem from aggressive write amplification, chatty and unoptimized read queries, or massive sequential aggregations. Moving a poorly optimized workload to a new platform without understanding the root cause of the database strain will just transfer the bottleneck.

Logical replication constraints when migrating from Aurora #

Moving the physical data requires streaming the database state via logical replication. Tools like AWS Database Migration Service, open-source Debezium, or ClickPipes are standard options, but each has specific limitations:

• AWS Database Migration Service (DMS): While heavily used, DMS can't support CDC extraction if routed through an RDS Proxy source. DMS also fails to process two-phase commits, and replication will yield highly unpredictable data integrity issues if primary key constraints on the source don't perfectly match the target.
• Debezium and logical replication slots: Using native PostgreSQL logical replication requires establishing replication slots. This approach carries risk for high-volume ingestion workloads. If the receiving consumer goes offline or experiences latency, the primary database can't delete its write-ahead log files. The database will retain all WAL data until the slot reconnects. In massive ingestion environments, an orphaned replication slot will cause unbounded disk growth, rapidly resulting in a full-disk database outage.

Migration risk mitigation checklist #

• Always benchmark massive bulk-insert scripts on the destination target before beginning a production cutover.
• Make sure your application's connection pooling logic is fully tested against the new architecture.
• Pay special attention to differences in connection handling between Amazon RDS Proxy and standard PgBouncer implementations.

Conclusion: choosing the right Amazon Aurora alternative for high-ingest workloads #

Outgrowing Aurora is a common and predictable milestone for highly successful, data-intensive engineering organizations.

Forcing a single distributed row-store architecture to handle unbounded event ingestion and complex analytical dashboards leads to escalating costs and unpredictable latency. Throwing increasingly expensive instance sizes at Aurora I/O limits won’t solve the underlying problem. You need a different architectural approach.

If unpredictable analytical queries and heavy event ingestion are driving up your managed database bills, continuing to scale vertically isn't the answer.

Use ClickHouse Managed Postgres for high-performance OLTP on local NVMe and a direct path to ClickHouse analytics. Available on AWS and GCP, it lets you keep Postgres as the transactional system of record and add a separately provisioned ClickHouse service as analytics grow. Start with PostgreSQL, scale with ClickHouse.