The 5 best AWS RDS alternatives in 2026 (pricing, performance, and migration)

AWS RDS has been the default managed relational database for engineering teams for years, but that's changing.

As data demands grow, engineering and data leaders are actively looking for replacements. They want predictable billing and relief from performance bottlenecks caused by mixing analytical and transactional workloads on the same database.

This guide looks at five credible RDS alternatives. We'll cover cost predictability, write performance, operational simplicity, and vendor lock-in. Understanding these architectural differences will help you find the right path forward when RDS no longer meets your requirements.

TL;DR: best AWS RDS alternatives by use case #

Teams leave RDS mainly due to unpredictable IOPS/storage costs and OLTP performance degradation under analytics workloads. This list ranks alternatives by cost predictability, write performance, operational simplicity, and lock-in.

• Best for high-performance OLTP with a direct path to OLAP: ClickHouse Managed Postgres
• Best for staying within AWS: Amazon Aurora
• Best for serverless developer workflows at lower scale: Neon
• Best for high-performance Postgres on GCP: AlloyDB
• Best for enterprise open-source: Crunchy Bridge

Why teams are leaving AWS RDS in 2026 #

Many engineering teams hit a point with AWS RDS where costs scale non-linearly, and performance degrades unpredictably. The root cause is architectural limitations and billing friction.

High and unpredictable costs catch teams off guard. Provisioned IOPS and storage scaling lead to ongoing billing surprises. Moving a write-heavy workload from gp3 to provisioned IOPS storage like io2 Block Express can multiply your monthly storage bill significantly, and Multi-AZ deployments immediately double your compute and storage costs, making high availability expensive as you grow.

Performance degrades under mixed workloads. Running heavy analytical queries, dashboards, and reporting on the same OLTP database slows down critical application transactions. Postgres's traditional row-store architecture struggles with heavy aggregations, leading to CPU spikes and memory evictions that affect your user-facing applications.

Vendor lock-in restricts long-term flexibility. Deep entanglement with AWS's proprietary ecosystem makes multi-cloud or hybrid deployments operationally painful.

RDS enforces inflexible scaling limitations. Vertical scaling requires taking the database offline for a reboot, though Multi-AZ deployments can use failover to minimize downtime. Read replica sprawl can offload select statements, but it introduces replication lag and yields diminishing returns for heavy write or analytical needs.

When AWS RDS is still the right choice #

RDS works well for small to medium workloads with simple, predictable query patterns. It also makes sense for teams locked into multi-year AWS Enterprise Discount Programs (EDPs) where spend minimums dictate infrastructure choices, or for organizations that lack the engineering bandwidth to manage a migration and are prioritizing the status quo over cost efficiency.

How to evaluate an AWS RDS alternative #

Choosing a replacement means moving past surface-level instance pricing and examining the architectural realities of your workload.

How compatible is it with PostgreSQL? #

Does the alternative offer drop-in compatibility, or will you need to rewrite application logic and queries? Pure Postgres solutions let you keep your existing ORMs, drivers, and extensions. Distributed SQL engines might require schema adaptations to handle distributed transactions safely.

Can it handle high write volumes and analytics workloads without slowing OLTP? #

Look at whether the architecture can handle bulk inserts without throttling. More importantly, does it provide a dedicated path for analytical queries?

Forcing analytical aggregations through a transactional engine will recreate the exact performance problems you're trying to escape.

How hard is migration from AWS RDS? #

A viable alternative needs a clear logical-replication path that minimizes cutover downtime and keeps the target current while you validate it and prepare the cutover. If migrating requires extensive downtime or the need to stand up complex data engineering pipelines just to keep a read replica warm, the operational overhead outweighs the benefits.

How predictable is pricing vs. AWS RDS? #

A surface-level comparison of compute instance prices is misleading. Compute is just the baseline.

True RDS costs hide in storage tiers, provisioned IOPS, and Multi-AZ deployments. Network and egress penalties for cross-AZ data transfer and backup retention further bloat the bill. Compare this à la carte pricing to alternatives that offer predictable flat-rate storage, consumption-based serverless models that scale to zero, or unified billing from modern data stack vendors.

What is the total cost of ownership (TCO) over time? #

Offloading analytical queries to a dedicated engine can cost less than continually upsizing a monolithic RDS instance when reporting and aggregation are what drive the extra capacity. Right-sizing a primary Postgres database for transactional workloads and using a purpose-built analytical layer lowers TCO while improving query performance.

AWS RDS alternatives compared (features, pricing, and migration) #

Reading the matrix: ClickHouse Managed Postgres pairs local-NVMe PostgreSQL for OLTP with a direct path to a separately provisioned ClickHouse service for OLAP. The services have independent compute, storage, and metering, with one ClickHouse Cloud control plane and a consolidated bill. AlloyDB has the columnar layer but pays for it with cross-cloud migration and HTAP contention. Aurora and Crunchy Bridge are row-store-only managed Postgres. Neon optimizes for serverless and developer workflows rather than sustained production scale.

Detailed reviews of the best AWS RDS alternatives #

Each option forces a specific tradeoff: AWS lock-in (Aurora), cold-start latency on network-attached storage (Neon), cross-cloud migration (AlloyDB), or cloud-provider block storage instead of collocated NVMe (Crunchy Bridge). For teams whose bottleneck is mixed-workload contention, ClickHouse Cloud offers a two-engine, two-service model: ClickHouse Managed Postgres for high-performance OLTP and a separately provisioned ClickHouse service for OLAP.

ClickHouse Managed Postgres (best for high-performance OLTP with a direct path to OLAP) #

Best for high-performance OLTP with a direct path to OLAP #

Teams that want PostgreSQL compatibility and local-NVMe performance, with a separately provisioned ClickHouse service when analytics needs dedicated compute.

Overview #

ClickHouse Managed Postgres is a fully managed PostgreSQL service within ClickHouse Cloud, available for production workloads on AWS and GCP and backed by local NVMe storage. When analytics needs dedicated compute, teams can add a separately provisioned ClickHouse service in the same platform. The services have independent compute and storage and are metered separately, while sharing the ClickHouse Cloud control plane and a consolidated bill.

Mixed transactional and analytical workloads can create CPU, memory, I/O, and cache contention on the primary. Postgres for OLTP and ClickHouse for OLAP keeps low-latency transactions in ClickHouse Managed Postgres, while selected analytical data can be replicated to the separately provisioned ClickHouse service for reporting, dashboards, and aggregations. The replicated copy is eventually consistent. This architecture is particularly relevant for AI-native applications, where agent-driven queries generate unpredictable bursts across both transactional and analytical data.

The Postgres service runs on local NVMe storage physically collocated with compute. Local disk access provides microsecond-level latency and avoids a network round trip to network-attached storage. The pinned PostgresBench snapshot used an approximately 500 GB TPC-B-like pgbench workload with 256 clients, 16 threads, prepared queries, 600-second runs, three runs per service, 16 vCPUs, HA disabled, provider-default settings, and us-east-2.

ClickHouse Managed Postgres with local NVMe and 64 GB RAM averaged 26,104.81 TPS. AWS RDS with gp3, 16,000 IOPS, and 64 GB RAM averaged 4,727.11 TPS; Aurora I/O-Optimized with 128 GB RAM averaged 11,011.36 TPS; Crunchy Bridge with 20,000 baseline and 40,000 maximum IOPS plus 64 GB RAM averaged 10,953.71 TPS; and Neon with 64 GB RAM averaged 7,692.91 TPS. This was a ClickHouse-maintained benchmark, and pricing was not normalized. At the smaller 4-vCPU, 16 GB, approximately 100 GB configuration, Crunchy Bridge averaged 6,320.00 TPS while ClickHouse Managed Postgres averaged 6,012.88 TPS. The results are workload- and configuration-specific, so teams should validate production sizing with their own data and HA topology.

The Postgres side is standard, unmodified PostgreSQL with familiar extensions and tooling. You get full superuser access, more than 90 extensions including PostGIS, pgvector, and pg_cron, plus editable PostgreSQL and PgBouncer configuration. That preserves familiar PostgreSQL control while adding a direct path to a separately provisioned ClickHouse service for analytics. The pg_clickhouse extension and PeerDB, the engine underlying ClickPipes CDC, are open source; ClickPipes and the managed operational layer are not. There is no proprietary fork or compatibility layer to walk back to if priorities change.

Key differences vs. AWS RDS #

• Managed analytics replication: ClickPipes replicates selected Postgres tables into a separately provisioned ClickHouse service with seconds-level freshness and eventual consistency. Teams select or create the destination service, configure the ClickPipe, and choose the source tables. Postgres still handles logical decoding, WAL retention, initial snapshots, and network traffic.
• PostgreSQL query access: The preinstalled pg_clickhouse extension lets applications query configured ClickHouse foreign tables through the PostgreSQL interface. Supported filters, joins, semi-joins, aggregations, and functions can be pushed down; unsupported work executes in Postgres, and mixed plans can still consume transactional compute.
• Unified control plane and consolidated billing: ClickHouse Managed Postgres and each ClickHouse service are separately provisioned, use independent compute and storage, and are metered separately. They are managed through one ClickHouse Cloud control plane and appear on a consolidated bill.
• Managed operations: Availability options include no standby, one asynchronous standby, or two synchronous standbys, with automatic failover when standbys are configured. HA standbys are reserved for failover and do not serve reads; dedicated read replicas handle read scaling. The service also supports point-in-time recovery, private networking on eligible tiers, Query Insights, Prometheus-compatible metrics, automation through OpenAPI and clickhousectl, and alpha Terraform support.

Pros #

In a January 2026 product test, ClickHouse reported that 14 of 22 TPC-H queries were fully pushed down through pg_clickhouse; that fully pushed-down subset ran over 60x faster than standard PostgreSQL. This result applies only to that subset. Unsupported operations still run in Postgres, and mixed plans can return rows to Postgres for local execution.

When analytical scans are what drive Postgres overprovisioning, sizing the Postgres and ClickHouse services independently can lower total infrastructure cost. Local NVMe can also reduce the compute required for disk-bound transactional workloads. The result depends on the workload, HA topology, and separately metered analytics capacity.

Cons #

ClickPipes creates an eventually consistent analytical copy in a separately provisioned ClickHouse service, with seconds-level freshness rather than sub-second end-to-end replication. Long-running source transactions, replication backlog, and the configured pull interval can increase lag. Logical decoding, WAL retention, initial snapshots, and network traffic still use source Postgres resources. If sub-second replication is decisive, WalShadow is a separate physical-WAL path when enabled; it remains asynchronous and should be validated on the workload. The Postgres and ClickHouse services use independent compute, storage, and metering, and teams configure ClickPipes and pg_clickhouse explicitly.

Pricing model #

ClickHouse Managed Postgres is available for production workloads on AWS and GCP. Pricing is usage-based on the selected VM configuration and tier, with metered billing. More than 50 NVMe-backed configurations range from 1 vCPU, 8 GB RAM, and 59 GB NVMe to 96 vCPUs, 768 GB RAM, and 60 TB NVMe.

A separately provisioned ClickHouse service for analytics and any dedicated Postgres read replicas consume and meter resources independently from the primary ClickHouse Managed Postgres service. All are managed through the same ClickHouse Cloud control plane and appear on a consolidated bill. Use the current pricing documentation and pricing calculator for a workload-specific estimate.

Migration from AWS RDS (initial load + CDC) #

Migration difficulty: Guided for supported paths, with manual steps for customized targets. ClickPipes supports initial load plus CDC, initial load only, and CDC-only migrations from RDS, Aurora, Google Cloud SQL, Neon, and generic PostgreSQL sources such as PlanetScale.

ClickPipes can copy the source schema automatically when the target database is empty and the source does not use an SSH tunnel. Existing or customized targets require manual schema transfer, and unsupported DDL changes require manual coordination. The guided cutover reduces downtime but still requires a brief write pause, validation, sequence resets, connection changes, and cleanup. Postgres-to-ClickHouse analytics replication is a separate ClickPipes workflow targeting a separately provisioned ClickHouse service.

Who has already moved off AWS RDS #

Teams are already running production workloads on the platform.

Physical Intelligence migrated from RDS to support scaling AI workloads and annotation pipelines.
ClickHouse helped us move off RDS and build a data platform that can support our growing AI workloads. We use Postgres for OLTP and ClickHouse for OLAP in the ClickHouse Cloud platform, giving researchers, training pipelines, and agents fast access to the same data foundation… As our annotation volume grows 10x and continues toward billions of annotations, ClickHouse gives us the platform headroom to keep scaling...

Amazon Aurora (best for staying on AWS) #

Best for #

Teams that want to stay on AWS but need better read scalability and storage auto-scaling without leaving the AWS ecosystem.

Overview #

Aurora is AWS's proprietary, cloud-native relational database engine. It offers drop-in compatibility with Postgres and MySQL, with a higher-performance, storage-decoupled upgrade from standard RDS.

Key differences vs. AWS RDS #

Storage automatically scales in 10GB increments up to 128TB without manual provisioning. Because compute and storage are decoupled, Aurora provides much faster read replica lag times and near-instant crash recovery compared to RDS.

Pros and cons #

Gain: Serverless v2 options for spiky workloads and the ability to provision up to 15 low-latency read replicas.

Give up: You remain within the AWS ecosystem with limited portability. Aurora's Standard pricing includes unpredictable I/O charges per million requests. The I/O-Optimized tier removes these fees but increases instance and storage costs, making it cost-effective only when I/O operations account for more than 25% of your total bill. Aurora is still a row store on a single writer, so it does not eliminate mixed-workload contention or the unpredictability of I/O costs that push teams away from RDS. It upgrades the operational and scaling story, not the analytics one.

Pricing and migration #

Priced per Aurora Capacity Unit (ACU) for serverless, plus storage and I/O.

Migration difficulty: Easy. You can execute a one-click promotion from RDS to Aurora via the AWS console.

Neon (best for serverless developer workflows at lower scale) #

Best for #

Teams that need serverless developer workflows at lower scale, including database branching, scale-to-zero economics, and lightweight testing environments.

Overview #

Neon is an open-source, serverless PostgreSQL platform designed with fully decoupled compute and storage. Neon replaces the standard PostgreSQL storage layer with a custom engine. The storage layer is network-attached. 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 and developer workflows like instant branching, but it adds latency under sustained write pressure.

Key differences vs. AWS RDS #

Neon targets developer workflows that RDS doesn't address: instant database branching for testing and staging, and integrated connection pooling via PgBouncer. It also offers scale-to-zero pricing, though cold-start latency means most production deployments keep compute running.

Pros and cons #

Gain: Potential scale-to-zero economics for variable workloads. Instant database branching, useful for development workflows and testing environments.

Give up: Cold-start latency. By default, Neon spins down compute after five minutes of inactivity. Activating from idle requires time to initialize before the database can serve queries. This startup latency can often be unacceptable and require disabling scale-to-zero. Neon's storage is network-attached, and that shows under sustained write load. In the pinned PostgresBench snapshot, the approximately 500 GB, 16-vCPU, HA-disabled comparison gave Neon with 64 GB RAM a three-run average of 7,692.91 TPS, while ClickHouse Managed Postgres with 64 GB RAM averaged 26,104.81 TPS. Pricing was not normalized. For sustained write-heavy workloads, storage architecture can become a binding constraint.

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.

Google Cloud SQL and AlloyDB (best for GCP and BigQuery) #

Best for #

Teams migrating from AWS to Google Cloud Platform, or requiring deep integration with BigQuery.

Overview #

Cloud SQL is GCP's direct equivalent to RDS, while AlloyDB is their specialized Aurora competitor. AlloyDB provides a Postgres-compatible engine paired with an in-memory columnar cache, making it a natural landing zone for organizations executing a Google-favored multi-cloud strategy. The storage layer is Google Colossus, a distributed network-attached system. Every I/O still crosses the network rather than hitting NVMe drives collocated with compute.

Key differences vs. AWS RDS #

AlloyDB offers a built-in columnar engine designed for hybrid transactional and analytical processing. While less mature than dedicated analytical databases, it provides tighter integration with Google's broader analytics and AI ecosystem. Google also engineered the underlying storage layer to simplify and accelerate standard PostgreSQL vacuuming processes, reducing the risk of transaction ID wraparound issues that can plague massive ingestion workloads.

Pros and cons #

Gain: Excellent GCP ecosystem integration. AlloyDB offers better analytical query performance than RDS, accelerated PostgreSQL vacuuming, and avoids charging separate per-I/O operation fees.

Give up: AlloyDB's columnar engine is an HTAP approach, in which both transactional and analytical workloads run on the same engine. That delivers a single-system experience, but heavy aggregations and transactional writes still compete for the same compute and memory budget. Decoupled OLTP + OLAP stacks avoid that contention by separating the engines. The other primary tradeoff is infrastructural. Transitioning to GCP requires a full cloud migration, not just a simple database swap. Running an application layer in AWS while reading from AlloyDB in GCP introduces significant cross-cloud latency constraints and data egress charges. If AlloyDB data needs to flow downstream to BigQuery for warehousing, to a separate analytics engine, or to any external system, that pipeline runs on Google Datastream, or Dataflow, a separate managed service with its own configuration and bill.

Pricing and migration #

Cloud SQL mirrors RDS pricing complexity. AlloyDB provides a pricing advantage over Aurora by eliminating per-I/O fees, making costs predictable for write-heavy workloads.

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

Crunchy Bridge (best for pure PostgreSQL with superuser access) #

Best for #

Engineers looking for vanilla PostgreSQL with flexible system tuning capabilities.

Overview #

Crunchy Bridge is a fully managed PostgreSQL service that runs across multiple cloud providers, including AWS.

Key differences vs. AWS RDS #

Crunchy Bridge runs standard, unmodified PostgreSQL rather than a proprietary fork. Database administrators get superuser access, allowing unrestricted use of the open-source extension ecosystem, including pgvector, PostGIS, and highly specialized data types. Crunchy Bridge runs on cloud-provider block storage with configurable IOPS, better than gp3 defaults but not matching the latency profile of local NVMe collocated with compute.

Pros and cons #

Gain: Total control over your database configuration, avoiding the managed restrictions of RDS.

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.

How to migrate from AWS RDS (step-by-step playbook) #

Moving off AWS RDS does not require relying only on a static database dump and a long outage. A low-downtime cutover can use PostgreSQL logical replication to copy the baseline, stream ongoing changes, validate the target, and then pause writes briefly for cutover.

Step 1: Audit dependencies and run pre-migration checks #

Inventory all AWS-specific dependencies, including RDS Proxy, IAM authentication, and specific parameter group tweaks. Verify that your target alternative supports all required Postgres extensions.

On your RDS instance, ensure rds.logical_replication is enabled. This requires an instance reboot, whereas multi-AZ deployments can apply the change via failover with minimal downtime.

Step 2: Set up logical replication (replication slot) #

Set up PostgreSQL Logical Replication to create a continuous stream from RDS to the new database. This avoids the severe downtime associated with traditional bulk dump-and-restore operations by capturing ongoing writes in the background.

Step 3: Run initial sync and validate in parallel #

Copy the baseline snapshot while the replication slot captures live changes. Run the systems in parallel.

Now validate query performance, verify indexing, and test analytics offloading on the new system without risking production data.

Step 4: Cut over with minimal downtime #

Pause application writes briefly. Wait for replication lag to reach zero, then compare row counts and run application-level validation. For ClickPipes migrations, large-table row counts may be approximate, so do not treat that comparison as exhaustive.

Update your connection strings and DNS routing, then resume writes to the new database. Keep the original RDS instance active as a read-only rollback option for 48 hours.

Step 5: Decommission RDS and remove replication slots #

Once the new database proves stable under production load, decommission the old RDS instance.

After cutover, remove the source replication slot through the migration tool’s cleanup flow. An unused slot can make RDS retain WAL, consume storage, and eventually threaten the legacy instance.

How to choose the right AWS RDS alternative #

AWS RDS is a solid foundational tool, but unpredictable I/O costs and the severe limitations of mixed workloads force engineering teams to seek modern alternatives in 2026.

Continuing to scale an overprovisioned RDS instance to serve both transactional and analytics workloads results in inflated cloud bills and degraded application performance.

The best alternative depends on your specific bottleneck, whether that’s developer velocity, multi-region scale, or the collision of operational and analytical queries.

If your team struggles with slow dashboards, expensive read replica sprawl, and brittle ETL pipelines, ClickHouse Managed Postgres keeps high-performance OLTP on local-NVMe PostgreSQL and provides a direct path to a separately provisioned ClickHouse service for analytics. The services have independent compute, storage, and metering, with one ClickHouse Cloud control plane and a consolidated bill. ClickPipes can replicate selected tables with seconds-level freshness and eventual consistency, while pg_clickhouse provides PostgreSQL query access and pushes supported work to ClickHouse. The Postgres service remains standard PostgreSQL, with no proprietary fork. ClickHouse Managed Postgres is available for production workloads on AWS and GCP. Start with PostgreSQL, scale with ClickHouse.