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 separating OLTP and OLAP without extra ops: Managed Postgres by ClickHouse
• 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, zero-downtime logical replication path and a unified control plane. 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 almost always costs less than continually upsizing a monolithic RDS instance to handle mixed workloads. 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: Only Managed Postgres by ClickHouse pairs a unified OLTP-plus-OLAP architecture with predictable pricing and an easy migration path. 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). The two-engine operational surface in Managed Postgres by ClickHouse is the lightest trade-off for the high-volume-and-analytics workloads that drive most teams away from RDS.

Managed Postgres by ClickHouse (best for unified OLTP and OLAP) #

Best for unified OLTP and OLAP without ETL #

Teams wanting high-performance managed Postgres combined with a purpose-built analytics layer.

Overview #

Managed Postgres by ClickHouse gives you high-performance, NVMe-backed transactional Postgres alongside the world's fastest analytical database. Both run from a single control plane.

This architecture directly solves the RDS scaling problem. Unifying OLTP and OLAP workloads means that low-latency transactions stay in Postgres, while analytical data is replicated to ClickHouse for heavy reporting, dashboards, and aggregations. This architecture is particularly relevant for AI-native applications, where agent-driven queries generate unpredictable bursts across both transactional and analytical data.

The Postgres side runs on local NVMe storage collocated with compute, delivering microsecond-level disk I/O latency rather than the milliseconds of network-attached storage, such as RDS's EBS volumes. PostgresBench, a reproducible TPC-B-style benchmark, shows this translates to roughly 3x to 5x higher TPS than RDS at the same instance size: 28,668 TPS for Managed Postgres by ClickHouse versus 8,133 TPS for RDS on a ~100 GB working set, and 26,328 versus 5,092 TPS on a ~500 GB working set. The same benchmark puts Aurora at 12,628 and 10,402 TPS, Neon at 8,563 and 7,802 TPS, and Crunchy Bridge at 14,790 and 11,113 TPS at the two scales. Local NVMe outperforms network-attached and block storage under sustained write load. The RDS figures here use default gp3 storage.

The Postgres side is also vanilla PostgreSQL, unmodified, with standard extensions and tooling. You also get full superuser access and 90+ extensions, including PostGIS, pgvector, and pg_cron, plus editable Postgres and PgBouncer configuration. That gives teams the same control they get from pure-Postgres services, and adds the analytics layer on top. The integration components, the pg_clickhouse extension, and the PeerDB engine that powers ClickPipes CDC are open source. There is no proprietary fork or compatibility layer to walk back to if priorities change.

Key differences vs. AWS RDS #

• Built-in real-time CDC: Out-of-the-box ClickPipes integration replicates Postgres data to ClickHouse in seconds, with no custom ETL pipelines to maintain.
• Query transparency: The pg_clickhouse extension lets your application query ClickHouse directly through the Postgres connection string. Existing ORMs, dashboards, and cron jobs keep their current SQL; complex aggregations route to the columnar engine transparently, without any code changes.
• Unified billing and management: One vendor, one bill, one control plane for both your transactional and analytical workloads.
• Enterprise-grade operations: High availability with up to two standbys, point-in-time recovery, read replicas, Private Link networking, Query Insights, Prometheus-compatible metrics, and Infrastructure as Code via OpenAPI and clickhousectl.

Pros #

Public TPC-H benchmarks show analytical queries dropping from 4,478 milliseconds in native PostgreSQL to 73 milliseconds when routed through the ClickHouse extension, a 61x speedup. You also lower total infrastructure costs by right-sizing Postgres for transactions instead of over-provisioning compute to handle analytics, and eliminating data engineering overhead for maintaining ingestion pipelines. The NVMe performance advantage also translates to lower compute requirements. Workloads that require a larger RDS instance to meet throughput targets can run on a smaller Managed Postgres instance, reducing costs regardless of pricing model differences.

Cons #

Analytical reads come from a CDC replica, so analytics data lags OLTP writes by a few seconds. For dashboards and reporting, that lag may not matter, but for use cases that require sub-second freshness for analytical queries, it may matter. Engineers should also know that the stack runs two engines internally, but application code, queries, and operations stay unified through pg_clickhouse and a single console.

Pricing model #

The service is in public beta and available to any ClickHouse Cloud user. It's free until June 15, 2026, with a 50% discount for the duration of the beta. Pricing starts at approximately $30/month for a 1 vCPU / 8 GB RAM / 59 GB NVMe configuration, scaling up to 96 vCPUs / 768 GB RAM / 60 TB NVMe. Over 50 VM configurations are available across memory-optimized, storage-optimized, and CPU-optimized instance types. Pricing is based on the full VM configuration (CPU, memory, and NVMe storage bundled together), not separate compute and disk charges. Native CDC via ClickPipes and the pg_clickhouse extension are included at no additional cost. Postgres and ClickHouse are delivered under one ClickHouse Cloud account with unified billing.

Migration from AWS RDS (logical replication + CDC) #

Migration difficulty: Easy. Fully managed migration workflows powered by ClickPipes handle moves from RDS, Aurora, CloudSQL, Neon, and PlanetScale Postgres with minimal downtime. Standard Postgres logical replication handles the transactional side, while native CDC handles analytical ingestion into ClickHouse.

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. On PostgresBench at the 500 GB scale, Neon delivered 7,802 TPS compared to 26,328 for NVMe-backed Postgres, roughly a 3.4x gap. For high-ingest workloads, the storage architecture is the 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 doesn't mean accepting maintenance windows and application downtime. A professional cutover strategy relies on PostgreSQL logical replication rather than static database dumps.

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 the replication lag to hit zero, ensuring the new database syncs perfectly.

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.

Immediately after cutover, you must manually drop the logical replication slot on the source RDS instance. If you don't, RDS will retain unpurged WAL files, rapidly consuming all available storage and causing an outage on 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, Managed Postgres by ClickHouse unifies OLTP and OLAP so transactions stay in NVMe-backed Postgres and analytics route to ClickHouse through the same connection string. One console, one bill, no proprietary fork. It's now in public beta, with $300 in free credits for new ClickHouse Cloud accounts.