10 Heroku Postgres alternatives for production managed PostgreSQL in 2026

Heroku Postgres is a developer-friendly managed database tightly integrated with Heroku's application platform. In February 2026, Heroku transitioned to a sustaining-engineering model focused on stability, security, reliability, and support. It remains actively supported and production-ready. The shift is a reason to reassess long-term database requirements, but not, by itself, a reason to migrate.

The decision should start with the constraint. I/O may be under pressure, configuration options may no longer fit, reporting queries may compete with production traffic, or the operating model may need to change. In some cases, moving from a Classic plan to Heroku Postgres Advanced may address the requirement. In others, an external managed PostgreSQL service may be the better path. Changing the database does not require leaving Heroku's application platform.

This guide compares 10 Heroku Postgres alternatives for production workloads across storage architecture, PostgreSQL controls, analytics, high availability, operating model, and migration requirements. The comparison focuses on managed PostgreSQL services rather than alternatives to the Heroku application platform itself.

Key takeaways #

• Choose ClickHouse Managed Postgres when sustained disk-bound OLTP is the main constraint and local NVMe fits the workload. Use it with ClickHouse for analytics when separate compute is needed, both within ClickHouse Cloud.

• Choose a hyperscaler-managed option when cloud-native identity, networking, regions, compliance, procurement, or operating standards drive the decision: RDS or Aurora on AWS, Cloud SQL or AlloyDB on GCP, and Azure Database for PostgreSQL on Azure.

• Consider Neon for branching, preview environments, and idle suspension, and Crunchy Bridge when a required PostgreSQL extension or setting is the deciding factor.

• Consider Aiven for multi-service open-source consolidation and DigitalOcean for packaged plans and a compact operating model.

• Stay on Heroku Postgres when its integration and current limits remain sufficient. Migration should solve a measured production constraint, not simply replace one managed PostgreSQL service with another.

Heroku Postgres alternatives compared at a glance #

When to consider a Heroku Postgres alternative: 5 decision criteria #

How Heroku Postgres plan limits create sustained I/O and memory pressure #

Heroku's classic plan tiers bundle compute, memory, and storage limits. If a workload runs short on memory or reaches its IOPS ceiling, the usual response is moving to a larger bundled plan, even when only one resource is constrained. Heroku Postgres Advanced, currently in Limited GA, separates compute from storage and adds follower instance pools.

For a confirmed disk bottleneck, compare the current Heroku plan's I/O behavior with alternatives that use local NVMe, provisioned I/O, distributed storage, or more flexible compute sizing.

How restricted configuration access affects connection pooling and extensions #

Heroku sets connection limits by plan and supports PgBouncer for connection pooling. However, teams have limited control over parameters such as max_connections and many WAL settings. If the workload needs different connection behavior or deeper PostgreSQL tuning, compare those requirements with the target provider’s controls.

Extension availability and versions vary by PostgreSQL version and infrastructure, so compare the installed catalog, including pgvector, with the workload’s requirements before migrating.

Why analytical queries on Heroku Postgres drive up overprovisioning costs #

Reporting scans and complex aggregations can compete with OLTP for CPU, memory, and I/O. Long-running snapshots can also delay vacuum cleanup. Teams may then move to a larger plan to handle analytical peaks even when the transactional workload doesn’t need the extra capacity. Whether that upgrade fixes the bottleneck depends on the workload.

Moving to another managed Postgres provider doesn't automatically solve OLAP contention either. Teams still need to evaluate dedicated read replicas, in-cluster columnar acceleration, or a deliberate OLTP-to-OLAP offload.

How failover and recovery requirements expose Heroku Postgres plan tradeoffs #

High availability architectures vary a lot across providers, so evaluating a production replacement means comparing actual replication, failover, and recovery mechanisms instead of generic uptime numbers.

Start with the exact Heroku plan in use. Primary databases on Premium, Private, and Shield plans fail over to an asynchronous hidden standby. Advanced plans use a standby within the leader instance pool when HA is enabled. Then compare this behavior with the production topology of each alternative.

How pricing, cloud operating model, and workflow constraints narrow the shortlist #

Pricing models vary across providers, so compare the cost of a complete production configuration rather than entry prices alone. Account for compute, storage, I/O, HA, backups, network transfer, and any separate analytics capacity.

The shortlist often comes down to the operating model as much as the price. Existing reliance on AWS IAM, Azure governance and compliance controls, specific Terraform coverage, or database branching for preview environments can rule out providers before cost even comes up.

Should you upgrade to Heroku Postgres Advanced or switch providers? #

Before switching providers, determine whether the constraint can be addressed within Heroku. For teams on Classic plans, Heroku Postgres Advanced changes the resource model and may address some compute, storage, or HA requirements while preserving the existing Heroku application workflow.

Switching becomes more relevant when the requirement goes beyond plan sizing. A different storage architecture, a required PostgreSQL extension or setting, a hyperscaler-native operating model, branching workflows, or a separate analytical serving path can justify evaluating an external managed PostgreSQL service.

When Heroku Postgres still makes sense #

• Teams that rely on Heroku Review Apps, Heroku Connect, Heroku Pipelines, and the wider PaaS workflow.

• Workloads that Heroku's current features and limits can support for the next 12 to 24 months.

• Cases where the cost and risk of migration outweigh the expected gains from addressing I/O or analytics contention.

Evaluating the managed PostgreSQL alternatives #

Comparing providers fairly means looking at their architecture, failover mechanics, and operating model, not forcing all of them into the same generic checklist.

Managed database services rather than application platforms #

This guide covers managed PostgreSQL services that can replace Heroku Postgres while the application remains on Heroku. Backend-as-a-service products and broader application platforms fall outside that scope.

Railway, Render, Fly.io, and Northflank combine application hosting with managed database options. Consider them when the project includes moving both the application and its database.

1. ClickHouse Managed Postgres as a local-NVMe alternative to Heroku Postgres #

Best for #

• Disk-bound or I/O-heavy transactional applications where local NVMe storage physically colocated with compute can address the measured bottleneck.

• Teams that need separate analytics compute. ClickPipes can replicate selected PostgreSQL tables into ClickHouse within ClickHouse Cloud, creating an eventually consistent analytical copy.

• Consider another provider when hyperscaler-native IAM, serverless branching, Microsoft ecosystem requirements, or a required PostgreSQL extension or setting unavailable in ClickHouse Managed Postgres matters more than storage locality.

Overview #

ClickHouse Managed Postgres is a fully managed PostgreSQL service backed by local NVMe storage colocated with compute. It's built for transactional applications that need standard PostgreSQL compatibility along with local-NVMe performance.

Key differences vs. Heroku Postgres #

• Runs PostgreSQL on local NVMe, avoiding the network round trip that network-attached storage architectures require.

• For production evaluation, use one primary and two standbys with quorum-based synchronous replication. A write is confirmed after at least one standby acknowledges it.

• ClickPipes can replicate selected PostgreSQL tables into a ClickHouse service within ClickHouse Cloud for analytics. Query compute for that analytical copy runs separately from the source Postgres instance.

Pros and cons #

• Benefits: Local NVMe supports disk-bound OLTP, while quorum replication requires an acknowledgment from at least one of two standbys. The service also provides managed backups and point-in-time recovery. Read replicas are available on eligible tiers.

• Tradeoffs: The analytical path deliberately uses a separate ClickHouse service within ClickHouse Cloud, which isolates OLAP compute from Postgres but adds separate provisioning, billing, CDC setup, and eventual consistency. Migrating the primary database and configuring analytics replication remain separate workflows.

• Decision boundary: A hyperscaler-managed database may be a better choice when native IAM, procurement, required regions, or cloud-console standardization matters more than raw I/O performance.

Pricing and migration #

• Pricing is usage-based on the VM configuration and tier. ClickHouse Cloud capacity for OLAP and dedicated Postgres read replicas are billed separately.

• Compare the cost of the full HA topology with alternatives instead of relying on single-node pricing.

• Costs depend on vCPU, memory, NVMe capacity, storage autoscaling, and read replicas. Backup retention is one day on Basic and seven days on Scale and Enterprise.

• Migration options depend on database size, acceptable downtime, and the Heroku plan. Paths include pg_dump/pg_restore, a PGBackups restore, Heroku follower/changeover patterns where available, or another supported external source.

• ClickPipes replicates selected tables into ClickHouse with seconds-level, eventually consistent freshness. Observed lag depends on the configured sync interval, source load, network conditions, and destination health; the interval is not an end-to-end freshness guarantee.

2. Amazon RDS for PostgreSQL as an AWS-native managed PostgreSQL alternative to Heroku Postgres #

Best for #

• AWS-native teams that need strict IAM integration, VPC controls, and established AWS procurement, along with deep compliance frameworks.

• Choose over Heroku when standard PostgreSQL controls and AWS operating-model fit are more important than PaaS simplicity.

• Not the right fit when Aurora's shared distributed storage, Neon's branching, or Heroku's low-ops workflow is more decisive.

Overview #

Amazon RDS for PostgreSQL is AWS's managed PostgreSQL service, with parameter groups, multiple storage choices, and both Single-AZ and Multi-AZ deployment options.

Key differences vs. Heroku Postgres #

• Natively integrates with AWS CloudWatch, IAM, VPC networking, and KMS, contrasting with Heroku's abstracted platform interface.

• Offers granular control over storage types, offering both gp3 and io2 Block Express volumes, alongside extensive parameter group tuning.

• Offers two Multi-AZ HA options: Multi-AZ DB instance with one standby and Multi-AZ DB cluster with two readable standbys.

Pros and cons #

• Benefits: Broad regional availability and mature Multi-AZ deployment options. Extensive compliance certifications and familiar AWS procurement round out the appeal for teams already on AWS.

• Tradeoffs: Compared with Heroku's integrated PaaS workflow, RDS exposes more infrastructure choices. Production cost depends on the selected instance, storage and I/O configuration, HA topology, backups, replicas, and network transfer, so compare complete production topologies rather than entry prices.

• Decision boundary: Use RDS to get standard PostgreSQL managed inside AWS. Choose Aurora if distributed storage, read scaling, or Serverless v2 elasticity is a hard requirement.

Pricing and migration #

• Pricing is granular and usage-based, covering Single-AZ vs. Multi-AZ DB instance vs. Multi-AZ DB cluster configurations, standby behavior, backup retention, and point-in-time recovery settings.

• Scaling costs map to instance class changes, storage volume increases (gp3 or io2), provisioned IOPS, read replicas, backup storage, and network egress.

• Migration paths include pg_dump/pg_restore, a PGBackups restore, or applicable RDS migration and import methods. The available path depends on database size, acceptable downtime, and whether it requires replication capabilities that Heroku exposes.

3. Amazon Aurora PostgreSQL as an AWS distributed-storage alternative to Heroku Postgres #

Best for #

• High-scale AWS workloads where read scaling, storage durability, or Serverless v2 elasticity is decisive.

• Choose over RDS when Aurora's cluster volume and replica model solve a real scaling or recovery constraint.

• Consider another provider when exact standard-PostgreSQL engine behavior or portability outside AWS matters more.

Overview #

Amazon Aurora PostgreSQL is an AWS-proprietary relational database engine compatible with PostgreSQL. Unlike RDS for PostgreSQL, it uses a distributed, log-structured storage system built for AWS-scale resilience.

Key differences vs. Heroku Postgres #

• Uses Aurora shared distributed storage across Availability Zones without manual storage provisioning or volume sizing.

• Aurora Serverless v2 provides elasticity for variable workloads, dynamically adjusting capacity unlike Heroku's static plan scaling.

• Aurora Replicas share the cluster volume with the primary and can be promoted during failover.

Pros and cons #

• Benefits: Shared distributed storage architecture, read replicas using the same cluster volume, and failover using Aurora's fast replica promotion mechanisms.

• Tradeoffs: PostgreSQL compatibility is not identical to community PostgreSQL in every edge case. Extension and minor version compatibility must be explicitly verified.

• Decision boundary: Choose Aurora for its AWS-native scale and proprietary storage architecture, not merely as a like-for-like replacement for a Heroku database.

Pricing and migration #

• Pricing is driven by Aurora Standard or Aurora I/O-Optimized models.

• Production HA cost drivers include cluster configuration, Aurora Replicas, Serverless v2 ACUs where used, and backup retention.

• Scaling costs involve instance size or ACUs, storage growth, read replicas, I/O charges, and data transfer.

• Before migration, map the Heroku database's extensions and PostgreSQL version to Aurora's supported catalog. Data can move through dump/restore, PGBackups, or another migration path compatible with both Aurora and the source Heroku plan.

4. Google AlloyDB for PostgreSQL as a GCP high-performance alternative to Heroku Postgres #

Best for #

• GCP-native enterprise workloads that need in-cluster columnar acceleration, high performance, or advanced vector search.

• Choose over Cloud SQL when AlloyDB-specific performance, columnar, read-pool, or AI/vector capabilities justify the higher-end operating model.

• Look elsewhere if the team just needs a simple Heroku-like replacement, standard managed Postgres, or minimal private-networking work.

Overview #

Google AlloyDB for PostgreSQL is Google's fully managed, PostgreSQL-compatible database with HA primary instances, read pools, a columnar engine, and vector-search features.

Key differences vs. Heroku Postgres #

• Uses a storage and caching architecture built for faster analytical scans, alongside a columnar engine for accelerated aggregation queries.

• Deep integration with Google's Vertex AI and vector-search ecosystem.

• High availability and read scaling run on a primary/standby plus read-pool architecture.

Pros and cons #

• Benefits: Strong GCP integration, a performance-focused distributed architecture, AI/ML integrations, and columnar acceleration where the workload can use it.

• Tradeoffs: AlloyDB adds GCP-specific networking, IAM, and operational choices. Weigh those against how much the application actually needs AlloyDB's columnar, read-pool, and vector capabilities.

• Decision boundary: Choose AlloyDB when its GCP-native performance features map directly to a measured bottleneck, not just because a standard Heroku Postgres plan feels constrained.

Pricing and migration #

• Pricing is usage-based, covering the primary instance, standby configuration, backup and point-in-time recovery settings, read pool instances, storage, and network transfer.

• Scaling costs stem from vCPU, memory, storage usage, read pools, backup storage, and egress.

• Migrating from Heroku requires moving the PostgreSQL data and configuring connectivity between the Heroku-hosted application and AlloyDB. Account for private connectivity, firewall rules, authentication, and application-to-database latency when the application remains on Heroku.

5. Google Cloud SQL for PostgreSQL as a standard GCP managed PostgreSQL alternative to Heroku Postgres #

Best for #

• Traditional production workloads native to Google Cloud requiring operational familiarity, IAM integration, VPC controls, and standard managed PostgreSQL behavior.

• Choose over AlloyDB when standard PostgreSQL management suffices and high-end columnar or vector acceleration is unnecessary.

• Weigh other options if database branching, scale-to-zero, or AlloyDB-specific columnar and vector capabilities are decisive.

Overview #

Google Cloud SQL for PostgreSQL is Google Cloud's managed PostgreSQL service, with IAM, VPC networking, regional HA, read replicas, and edition-dependent read pools.

Key differences vs. Heroku Postgres #

• Offers deep integration with Google Cloud IAM, Secret Manager, VPC networking, and BigQuery through supported federation patterns.

• Exposes maintenance window controls and flexible machine and storage configuration, giving teams more control over resource sizing than classic Heroku plans.

• Employs Cloud SQL regional HA, placing a standby instance in another zone. Read replicas run entirely separately from that standby behavior.

Pros and cons #

• Benefits: Straightforward integration with the broader GCP data ecosystem, regional HA with a standby instance in another zone, and familiar Google Cloud operations.

• Tradeoffs: Cloud SQL adds GCP-specific networking, IAM, maintenance, and edition choices that Heroku abstracts. Verify the required PostgreSQL version, extension, and database-flag support before migration.

• Decision boundary: Choose Cloud SQL for standard GCP-managed Postgres. Choose AlloyDB only when its premium disaggregated architecture solves a specifically measured bottleneck.

Pricing and migration #

• Pricing is usage-based, covering zonal vs. regional HA configuration, the standby instance, backup retention, and any read replicas used.

• Scaling costs factor in machine type, CPU, RAM, storage, backup storage, network transfer, and any applicable Committed Use Discounts.

• Migration paths include pg_dump/pg_restore, a PGBackups restore, or an applicable Cloud SQL import path. Validate the PostgreSQL version, extensions, roles, and networking requirements before cutover.

6. Azure Database for PostgreSQL Flexible Server as an Azure-native compliance alternative to Heroku Postgres #

Best for #

• Workloads that need Azure-native identity, Microsoft Entra ID, compliance, private networking, and the Microsoft operating model.

• Choose over Heroku when Azure governance, procurement, security tooling, or enterprise compliance is a hard requirement.

• Consider other tools if the team wants Heroku-like simplicity, AWS/GCP-native controls, or requires a PostgreSQL extension or setting that Azure Database for PostgreSQL does not provide.

Overview #

Azure Database for PostgreSQL Flexible Server is Microsoft's managed PostgreSQL service with configurable compute, storage, networking, identity, and HA options.

Key differences vs. Heroku Postgres #

• Exposes burstable and general-purpose compute options, providing more granular infrastructure-sizing control than classic Heroku plans.

• Natively integrates with Microsoft Entra ID for identity and authentication, while Microsoft Defender for Cloud provides threat protection and security recommendations.

• Offers same-zone or zone-redundant high availability using a dedicated standby server.

Pros and cons #

• Benefits: Granular parameter tuning, a strong enterprise compliance posture, and deep Microsoft identity and governance integration.

• Tradeoffs: Azure portal, resource-group, and networking management require configuration that Heroku abstracts.

• Decision boundary: Choose Azure Database for PostgreSQL when fitting the Microsoft operating model matters most. Consider another provider when the main goal is a simpler replacement for Heroku Postgres.

Pricing and migration #

• Pricing is usage-based, covering same-zone vs. zone-redundant HA, the standby server, backup retention, point-in-time recovery settings, and read replicas.

• Scaling costs include compute tier, vCores, storage, IOPS where applicable, backup storage, and network transfer.

• Migration paths include PGBackups, changeover-based workflows where available, and offline dump/restore. Azure Database Migration Service also supports PostgreSQL migration workflows, but the selected path must account for Heroku's replication constraints.

7. Neon as a serverless, scale-to-zero alternative to Heroku Postgres #

Best for #

• Variable workloads, development and test environments, preview environments, and teams prioritizing database branching.

• Choose over Heroku when branch-per-preview workflows or idle-compute savings are important.

• For sustained I/O-heavy production OLTP, benchmark the exact Neon target configuration against other architectures rather than treating branching, autoscaling, or scale-to-zero as a throughput claim.

Overview #

Neon is a serverless Postgres platform built on separated storage and compute, with autoscaling, scale-to-zero, and database branching.

Key differences vs. Heroku Postgres #

• Scale-to-zero pauses compute billing when the database is idle, on plans and settings that support it.

• Database branching lets developers clone production-like data on demand for preview environments.

• Compute and storage separation fundamentally changes the HA, latency, and recovery model compared to traditional primary/standby PostgreSQL services.

Pros and cons #

• Benefits: A strong developer experience, dev/test savings when scale-to-zero reduces idle compute, and database branching for preview workflows.

• Tradeoffs: Scale-to-zero can add resume latency. Staying always-active avoids that delay, at the cost of paying for idle compute. For sustained I/O-heavy OLTP, compare a production Neon configuration with other architectures on the actual workload.

• Decision boundary: Choose Neon for a smaller-scale or latency-tolerant development workflow when branching, preview environments, or idle suspension is decisive. For sustained I/O-heavy production OLTP, compare the exact target configurations on the workload.

Pricing and migration #

• Pricing is usage-based, covering compute and storage configuration, read replicas or additional compute where supported, point-in-time recovery history retention, and project limits.

• Scaling costs are driven by active compute time, storage size, data written, branching volume, and additional compute endpoints.

• Development databases can typically move through standard dump/restore workflows. Production cutovers that require low downtime need a synchronization path compatible with Heroku's source-side replication constraints, followed by validation and final cutover.

8. Crunchy Bridge as a PostgreSQL extension and configuration alternative to Heroku Postgres #

Best for #

• Teams that require a PostgreSQL extension or setting unavailable in ClickHouse Managed Postgres.

• Choose over Heroku when broader PostgreSQL extension or configuration access matters more than integrated app-platform convenience.

• Consider another provider when hyperscaler-native IAM, scale-to-zero branching, or bundled Heroku-style app deployment is decisive.

Overview #

Crunchy Bridge is a managed PostgreSQL service from Crunchy Data with a broad extension catalog and a postgres superuser role for administrative tasks.

Key differences vs. Heroku Postgres #

• Provides a broad extension catalog and PostgreSQL role and tuning controls, subject to Crunchy Bridge's current limits.

• Crunchy Data Warehouse keeps a PostgreSQL interface while its planner can delegate eligible analytical query work to an integrated DuckDB vectorized execution path, with Iceberg support. It also supports logical replication from another PostgreSQL source into managed Iceberg tables.

• Deploys on AWS, GCP, or Azure through a single, unified control plane, giving teams a consistent management experience across supported clouds.

• Uses its own replica and failover model rather than Heroku's hidden-standby or leader-instance-pool model.

Pros and cons #

• Benefits: Broad extension support, a postgres superuser role for administrative tasks, and PostgreSQL configuration controls.

• Tradeoffs: Lacks Heroku's integrated PaaS application-hosting layer. Compare the required extension or setting, HA model, region, and migration path against the workload.

• Decision boundary: Choose Crunchy Bridge when the workload requires a PostgreSQL extension or setting that ClickHouse Managed Postgres does not currently provide.

Pricing and migration #

• Pricing is instance-based, covering instance size, storage, replicas, backup and point-in-time recovery retention, and cloud/region selection.

• Scaling costs map to plan family, CPU, RAM, storage, replica count, backup storage, and region provider choice.

• Standard migrations can use dump/restore. A low-downtime cutover requires a synchronization method that works within Heroku's replication constraints, including any external CDC path used before the final cutover.

9. Aiven for PostgreSQL as a multi-cloud open-source platform alternative to Heroku Postgres #

Best for #

• Organizations consolidating PostgreSQL alongside other open-source services, including Kafka, Valkey, OpenSearch, and ClickHouse, under a single control plane.

• Choose over Heroku when multi-cloud open-source service consolidation and infrastructure-as-code workflows matter more than PaaS simplicity.

• Look elsewhere if hyperscaler-native IAM or procurement, or a smaller standalone PostgreSQL operating surface, is the primary decision criterion.

Overview #

Aiven is a managed data platform that runs PostgreSQL and other open-source data services across supported public clouds.

Key differences vs. Heroku Postgres #

• Deployable across AWS, GCP, Azure, DigitalOcean, and UpCloud where supported.

• Aiven provides Terraform-based provisioning for PostgreSQL. Compare its lifecycle coverage against the Heroku CLI and add-on workflow the team currently relies on.

• Requires analyzing Aiven's plan-specific HA and standby behavior rather than assuming a normalized PostgreSQL topology.

Pros and cons #

• Benefits: A single bill and control plane for multiple data services, specific HA options by plan, and multi-cloud deployment flexibility.

• Tradeoffs: Costs fluctuate based on cloud provider, region, service plan, node count, and HA configuration. Cross-service integration responsibilities sit with the user.

• Decision boundary: Choose Aiven when consolidating several managed open-source services matters more than finding the simplest standalone PostgreSQL replacement.

Pricing and migration #

• Pricing is plan- and cloud-dependent, covering the service plan, cloud provider, region, node count, standby and HA behavior, backup retention, and read replicas.

• Scaling costs factor in CPU, RAM, storage, cloud provider selection, additional nodes, backup storage, and network traffic.

• Migration can use Aiven's migration utilities where they are compatible with the Heroku source, or standard dump/restore workflows. Verify whether the selected path requires source-side replication capabilities that Heroku does not expose.

10. DigitalOcean Managed PostgreSQL as a simple packaged-pricing alternative to Heroku Postgres #

Best for #

• Workloads where packaged plans and a compact operational surface matter most.

• Choose over Heroku when those packaged economics matter more than granular infrastructure control.

• Consider another provider when a required PostgreSQL setting or extension is unavailable, superuser access is required, or a separate analytical serving path is decisive.

Overview #

DigitalOcean Managed PostgreSQL is a managed PostgreSQL service that uses packaged node plans and can be deployed alongside DigitalOcean App Platform.

Key differences vs. Heroku Postgres #

• Packaged pricing shows the compute, storage, and monthly cost of each plan before purchase.

• Offers a simpler UI and network configuration than AWS or GCP, matching some of Heroku's ease of use.

• Standby nodes provide automatic failover, while read-only nodes support read scaling, including across regions.

Pros and cons #

• Benefits: Packaged plans and a smaller operational surface than hyperscaler databases.

• Tradeoffs: DigitalOcean restricts superuser access and exposes a subset of PostgreSQL configuration flags. Teams needing a specific unavailable setting or extension, superuser access, or a separate analytical serving path must evaluate those requirements explicitly.

• Decision boundary: Choose DigitalOcean when packaged pricing and a smaller operational surface matter more than the combined Postgres and analytics path.

Pricing and migration #

• Pricing is packaged and transparent, covering shared or dedicated CPU plans, standby nodes, read-only nodes, additional storage, and region.

• Scaling costs depend on node size, additional storage, standby nodes, and read-only nodes.

• Smaller databases can move through pg_dump/pg_restore or PGBackups. Production cutovers that require low downtime need a synchronization method compatible with Heroku's replication constraints and a planned final write freeze or cutover step.

How to migrate off Heroku Postgres (pg_dump, PGBackups, forks/followers, CDC, and analytics offload) #

Pre-migration diagnostics: what to measure on Heroku Postgres #

Right-sizing the target cluster starts with what Heroku's own metrics already show. Check for:

• Sustained memory swapping, connection queuing, or storage growth

• Write volume, I/O pressure, slow queries, and lock waits

Then review the database itself: installed extensions (\dx), the PostgreSQL major version, collation and locale assumptions, roles and privileges, large objects, partitions, and custom types.

Before planning any external CDC path, identify tables without primary keys or stable replica identity, and check for schema drift, sequences, generated columns, triggers, and materialized views. Confirm that both Heroku and the destination support the proposed CDC method.

Verify the current Heroku plan's PGBackups availability, retention, Continuous Protection, follower, fork, changeover, and export behavior before planning the migration. Don't assume tier eligibility. Use PGBackups for databases up to 20 GB. Advanced Limited GA doesn't support PGBackups.

Use Heroku forks where available to rehearse the pg_dump/pg_restore process, test extension compatibility, and validate application queries without touching production data. Use Heroku followers where available for read-only validation and lag measurement.

Replication, dump/restore, and analytics offload constraints when leaving Heroku Postgres #

Heroku Postgres doesn't expose external logical replication slots. Standard low-downtime PostgreSQL publisher/subscriber migrations to another managed provider are not natively available from Heroku.

For smaller databases or migrations that can tolerate a write freeze, use pg_dump/pg_restore or a PGBackups restore. Target provider import tools may support PostgreSQL generally, but teams must validate that the chosen path doesn't require an external logical replication slot on Heroku.

If using any separately available CDC path, monitor WAL growth, lag, unsupported DDL, sequence synchronization, and primary-key requirements.

After migrating to ClickHouse Managed Postgres, ClickPipes can replicate selected PostgreSQL tables into ClickHouse for OLAP, creating an eventually consistent analytical copy. ClickPipes handles analytics offload rather than the primary database migration. Teams still need to map PostgreSQL types to ClickHouse types, validate which tables are selected, and account for schema changes.

Migration risk mitigation checklist #

Run parallel systems where possible, while keeping Heroku as the system of record until cutover. Backfill from a dump, PGBackups, or supported import path, compare row counts and checksums, validate indexes, and replay representative application queries against the new target.

Plan the final cutover precisely. Freeze writes on Heroku, complete the final data sync or supported synchronization step, sync sequences, switch the DATABASE_URL, validate connection strings, and confirm that the rollback procedure works.

Validate application connectivity from Heroku, checking SSL modes, firewall allowlists, private networking configurations, connection pooling, and DNS TTLs. Watch for downstream breakages in Heroku config vars, release-phase tasks, background workers, and BI tools.

Keep the Heroku Postgres instance, Continuous Protection restore point, fork, or follower as a rollback reference for a defined window. A rollback is safe only if writes remain frozen or any post-cutover changes can be reconciled.

Conclusion #

The best Heroku Postgres alternative depends on what has actually stopped working in the current setup. A storage bottleneck, an extension requirement, analytics contention, cloud-standardization requirements, and development branching are different problems and point to different providers.

ClickHouse Managed Postgres is worth evaluating when sustained disk-bound OLTP is the primary constraint and its local-NVMe architecture with two synchronous standbys fits the required durability model. When analytics needs dedicated resources, ClickPipes can replicate selected tables into a separate ClickHouse service within ClickHouse Cloud rather than making analytical queries compete with transactional workloads.

Hyperscaler-managed services remain strong choices when native IAM, networking, regions, procurement, or existing cloud operations are decisive. Neon, Crunchy Bridge, Aiven, and DigitalOcean each fit narrower workflow or operating-model requirements.

The next step is to reduce the list to the two or three providers that address the measured constraint, then test them using the same production queries, data size, HA topology, migration assumptions, and complete cost model.