AWS vs GCP vs Azure vs Neon vs Supabase: Strategic Analysis for PostgreSQL Hosting Selection

I. Executive Summary: The PostgreSQL Soulmate Framework

The modern landscape for PostgreSQL hosting presents a complex choice, moving far beyond traditional database-as-a-service (DBaaS) offerings. Organizations seeking their “PostgreSQL Soulmate” must evaluate solutions based on three distinct strategic pillars, each optimized for a specific value proposition—Operational Maturity, Consumption Efficiency, or Developer Velocity. The selection process requires a comprehensive understanding of architectural trade-offs, Total Cost of Ownership (TCO) across different scaling profiles, and alignment with emerging technology needs such as Generative AI.

A. The Three Pillars of Modern PostgreSQL Hosting

The current market is fundamentally segmented by core design philosophy:

1. Hyperscaler Managed Services (AWS, GCP, Azure): These platforms optimize for Operational Maturity, Compliance, and Global Scale. They are the default choice for established enterprises that require proven reliability, stringent compliance certifications (like SOC2 or HIPAA), and deep, seamless integration into a vast cloud ecosystem for security, networking, and analytics. Their architecture focuses on high-availability (HA) and maximizing raw performance through proprietary optimizations.
2. Cloud-Native Serverless (Neon): This category optimizes for Developer Workflow and Consumption Cost. Neon is defined by an architectural disruption: the effective separation of storage and compute.1 This fundamental design choice enables capabilities previously impractical for relational databases, such as scale-to-zero efficiency for highly variable loads and instant database branching, which drastically improves CI/CD pipelines.3
3. Backend-as-a-Service (BaaS) (Supabase): Supabase optimizes for Developer Velocity and Full-Stack Completeness. It bundles a standard Postgres core with essential backend services—Authentication, Realtime subscriptions, Storage, and instant APIs.5 This approach prioritizes the speed of application development, effectively serving as an open-source alternative to proprietary platforms like Firebase.

B. Synopsis of Key Findings

Strategic comparison across the five platforms yields clear differentiators:

• Performance Leader: AWS RDS PostgreSQL, particularly the Aurora variant, maintains the industry lead in transactional throughput (TPS) and lowest latency, a benefit derived from its optimized, distributed storage layer.6
• TCO Leader (Variable Load): Neon offers superior cost efficiency for development environments and applications with sporadic or highly unpredictable traffic patterns, thanks to its scale-to-zero architecture, where compute resources automatically idle when not in use.4
• DevX Leader: Neon’s instant Copy-on-Write (CoW) branching provides an architecturally superior mechanism for database cloning, offering a significant advantage for CI/CD workflows and heavy testing compared to the instance duplication or migration overhead required by Supabase or Hyperscalers.3
• Real-Time Champion: Supabase is the specialized platform for real-time applications, offering native, built-in Realtime subscriptions out-of-the-box, eliminating the need for external service orchestration.8
• AI Readiness: Both AWS Aurora and Neon have demonstrated early leadership in adopting advanced vector capabilities. AWS Aurora announced support for pgvector 0.8.0 10, while Neon is strategically positioned and purpose-built for AI/ML and agentic workloads that require dynamic resource provisioning.8

II. Architectural Disruption and Scaling Paradigms

Understanding the underlying architecture is essential for predicting performance limits, scaling costs, and managing vendor dependencies. The five contenders represent a spectrum from tightly customized, high-performance engines to highly flexible, modular, serverless components.

A. Hyperscaler Architecture: Performance vs. Portability

Hyperscalers invest heavily in optimizing PostgreSQL for the constraints and opportunities of massive cloud infrastructure.

1. AWS Aurora PostgreSQL

Amazon Aurora is engineered to combine the availability of high-end commercial databases with the cost efficiency of open-source solutions.7 Aurora achieves up to $3\times$ the throughput of standard PostgreSQL through proprietary software and hardware techniques that optimize I/O operations and performance consistency using distributed systems.7

Scaling is managed through multiple, sophisticated mechanisms:

• Aurora Serverless v2 provides an on-demand, auto-scaling configuration that automatically starts up, shuts down, and vertically scales capacity up or down based on application needs, allowing users to run databases without managing instance types.7
• For extreme scale, Aurora PostgreSQL Limitless Database horizontally scales to support millions of write transactions per second and petabytes of data, seamlessly exceeding the limitations of a single instance while maintaining transactional consistency.7

2. Azure Database for PostgreSQL Flexible Server

Azure’s offering emphasizes operational flexibility and strong integration with Azure’s cloud services. The Flexible Server delivers built-in high availability (HA), automatic backups, and elastic vertical scaling that typically executes within seconds.11 A key differentiator for cost optimization is the ability to stop and start the server, a feature particularly useful for non-production environments.11 Azure supports a wide range of community PostgreSQL versions (currently up to 18, 17, 16, 15, 14, 13, 12, and 11).11

3. GCP Cloud SQL

GCP Cloud SQL offers a managed vanilla PostgreSQL experience, providing high compatibility and integration within the Google Cloud ecosystem. It features two primary editions, Enterprise and Enterprise Plus, which offer enhanced levels of availability, performance, and scale. Enterprise Plus instances can scale up to 128 CPUs and 864 GiB of memory.13 Pricing for resources, including CPUs and memory, is region-dependent, and the platform utilizes Committed Use Discounts (CUDs) for long-term cost reduction.13

The Trade-off Between Custom Storage and Community Compatibility

The architectural choices made by the Hyperscalers directly dictate the degree of vendor lock-in. While AWS Aurora’s custom, proprietary storage layer delivers industry-leading performance 7, it simultaneously creates a deeper level of architectural vendor dependency than its competitors. Aurora intercepts and redirects fundamental calls from reading and writing a local file system to RPC calls into its custom-built, cloud-native storage.9 This non-standard I/O layer is proprietary to AWS. Consequently, operational scripts, monitoring tools, and scaling logic developed for Aurora cannot be directly ported to another vendor. By contrast, Azure and GCP, relying on closer-to-vanilla Postgres instances, offer better underlying portability, positioning them as strategically safer choices for organizations prioritizing vendor agnosticism over marginal performance gains.

B. Cloud-Native Serverless (Neon): The CoW Revolution

Neon represents a fundamental architectural shift for relational databases.

Its Core Architectural Design centers on separating the compute and storage layers.1 The compute layer uses standard PostgreSQL server nodes, while the storage component is a custom-built, multi-tenant system shared across all compute nodes.2 This architecture is designed for cloud independence and high efficiency.1

The separation enables several key features critical for modern development workflows:

• Automatic Scaling: Compute resources scale automatically based on the workload and can idle when not in use, enabling a pay-per-use model that minimizes cost during inactive periods.4
• Instant Branching: Neon’s purpose-built storage layer uses Copy-on-Write (CoW), a feature commonly associated with version control systems like Git.9 This allows developers to create a new branch from production instantly by moving a few pointers, yielding an isolated, full copy of the data without the time or storage overhead of a physical data duplication.2
• Open Source Commitment: Neon’s core technology is open source (Apache 2.0), furthering its commitment to cloud independence and fostering community contribution.1

C. Backend-as-a-Service (Supabase): Vanilla Postgres with Middleware

Supabase’s primary value proposition is developer velocity. It is a “batteries-included Postgres platform” that wraps a dedicated vanilla Postgres instance with various open-source middlewares.2 These bundled services include Authentication, Edge Functions, Realtime subscriptions, Storage, and instant APIs.5

The Scaling Model employed by Supabase relies on fixed compute tiers tied to the chosen plan (e.g., Pro, Team).3 Performance is constrained by the allocated compute resources, and scaling usually requires upgrading the underlying instance size. This instance upgrade process typically involves brief downtime.3 While storage scales independently and read replicas can help distribute read-heavy workloads, this model functions best for steady traffic patterns.3

The BaaS Latency Penalty

The convenience and rapid prototyping benefits offered by Supabase’s bundled services introduce a tangible cost in terms of raw, optimized database performance, especially for heavy I/O operations. Benchmarks indicate that Supabase trails Hyperscalers like AWS RDS and Azure Flexible Server in transactional throughput (TPS).6 More critically, Supabase demonstrated significantly slower batch download times, requiring 160 seconds to download 5GB of data, compared to 51 to 54 seconds for AWS, GCP, and Azure.6 Furthermore, Supabase utilizes ARM processors, and overall CPU utilization during load peaked higher (57%) compared to AWS and GCP (45-50%).6 This performance differential suggests that the mandatory overhead introduced by the middleware layer (for features like Realtime and Auth) and the specific hardware architecture result in a measurable performance tax compared to specialized managed databases.

III. Financial and Operational Analysis: Total Cost of Ownership (TCO)

The financial comparison of these platforms requires analyzing three fundamentally different pricing paradigms: provisioned, consumption, and tiered fixed pricing.

A. Pricing Model Breakdown: Provisioned vs. Consumption vs. Tiered

1. Provisioned (Hyperscalers): Costs are predictable and based on reserved resources (vCPU/RAM/Storage). Cost optimization for long-term, high-volume workloads relies heavily on Committed Use Discounts (CUDs) in GCP 13 or Reserved Instances in AWS, which require multi-year financial commitments.15 Storage (General Purpose or Provisioned IOPS) and data transfer (egress) are billed separately.16
2. Consumption (Neon): The Pay-per-use model means users only incur costs for resources actively used. The automatic scaling capabilities allow compute to idle during periods of inactivity, making this model highly cost-efficient for development environments, staging, and SaaS applications characterized by highly variable daily or weekly traffic patterns.4
3. Tiered (Supabase): Supabase uses simple, fixed-price tiers. For instance, the Pro plan starts at $25 per month and includes specific allowances for compute credits, disk size (8 GB), and egress (250 GB).17 This model offers excellent budget predictability for small to medium workloads with generally steady traffic.16

B. TCO Benchmark Scenarios (Low, Mid, High Workload Profiles)

Comparing entry-level and mid-range costs highlights the structural differences in resource allocation and pricing philosophies:

• Low-Cost Dev/Test (Startup MVP): For minimal environments, Neon’s Free Tier or Supabase Pro ($25/month) are the most cost-effective.15 Neon excels if compute should scale-to-zero, while Supabase is better if the immediate utility of built-in Auth and APIs is required.
• Mid-Range SaaS (Steady Traffic): For a small production setup (e.g., 100 GB storage, moderate traffic), Supabase often ranges between $40–$60 per month, depending on egress and compute size, while a comparable AWS RDS setup (e.g., db.t3.small with storage) typically costs around $70–$100 per month.16
• High Write/Read Throughput (Enterprise): At enterprise scale, the Hyperscalers dominate. High-performance instances, such as AWS Aurora’s db.r6g.4xlarge, can cost upwards of $1,693.60 monthly.18 For long-term commitments, AWS Aurora Reserved Instances are often the most cost-effective choice for demanding, high-performance needs, with costs ranging from $96–$437 per month depending on the configuration, due to I/O-optimized pricing and built-in replication.15

The Cloud Vendor Lock-in Cost Paradox

The TCO analysis reveals that while Hyperscalers have higher entry-level provisioned prices, their long-term financial structures generate significant economic barriers to switching. AWS RDS Reserved Instances, for example, can cut the compute cost of a stable workload by up to 60% when a long-term commitment is made.15 This deep, multi-year discount effectively subsidizes the cost of the underlying cloud infrastructure, making the overall TCO for a stable, high-scale workload lower than competing cloud-native platforms over a multi-year period. Consequently, for organizations that prioritize financial predictability and guaranteed low compute rates, the financial dependence created by these contracts can be a more powerful form of vendor lock-in than the underlying architectural lock-in.19

IV. Developer Experience (DevX) and Modern Workflow Enablement

The shift towards cloud-native databases is heavily influenced by their ability to support modern DevOps and CI/CD practices. Platforms are increasingly judged on developer experience (DevX), particularly through features that streamline testing and iteration.

A. Database Branching: The Cornerstone of Modern CI/CD

Database branching, a concept borrowed from Git repositories, has become a core requirement for efficient development, allowing developers to test migrations and features against full production data copies in isolated sandboxes.

Neon’s Architectural Superiority

Neon’s serverless architecture, which separates compute and storage, makes database branching instantaneous and resource-efficient.9 Because of its custom Copy-on-Write (CoW) implementation, creating a new branch is achieved by simply moving a few pointers, resulting in an isolated, full copy of the data without physically duplicating the massive dataset.9 This architectural design fundamentally transforms the database from an infrastructure component into a core developer workflow tool, ideal for integration into CI/CD pipelines and pull request (PR) testing.8

Supabase’s Implementation

Supabase does offer Git-integrated branching, which provides integration convenience.2 However, because it relies on standard Postgres instance behavior, it lacks the CoW efficiency of Neon. Branching often involves duplicating projects either manually or using tools like pg_dump, a process that increases in time and cost proportionally with the size of the database.3 For most teams using Supabase, the common practice remains running separate staging and production projects.3

Hyperscaler Deficiency

AWS, GCP, and Azure, while offering powerful features, do not have native, instantaneous branching capabilities derived from CoW. Creating test environments requires time-consuming and expensive operations involving manual snapshotting, restoring, or cloning the database. This operational friction inherently limits the practical frequency of per-feature branching, slowing down development velocity relative to Neon.

B. Real-Time Capabilities

The need for real-time data synchronization in collaborative applications, chat systems, and live dashboards is a strong differentiator.

• Supabase Advantage: Supabase is the undisputed leader in this domain because it offers built-in Real-time subscriptions as a core service, alongside its database.5 This capability is available out-of-the-box, significantly simplifying the architecture for applications requiring live data feeds.8
• Neon/Hyperscaler Requirement: Neon and the Hyperscalers focus purely on the database layer. Achieving real-time functionality on these platforms requires users to architect and manage external solutions, such as change data capture (CDC) connected to message queues (e.g., Kafka) or in-memory caches (e.g., Redis), adding complexity, cost, and latency to the overall application stack.8

C. The Full-Stack vs. Database-Only Philosophy

The choice between Supabase and Neon reflects a fundamental philosophical difference in development approach.

• Supabase: The “batteries-included” approach means developers interact with a single, comprehensive platform for data, authentication, storage, and APIs.5 This integrated stack optimizes for rapid prototyping and minimizing the number of services managed.3
• Neon: Neon focuses exclusively on optimizing the PostgreSQL database experience for cloud-native workflows. The developer is expected to provide their own authentication, storage, and API layers.3 This approach grants maximum flexibility but requires more initial setup and integration effort.

The Scaling Wall in Developer Velocity (Supabase)

While Supabase optimizes for early-stage velocity, its reliance on fixed instance scaling models becomes an operational bottleneck for maturing applications experiencing unpredictable traffic. Supabase performance depends on its fixed instance size, and handling traffic spikes or sustained heavy loads requires upgrading the instance.3 This upgrade is a manual operation that involves brief downtime and requires proactive capacity planning.3 In contrast, Neon and AWS Aurora Serverless v2 minimize operational risk by handling variable traffic automatically, scaling compute up and down dynamically based on demand.3 For a growing organization, automated, elastic scaling offers superior resilience and continuity under fluctuating load.

V. Feature Parity, Enterprise Readiness, and the AI Frontier

PostgreSQL’s versatility, driven by its extension ecosystem, is central to its accelerating adoption across enterprise IT strategies in 2025.20 Evaluating managed services requires scrutiny of their support for specialized extensions and their commitment to enterprise compliance.

A. Vector Database Capabilities and the AI Advantage

The rise of Generative AI has made native support for vector embeddings crucial for Retrieval-Augmented Generation (RAG) and semantic search applications.

• AWS Leadership: AWS Aurora demonstrated leadership by announcing support for pgvector 0.8.0.10 This version includes significant improvements, such as enhanced performance for searching and building HNSW indexes, better data filtering using WHERE clauses, and iterative index scans to prevent “overfiltering” and ensure sufficient result generation.10 This positions AWS as a robust provider for enterprise-grade RAG infrastructure built on PostgreSQL.
• Neon’s Strategic Alignment: Neon is designed for AI/ML applications and agentic workloads.8 Its ability to instantly provision database environments and dynamically scale compute resources perfectly suits the bursty, variable nature of AI agent computations.

B. Extension Support and Compatibility

PostgreSQL’s utility for specialized data types, such as time series (TimescaleDB) 21 and geospatial data (PostGIS) 22, depends entirely on managed services providing robust and up-to-date extension support.

• Extension Policy (AWS): AWS maintains tight control over extensions for stability and security. New extensions or updated versions are typically offered only in the subsequent quarterly release cycle after becoming available in the community.23 While necessary for enterprise stability, this policy creates inherent delays in accessing the absolute newest open-source features. AWS also reserves the right to end support for extensions with short notice if critical security issues arise.23
• Cloud-Native Flexibility: Cloud-native platforms like Neon and Supabase, which are designed to hug vanilla Postgres closely, often exhibit faster adoption cycles for community extensions like TimescaleDB, which extends core PostgreSQL functionality with optimizations for high-volume time-series data, including data compression and custom SQL functions.21

The Latency of Enterprise Extension Adoption

The necessary operational rigor of Hyperscalers, characterized by quarterly release cycles and stringent vetting 23, introduces a strategic disadvantage in rapidly evolving technology fields, such as AI/ML, which depend heavily on the latest open-source feature optimizations. The delay in adopting the newest community release of critical extensions like pgvector means that performance gains, new indexing capabilities, or critical bug fixes are unavailable to Hyperscaler customers immediately. Organizations prioritizing speed of innovation in AI infrastructure may find that cloud-native platforms, which can adopt these community releases faster, offer a competitive edge in model performance optimization and application tuning.

C. Operational Maturity and Compliance

For enterprise-grade workloads, reliability, security, and compliance are non-negotiable foundations.

• Hyperscaler HA and DR: AWS, GCP, and Azure provide established, world-class high-availability (HA) and disaster recovery (DR) mechanisms, including automated multi-Region replication (Aurora) 10, zone redundant HA (Azure Flexible Server) 11, continuous backups, and robust point-in-time recovery for defined retention periods (up to 35 days on Azure).11 Their operational track records are unmatched for mission-critical enterprise applications.
• Enterprise Compliance: Supabase has made significant strides in enterprise readiness, offering SOC2 compliance and advanced organizational controls. HIPAA compliance is available as a paid add-on, along with sophisticated security features like SSO for the dashboard, JWT-based authentication, and OAuth support.8
• Automated Maintenance: All five services are fully managed, handling essential tasks such as underlying hardware, operating system, and database engine maintenance automatically, freeing development and operations teams to focus on application logic.4

VI. Strategic Imperatives: Performance, Portability, and Vendor Lock-in

Strategic selection depends on balancing current performance requirements against future flexibility and cost management.

A. Observed Performance Analysis

Independent benchmarks confirm that architectural differences lead to measurable performance variations:

• Overall Performance: AWS RDS PostgreSQL leads the field with an average of 2.7K Transactions Per Second (TPS) and $2.884$ milliseconds average latency. Azure Flexible Server ranks a close second, achieving an average of 2.4K TPS and $3.260$ milliseconds latency.6
• Efficiency: Hyperscalers generally demonstrated high I/O efficiency, with AWS and GCP peaking around 45-50% CPU utilization during heavy bulk operations (e.g., COPY command), whereas Azure peaked higher at 85%, and Supabase peaked around 57%.6 This confirms that the Hyperscalers’ optimized storage and compute configuration yield better resource efficiency under heavy load.
• Implication: For extreme scale, transactional consistency, and guaranteed low latency, the custom, distributed storage of AWS Aurora provides a definitive performance advantage that generalized vanilla Postgres hosts cannot easily match.

B. The Spectrum of Vendor Lock-in

The core PostgreSQL engine acts as a powerful strategic hedge against vendor lock-in because its open-source nature simplifies migration compared to proprietary legacy databases.25 However, lock-in still manifests in financial and architectural forms.

• Hyperscaler Lock-in: The lock-in is primarily operational (due to proprietary storage like Aurora) and financial (due to deep, long-term CUDs/Reserved Instances that make switching expensive).9
• Cloud-Native Lock-in:
  • Neon: Exhibits the lowest vendor lock-in risk for the database component, due to its open-source commitment (Apache 2.0) and its focus purely on enhancing vanilla Postgres.1
  • Supabase: The lock-in risk is shifted to the application layer.

The Definition of Portability Shifts (BaaS vs. DB)

While the data within Supabase is standard PostgreSQL and highly portable, the platform creates significant architectural dependency through its tightly coupled services (Auth, Realtime, Storage, Instant APIs).5 When an organization chooses Supabase for rapid development, they gain speed but integrate core application logic with the Supabase APIs. If the organization decides to migrate to a Hyperscaler (e.g., AWS RDS) later, they must not only export the database but must also completely re-architect or replace the application’s reliance on Supabase Auth and Realtime services. This technical switching cost, focused on the application architecture rather than the data itself, significantly increases the barriers to exit.19 Therefore, Neon offers better strategic portability for a pure database environment, while Supabase offers better data portability but poorer platform portability.

VII. Conclusion and PostgreSQL Soulmate Recommendations

The optimal PostgreSQL hosting solution is not a singular choice but a strategic alignment of the platform’s core competencies with the organization’s current and future business objectives.

Critical Feature Matrix: Hyperscalers vs. Cloud-Native Platforms

A. The PostgreSQL Soulmate Decision Matrix

Final recommendations should be mapped directly to critical business needs:

B. Strategic Outlook 2025: PostgreSQL as the Enterprise Standard

As of 2025, PostgreSQL commands a 16.85% share of the relational database market, solidifying its position as the second most used open-source database.20 This acceleration is driven by its cloud-ready design, built-in security, and rich extension ecosystem.25 The five platforms analyzed reflect the maturity of this market:

1. Established Enterprises seeking operational excellence, compliance, and guaranteed performance consistency should remain firmly within the Hyperscaler domain, leveraging AWS Aurora or Azure/GCP for robust HA and compliance features.
2. Startups and Mid-Market Technology Companies prioritizing agility, rapid feature deployment, and innovative development workflows will find a competitive edge in the architectural innovations of Neon (for pure database and DevX) and the integrated completeness of Supabase (for full-stack velocity).
3. The decisive battleground moving forward will be AI/ML integration. Platforms that can rapidly adopt and optimize extensions like pgvector, combined with flexible architectures that handle bursty AI compute loads, will define the next generation of cloud PostgreSQL leadership.8

引用的著作

1. Why Neon? - Neon Docs, https://neon.com/docs/get-started/why-neon
2. Neon vs. Supabase: Which One Should I Choose - Bytebase， https://www.bytebase.com/blog/neon-vs-supabase/
3. Supabase vs Neon Comparison: Features, Pricing & Use Cases - Leanware， https://www.leanware.co/insights/supabase-vs-neon
4. Neon: The Next Generation OpenSource Serverless PostgreSQL | by Mehmet Cevheri Bozoğlan， https://cevheri.medium.com/neon-the-next-generation-opensource-serverless-postgresql-ab86e367627f
5. Supabase | The Postgres Development Platform.， https://supabase.com/
6. Comparing Postgres Managed Services: AWS, Azure, GCP and Supabase - PeerDB Blog, https://blog.peerdb.io/comparing-postgres-managed-services-aws-azure-gcp-and-supabase
7. Relational Database – Amazon Aurora MySQL PostgreSQL Features - AWS, https://aws.amazon.com/rds/aurora/features/
8. Neon vs Supabase: Complete PostgreSQL Platform Comparison 2025 - Vela - simplyblock, https://vela.simplyblock.io/neon-vs-supabase/
9. Neon: Branching in Serverless PostgreSQL - The New Stack, https://thenewstack.io/neon-branching-in-serverless-postgresql/
10. Announcing pgvector 0.8.0 support in Aurora PostgreSQL - AWS, https://aws.amazon.com/about-aws/whats-new/2025/04/pgvector-0-8-0-aurora-postgresql/
11. Service overview - Azure Database for PostgreSQL | Microsoft Learn, https://learn.microsoft.com/en-us/azure/postgresql/flexible-server/service-overview
12. Scaling Resources - Azure Database for PostgreSQL - Microsoft Learn, https://learn.microsoft.com/en-us/azure/postgresql/flexible-server/concepts-scaling-resources
13. Cloud SQL pricing, https://cloud.google.com/sql/pricing
14. Neon Postgres Database — Encore Docs, https://encore.dev/docs/platform/infrastructure/neon
15. Supabase vs AWS: Database Pricing Comparison in 2025 - Bytebase, https://www.bytebase.com/blog/supabase-vs-aws-database-pricing/
16. Supabase vs AWS RDS Comparison - Costs and Performance - Leanware, https://www.leanware.co/insights/supabase-vs-aws-rds
17. Pricing & Fees | Supabase, https://supabase.com/pricing
18. PostgreSQL Hosting Options in 2025: Pricing Comparison - Bytebase, https://www.bytebase.com/blog/postgres-hosting-options-pricing-comparison/
19. What is Vendor Lock-In? 5 Strategies & Tools To Avoid It - Superblocks, https://www.superblocks.com/blog/vendor-lock
20. Why is PostgreSQL adoption accelerating in enterprise IT strategies in 2025?, https://www.optisolbusiness.com/insight/why-is-postgresql-adoption-accelerating-in-enterprise-it-strategies-in-2025
21. Compare PostgreSQL vs TimescaleDB - InfluxDB, https://www.influxdata.com/comparison/postgres-vs-timescaledb/
22. PostgreSQL Extensions: Using PostGIS and Timescale for Advanced Geospatial Insights, https://www.tigerdata.com/learn/postgresql-extensions-postgis
23. Extension versions for Amazon Aurora PostgreSQL, https://docs.aws.amazon.com/AmazonRDS/latest/AuroraPostgreSQLReleaseNotes/AuroraPostgreSQL.Extensions.html
24. Best managed PostgreSQL options: Top 5 solutions in 2025, https://www.instaclustr.com/education/postgresql/best-managed-postgresql-options-top-5-solutions-in-2025/
25. Struggling with Vendor Lock-In? Why PostgreSQL is Your Exit Strategy - Medium, https://medium.com/@lovleenajeni/struggling-with-vendor-lock-in-why-postgresql-is-your-exit-strategy-a992222e15a9

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