In today’s cloud-based database landscape, choosing the right solution is a critical factor in a project’s success — especially when working with real-time data, large volumes of information and ensuring efficient data management. In this article, we compare two popular free-tier offerings: Neon serverless Postgres database and SingleStore distributed SQL database, using a real-world benchmark of 3.5 million rows.
We’ll explore their architectures, free-tier limitations and database engines, as well as performance benchmarks and key factors for selecting a fully managed, high-performance and real-time database. The comparison also covers handling customer data, supporting diverse data types and structured data and enabling real-time analytics for applications that need to perform queries on growing data volumes, or build scalable solutions. This will help you select the optimal cloud service to create a robust platform for your project requirements.
To dive deeper, you can access the full benchmark on the GitHub repository, Neon vs SingleStore: Free Plan Comparison to review the tests and run them yourself on your Free Tier account.
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Quick overview
Neon
Neon is a fully managed, serverless Postgres database platform used for modern application development. This cloud-based relational database provides compute and data storage separation with automatic scaling on its free plan, offering up to 2 vCPU/~8 GB RAM. Its features include database branching for creating instant environment clones (up to 10 branches per project), point-in-time restores with up to 24 hours of history and support for PostgreSQL extensions.
Neon can be applied for prototyping, small services and structured data workloads. It stores data, processes raw data and allows for data management across different data sources. The platform offers standard deployment options, the ability to create new instances and supports data transfer and shared data capabilities for development workflows.
SingleStore
SingleStore is a fully managed, cloud-based distributed SQL database service (SingleStore Helios®) known for delivering high performance and a unified platform that combines transactional and analytical workloads in a single HTAP engine. The Free Shared Tier provides a Starter Workspace with one attached SQL database, shared compute and up to 1 GB of compressed data storage (~4 GB uncompressed).
SingleStore is designed to handle real-time data and real-time analytics, processing both streaming and historical datasets with exceptional efficiency. Its architecture supports sensitive data, time series data and machine learning pipelines, making it highly suitable for AI applications. With a hybrid rowstore/columnstore engine and separation of storage and compute, it stores data effectively while maintaining flexibility for diverse data sources and a flexible data model.
Additional advanced capabilities include vector search with vector embeddings, Python Notebooks and NoSQL database compatibility via SingleStore Kai™, as well as simple migration from MongoDB® Atlas by updating the connection string. SingleStore supports connections from a wide range of programming languages, making it easy to integrate into different development stacks. The platform provides a rich set of developer tools, an intuitive interface and a strong focus on boosting developer productivity and developer experience. Its enterprise-grade performance in the paid plan extends these strengths for production-scale workloads.
Key differences at a glance
Architecture
Neon is a fully managed, serverless Postgres database platform with separated compute and data storage, supporting automatic scaling and database branching. SingleStore is a cloud-based distributed SQL relational database with a hybrid rowstore/columnstore engine and separation of storage and compute, optimized for real-time data processing and HTAP workloads.
Consistency
Neon, built on PostgreSQL, inherits MVCC for ACID compliance and transactional snapshots with support for point‑in‑time restore. SingleStore provides strong consistency within each partitioned shard, uniquely combining transactional and analytical workloads in a single HTAP engine — making it effective for managing time series data and sensitive data at scale.
Performance
SingleStore delivers high performance with low‑millisecond or sub‑second response times for real-time analytics and AI apps over streaming and historical datasets, along with massive ingestion throughput capabilities. Neon offers general-purpose relational database performance using the PostgreSQL engine, serving standard transactional and query workloads typical of lightweight services and cloud service applications.
Benchmark summary
Environment
Both databases were tested in their respective free plan environments.
Dataset
Table | Rows |
transactions | 3,500,000 |
users | 10 |
accounts | 10 |
transaction_statuses | 3 |
transaction_types | 3 |
This schema models a core financial system often used by financial institutions, with a large transactions table capturing 3.5 million records of monetary movements. The dataset also includes users, accounts, statuses and types as small lookup tables, providing realistic data relationships to create a consistent test environment. The total dataset size is approximately 500 MB of compressed storage, simulating real-world workloads at an entry-level data volume.
Workload
Three representative queries were executed five times each to reduce variability:
getTransactionsSum. Calculates the sum of successful transactions over the last 30 days, useful for analyzing time series data and aggregated financial data.
listTopRecipients. Identifies the top 10 recipients of successful transfers, highlighting patterns in real-time data movement across accounts.
listRecentTransactionsWithInfo. Retrieves the 100 most recent transactions from the last seven days — including user and transaction details — providing insight into time series data and user activity trends.
Result details
getTransactionsSum (a real-time analytics metric)
This query sums the total of successful transactions over the past 30 days, making it a clear example of aggregated financial time series data.
1getTransactionsSum: async () => {2 const result = await singlestore3 .select({ sum: sum(transactionsTable.amount) })4 .from(transactionsTable)5 .innerJoin(transactionStatusesTable, eq(transactionStatusesTable.id, transactionsTable.statusId))6 .where(and(gt(transactionsTable.createdAt, subDays(new Date(), 30)), eq(transactionStatusesTable.name, "success")));7 8 return +(result.at(0)?.sum ?? 0);9}
Engine | Run 1 | Run 2 | Run 3 | Run 4 | Run 5 | Avg (ms) | X faster |
1739.98 | 498.92 | 587.33 | 507.60 | 514.93 | 1.00 | ||
775.82 | 105.60 | 107.28 | 104.70 | 106.13 | 3.21 |
listTopRecipients (an analytics lookup)
Returns the top 10 recipients of successful transfers, focusing on identifying key accounts in transactional data flows.
1listTopRecipients: async () => {2 const result = await singlestore3 .select({4 accountId: transactionsTable.accountIdTo,5 count: count(transactionsTable.id).as("count"),6 })7 .from(transactionsTable)8 .innerJoin(transactionTypesTable, eq(transactionTypesTable.id, transactionsTable.typeId))9 .innerJoin(transactionStatusesTable, eq(transactionStatusesTable.id, transactionsTable.statusId))10 .where(and(eq(transactionTypesTable.name, "transfer"), eq(transactionStatusesTable.name, "success")))11 .groupBy(transactionsTable.accountIdTo)12 .orderBy(desc(sql`count`), asc(transactionsTable.accountIdTo))13 .limit(10);14 15 return result;16}
Engine | Run 1 | Run 2 | Run 3 | Run 4 | Run 5 | Avg (ms) | X faster |
612.68 | 614.42 | 545.27 | 686.89 | 610.00 | 1.00 | ||
154.47 | 95.66 | 96.35 | 95.51 | 95.70 | 5.71 |
listRecentTransactionsWithInfo (a mixed workload join)
Retrieves the 100 most recent transactions with user and transaction details from the last seven days, making it a representative time-series data workload for tracking recent activity.
1listRecentTransactionsWithInfo: async () => {2 const result = await singlestore3 .select({4 userId: usersTable.id,5 accountId: accountsTable.id,6 name: usersTable.name,7 amount: transactionsTable.amount,8 type: transactionTypesTable.name,9 createdAt: transactionsTable.createdAt,10 })11 .from(usersTable)12 .innerJoin(accountsTable, eq(accountsTable.userId, usersTable.id))13 .innerJoin(transactionsTable, eq(transactionsTable.accountIdFrom, accountsTable.id))14 .innerJoin(transactionTypesTable, eq(transactionTypesTable.id, transactionsTable.typeId))15 .where(gte(transactionsTable.createdAt, subDays(new Date(), 7)))16 .orderBy(desc(transactionsTable.createdAt), desc(transactionsTable.id))17 .limit(100);18 19 return result;20}
Engine | Run 1 | Run 2 | Run 3 | Run 4 | Run 5 | Avg (ms) | X faster |
4710.71 | 4819.69 | 4809.98 | 4810.99 | 2632.05 | 1.00 | ||
264.87 | 201.79 | 198.64 | 200.73 | 199.17 | 20.45 |
Across all three representative queries, SingleStore’s Free Tier demonstrated consistently higher performance on the 3.5 million-row benchmark. Its ability to process real-time analytics and time-series data with low latency highlights the strengths of its distributed HTAP architecture. Neon maintained stable transactional performance aligned with general-purpose relational workloads.
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Pros and cons
Neon free plan
Pros
Compute and storage separation with a serverless architecture, automatically scaling up to 2 vCPU and ~8 GB RAM for flexible development workloads.
Instant, zero‑copy branching (up to 10 branches per project) for rapid environment cloning and isolated feature testing in the same way across different environments.
Point‑in‑time restores covering up to 24 hours of historical data, useful for recovering sensitive code after experiments or errors.
Full compatibility with PostgreSQL databases and extensions, easing adoption for teams already familiar with Postgres and enabling migration from older versions.
A hobby plan designed for prototyping and lightweight services, allowing small team sizes to create and test projects with minimal operational overhead.
Cons
Resource envelope capped at 2 vCPU and 8 GB RAM, which can limit performance under heavier or more concurrent workloads.
Free plan includes up to 191.9 compute-hours per month. Autoscaling under load can quickly consume this limit, suspending compute until the next monthly cycle.
Strict storage limit; 0.5 GB of storage makes it appropriate for prototyping but unsuitable for production-scale data.
Branching limit (10 branches) and short history retention (24 h) may be insufficient for complex or long‑running testing scenarios.
Lacks built‑in real‑time analytics or HTAP capabilities, relying on Postgres’s general-purpose query performance.
Observed query latencies are higher compared to SingleStore in benchmarking (~770 ms vs ~240 ms for aggregation; ~612 ms vs ~107 ms for lookups; ~4.3 s vs ~213 ms for joins).
SingleStore Free Shared Tier
Pros
Fully managed, distributed SQL HTAP engine combining transactional and analytical workloads with a hybrid rowstore/columnstore design, and separation of storage from compute
Starter Workspace on shared compute simplifies operations by providing one attached database without management overhead, making it easy to create and test applications
Generous prototyping capacity with up to 1 GB of compressed storage (~4 GB uncompressed) for early-stage projects and small team sizes
Real-time analytics on both streaming and historical data deliver low-latency insights with strong SQL support
Integrated pipelines for seamless data ingestion and processing across different data sources
Built-in vector search capabilities to support AI/ML workloads and experimentation
Embedded Python Notebooks for in-platform data exploration and iterative learning curves
MongoDB‑compatible interfaces via SingleStore Kai, allowing straightforward migration of document-store workloads
Supports read replicas and scaling strategies in higher tiers, giving a clear path from prototyping to production
Cons
Runs on shared compute resources, so performance can fluctuate without dedicated guarantees
Restricted to a single attached database per starter Workspace
The free tier supports up to ten tables per database
Use сases
Choose Neon for:
Proof‑of‑concepts, hackathons or training. You need a minimal‑ops, hobby plan Postgres environment to accelerate experimentation, team training and demos for a small team size.
Choose SingleStore for:
Hybrid transactional and analytics applications. Run both OLTP and real‑time analytics queries in the same database, leveraging SingleStore’s hybrid rowstore/columnstore engine for sub‑second insights on current and historical data.
Streaming and batch data pipelines. Ingest, transform and analyze data seamlessly using built‑in pipelines without external ETL tools.
AI/ML experimentation with vector search. Prototype recommendation systems or semantic‑search engines by indexing embeddings directly in the database, and exploring results in embedded Python Notebooks for machine learning workflows.
MongoDB‑to‑SQL migrations. Test document‑store workloads using the MongoDB‑compatible SingleStore Kai interface to validate schema designs and performance before full migration.
Small‑scale dashboards and BI proofs‑of‑concept. Build interactive dashboards or ad‑hoc reports on up to 1 GB compressed (∼4 GB uncompressed) of data, ideal for monitoring key metrics with a high‑performance engine and no infrastructure management.
Developer learning and hackathons. Explore HTAP capabilities, SQL analytics and machine learning workflows in a zero‑ops environment with instant startup and no resource provisioning.
Access the full benchmark
Gain complete visibility into the 3.5 million‑row benchmark by cloning the GitHub repository: Neon vs SingleStore: Free Plan Comparison. This repository allows you to reproduce the tests, extend scenarios or adapt the benchmark for your free tier account.
Conclusion
SingleStore’s Free Tier demonstrates a clear performance advantage across all tested workloads. In the mixed-workload join query, it achieved an average latency of ~213 ms (about 20x faster) compared to Neon’s ~4,357 ms. For analytics lookups, SingleStore averaged ~107 ms (~5.7x faster) versus Neon’s ~612 ms. Even for aggregation workloads, SingleStore maintained an average of ~240 ms (~3.2x faster) compared to Neon’s ~770 ms.
Neon’s free tier remains a practical option for rapid prototyping on a lightweight relational database, especially for early-stage development and mobile apps with modest data requirements. However, if low-latency performance, scalability and advanced real-time analytics are key priorities for AI applications or production systems, SingleStore’s Free Tier stands out as a high-performance platform. Its ability to handle both SQL and NoSQL database workloads makes it versatile for diverse use cases and modern application architectures.
