

ClickHouse and Supabase Vector compete in the database solutions category. ClickHouse appears to hold the upper hand with its high-performance capabilities and cost-effective open-source model, though Supabase Vector offers strong semantic search integration and ease of use for startups.
Features: ClickHouse is known for its high performance in OLAP workflows, featuring a powerful optimizer, efficient real-time query performance, and seamless integration with tools like Kafka and Tableau. Supabase Vector integrates SQL with vector search for semantic abilities, providing a simple architecture, row-level security, and seamless embedding handling beneficial for recommendation systems and startups.
Room for Improvement: ClickHouse could improve its documentation, setup complexity, transactional capabilities, smoother third-party integration, and intuitive materialized view management. Supabase Vector could enhance hybrid search within its system, debugging for row-level security, ease of initial user setup, and strengthen backend features and SDK compatibility.
Ease of Deployment and Customer Service: ClickHouse is versatile in deployment across private, hybrid, and public clouds, with an emphasis on on-premises setups and community support, although enterprise-grade support primarily comes through cloud subscriptions. Supabase offers broader deployment flexibility, including hybrid clouds, with integrated support channels like Slack and community forums, though support quality in India has faced challenges.
Pricing and ROI: ClickHouse provides cost-effective benefits as an open-source solution with notable savings and high parallel execution efficiency, especially on-premises. Supabase Vector's pricing aligns with startups using a mix of free and pay-as-you-go plans, delivering value through reduced setup costs and operational streamlining. Both solutions offer significant returns, but ClickHouse excels in long-term cost efficiency through its open-source nature.
I estimate we save four to five hours per person per week due to this efficiency, translating to around 20 to 25 hours saved monthly for each individual.
We could reduce the amount of employees needed when we migrated to ClickHouse Cloud.
With ClickHouse, we didn't need to spend much on resources, cutting costs by around 25 to 30%.
The dashboard's management made access straightforward for users and super easy to maintain, resulting in very few errors.
We notice significant improvements when measuring metrics such as average response times, which have shifted from 800 milliseconds to 2.5 seconds down to around 200 milliseconds to 800 milliseconds, with click-through rates for recommendations improving by 45 to 70%.
The use of these technologies definitely impacts reducing the time and cost of implementation or deployment.
If more timely support could be provided during critical issues, situations could have been resolved much more quickly, saving considerable time.
When we faced any challenges, the ClickHouse support team provided helpful resolutions.
We utilize AVN ClickHouse, which is effectively managed by AVN, providing bug fixes and developing new functionalities along with architecture reviews.
I would rate the customer support a nine since they replied quickly and answered my questions properly, which helped me a lot.
I have always been able to solve it out with the help of my community or sometimes YouTube.
Community support from helpful developers and engineers provides fast responses on GitHub issues and community forums.
The vertical scalability is impressive, with high insert throughput, allowing millions of rows per second with low latency.
ClickHouse is highly scalable.
The scalability of ClickHouse is great.
As we move toward larger scales, such as multi-million vectors, it requires careful engineering to maintain predictable performance.
I have basically used it for small teams, not large teams that need to cover thousands of users.
Supabase Vector is highly scalable for small to medium to large scale applications.
I can confidently say that it is very consistent and stable even when handling high volume loads and real-time streaming analytics across financial and operational domains.
ClickHouse handles large volumes of data efficiently.
ClickHouse is stable, as we did not encounter stability issues in production.
From my experience, Supabase Vector is stable.
Achieving the best performance at higher scales depends largely on optimization of queries and indexes.
I basically use it for my web-coded apps and for the RAG agent and it does all of the needs that I want it to do for my project and for my client's project.
Another challenge is the lack of robust support for transactional databases, which limits its use as a primary database.
ClickHouse should be able to import data from other types of sources like Parquet and Iceberg tables and all the new upcoming data formats.
My experience with ClickHouse's documentation is that it needs improvement; I think it can be made more beginner-friendly, while the community support is really good.
Better query debugging tools and built-in evaluation toolkits for vector search would be incredibly helpful for developers.
If they could make the debugging process clearer to prevent the error messages, that will make development faster for web-coded apps.
For index optimization guidance, clearer instructions on when to use IVFFlat versus HNSW indexes would be helpful.
My experience with pricing, setup cost, and licensing indicates that it is very expensive—ClickHouse is the most expensive option.
ClickHouse is open source with no hidden fees, offering cost-effective data management.
I found ClickHouse's pricing to be efficient in comparison to other services such as Redshift.
It was amazing to be able to create all this technology for free, without the need to pay additional costs to use those technologies, apart from the embeddings ones from Google.
For now, I think the pricing is perfect because every business person can afford it and a developer can afford that price.
I utilize the free tier, which includes a 500 MB database with vector support at no cost, allowing support for millions of embeddings.
ClickHouse has reduced our storage cost and improved our 99th percentile latency by 40%.
For cost optimization, after deploying the cluster on-premises and using S3 Express, approximately 5x cost savings were achieved on data storage.
ClickHouse positively impacted our organization by absorbing the whole logging system without hassle, storing logs for six months efficiently.
We have Supabase basically as the host of most of our business relational database and user data, so since the client's applications are migrating to language model-empowered features, it is very useful, and we do not need to register for other database types.
Supabase Vector is a managed service, so I do not need to worry about scaling the database and managing the infrastructure.
Supabase Vector has positively impacted my organization by significantly reducing our testing time.
| Product | Mindshare (%) |
|---|---|
| Supabase Vector | 5.7% |
| ClickHouse | 5.7% |
| Other | 88.6% |

| Company Size | Count |
|---|---|
| Small Business | 12 |
| Midsize Enterprise | 5 |
| Large Enterprise | 8 |
| Company Size | Count |
|---|---|
| Small Business | 11 |
| Midsize Enterprise | 1 |
| Large Enterprise | 7 |
ClickHouse is renowned for its speed, scalability, and real-time query performance. Its compatibility with SQL standards enhances flexibility while enabling integration with popular tools.
ClickHouse leverages a column-based architecture for efficient data compression and real-time analytics. It seamlessly integrates with tools like Kafka and Tableau and is effective in handling large datasets due to its cost-efficient aggregation capabilities. With robust data deduplication and strong community backing, users can access comprehensive documentation and up-to-date functionality. However, improvements in third-party integration, cloud deployment, and handling of SQL syntax differences are noted, impacting ease-of-use and migration from other databases.
What features make ClickHouse outstanding?
What benefits should users consider?
ClickHouse is deployed in sectors like telecommunications for passive monitoring and is beneficial for data analytics, logging Clickstream data, and as an ETL engine. Organizations harness it for machine learning applications when combined with GPT. With the ability to be installed independently, it's an attractive option for avoiding cloud service costs.
Supabase Vector offers an efficient way to manage and query vector embeddings, catering to the needs of developers and data scientists seeking scalable solutions for vector-based data handling.
Supabase Vector is designed to streamline the process of storing, managing, and querying vector embeddings, essential for applications like machine learning algorithms and personalized recommendations. Its intuitive API and integration capabilities make it a preferred choice for tech professionals seeking a reliable backend for their vector data requirements. With flexible storage options and robust querying features, it accommodates the dynamic demands of AI-driven projects.
What are its key features?
What benefits or ROI should users look for?
Supabase Vector can be particularly beneficial in industries such as e-commerce for personalized product recommendations, in finance for fraud detection through pattern analysis, and in healthcare for patient data insights. Its capability to handle diverse sets of embeddings makes it versatile across different sectors needing robust data processing tools.
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