Sold by: Tinybird
Deployed on AWS
We help developers who want to build fast APIs faster over streaming data at scale.
4.1
Overview
Public pricing below relates to our Professional plan. For custom pricing, EULA, or a private contract, please contact awsmarketplace@tinybird.co for a private offer.
Tinybird is a platform for developers who want to build fast APIs over streaming data at scale. By using Tinybird, developers are able to get products to market more quickly without worrying about infrastructure performance or scalability.
Highly Performant Build fast APIs at any scale. Even with complex queries over billions of rows, expect sub-second latency.
Lower Infrastructure Costs Simplify your stack with fewer moving parts. Tinybird is stream processing, transformation, and publication in one platform.
Production-Ready Build with SQL, and iterate with Git. Tinybird makes versioning, tests, and CI/CD deployments second nature.
Serverless Scale Scale as you need and pay as you grow. Tinybird is serverless, so you can build your APIs without worrying about infra.
Low Maintenance Focus on building, not managing infrastructure. Tinybird lets you set up a database cluster in seconds, and never worry about things like sharding or replication.
Highlights
- For more info please visit https://www.tinybird.co/
Details
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Deployed on AWS
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Pricing
Pricing is based on the duration and terms of your contract with the vendor, and additional usage. You pay upfront or in installments according to your contract terms with the vendor. This entitles you to a specified quantity of use for the contract duration. Usage-based pricing is in effect for overages or additional usage not covered in the contract. These charges are applied on top of the contract price. If you choose not to renew or replace your contract before the contract end date, access to your entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/month |
|---|---|---|
Tinybird Platform | Usage based pricing - see additional usage | $0.00 |
The following dimensions are not included in the contract terms, which will be charged based on your usage.
Dimension | Description | Cost/unit |
|---|---|---|
Tinybird Credits | Credits that can be used against spend for compute, storage, support, or any other SKU. | $1.00 |
Dimensions summary
This contract listing bills through two dimensions. Tinybird Credits are prepaid units you draw down against any charge, including compute, storage, or support. You can apply them flexibly as costs occur. The Tinybird Platform dimension is usage-based, so your charges rise and fall with actual consumption of compute and other metered resources. Pricing scales elastically based on compute, meaning you pay for the query and processing activity your workloads generate. Together, the two dimensions let you commit funds upfront through credits while the platform meters ongoing usage against that balance.
Top-of-mind questions for buyers
Credits draw down against any charge on the platform. You can apply them to compute, storage, support, or any other billed item. There is no need to allocate them in advance. As each cost occurs, it consumes credits from your balance.
Instead of throttling queries, the platform lets them burst beyond your base vCPUs. The extra compute is billed as an overage, charged per second. You can monitor vCPU usage in your organization settings to track these costs.
Pricing scales elastically based on compute, so query and processing activity usually drives the largest portion. Storage, data transfer, and other metered resources also add to the bill. All usage-based charges meter simultaneously and draw down against your credit balance.
www.tinybird.co
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Delivery details
Software as a Service (SaaS)
SaaS delivers cloud-based software applications directly to customers over the internet. You can access these applications through a subscription model. You will pay recurring monthly usage fees through your AWS bill, while AWS handles deployment and infrastructure management, ensuring scalability, reliability, and seamless integration with other AWS services.
Resources
Vendor resources
Support
Vendor support
Support Contact - support@tinybird.co
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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In Analytic Platforms, Databases & Analytics Platforms, Databases
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Sentiment is AI generated from actual customer reviews on AWS and G2
Functionality
Ease of use
Customer service
Cost effectiveness
Positive reviews
Mixed reviews
Negative reviews
AI generated from product descriptions
Sub-Second Query Latency
Executes complex queries over billions of rows with sub-second latency response times
SQL-Based API Development
Enables API construction using SQL with Git-based versioning, testing, and CI/CD deployment capabilities
Serverless Stream Processing
Provides serverless architecture for stream processing, data transformation, and API publication without infrastructure management
Automated Database Cluster Management
Supports rapid database cluster setup with automatic handling of sharding and replication operations
Unified Data Platform
Consolidates stream processing, transformation, and API publication functionality into a single integrated platform
Distributed SQL Database Architecture
Fully managed, distributed SQL database with lock-free cloud-native architecture designed for transactional (OLTP) and analytical (OLAP) workloads in a single engine
High-Throughput Data Ingestion
Parallel, distributed lock-free ingestion capable of processing millions of events per second with real-time query processing on billions of rows
Vector Search Capabilities
Indexed vector search with full-text search capabilities optimized for generative AI applications and semantic search operations
Concurrent User Scalability
Cloud-native elastic scale-out architecture supporting tens or hundreds of thousands of concurrent users with super-low latency query performance
Unified Workload Processing
Single engine capability to power high-performance transactional, analytical, and vector workloads simultaneously without requiring data movement between systems
Data Warehouse Engine
Built on ClickHouse, the platform provides a fully managed data warehouse infrastructure in the cloud.
Real-time Data Ingestion
Supports ingestion of millions of events per second from 100+ sources including Snowflake, Kafka, S3, Databricks, and direct API endpoints without requiring batching or scheduling.
SQL Query to API Conversion
Converts SQL queries into scalable API endpoints without requiring backend code or web service management.
Infrastructure as Code for Data Pipelines
Enables definition of real-time data pipelines using declarative code with version control and CI/CD integration capabilities.
Unstructured Data Processing
Automatically converts unstructured data such as documents and logs into SQL-queryable tables using AI-powered transformation.
Standard contract
No
No
Customer reviews
Akshat Sunar
Real-time data has transformed our workflows and delivers faster, more reliable insights
Reviewed on Nov 07, 2026
Review from a verified AWS customer
What is our primary use case?
Our main use case for Tinybird revolves around ensuring that real-time data processing, analytics, and API-driven data access are tracked properly. From an engineering management perspective, this platform helps us simplify the way application data is ingested, transformed, and queried in our systems. It also simplifies business outcomes and operational data, making them more accessible while maintaining performance, reliability, and scalability. It provides a SQL-based environment that helps us and our teams build data pipelines and deliver analytical insights without having to manage the heavy aspects of the underlying data structure.
Tinybird simplifies our application data ingestion and transformation through its approach, which helps streamline engineering workflows and enables our teams to focus more on business-facing functionality rather than infrastructure management. We aim to achieve faster data availability, improved query performance, reduced development overhead, and better access to operational insights.
How has it helped my organization?
The business value that Tinybird offers across engineering and infrastructure, followed by query performance and development time, has been significant. It has helped us reduce engineering hours spent maintaining data infrastructure by 73.88%. Additionally, we have improved query response times, dropping from around three to five seconds down to within milliseconds, reflecting a 78.32% improvement. There has also been a reduction in infrastructure and operational costs, which has helped us save around $300,000 a year. We have seen faster delivery of analytics capabilities, with an improvement of 57.38% in overall feature delivery to clients, and a reduction of 48.39% in the time required to develop and deploy data APIs. These metrics represent the great ROI we have achieved with Tinybird.
In our recent project, the reductions in engineering effort, query latency, infrastructure costs, and analytics API development time have given an overall edge to our delivery teams and engineering teams. Overall productivity and throughput have increased significantly. The engineering teams feel more aligned with the working culture since they can contribute more with less complexity. Moreover, the engineering team's collaboration has increased by 40% because they have more time as the overall effort was reduced.
What is most valuable?
I have multiple favorite features of Tinybird that stand out to me. The first is real-time data processing, which enables timely access to operational and analytical information. Second, we love the SQL-based transformation, which makes data manipulation familiar and accessible to our engineering teams. Third, API development simplifies the exposure of analytical queries as an API for application consumption. Fourth, the data pipelines help us organize ingestion and transformation workflows. Tinybird supports data-intensive applications and growing analytical workloads by reducing the engineering effort involved in building custom analytics features. I really appreciate the combination of SQL-based development and API delivery, which creates a straightforward workflow for the engineering teams.
I feel that Tinybird's data governance and data security are on point and compliant with HIPAA and ISO certification guidelines, so there are no challenges on that part.
The accuracy and reliability of Tinybird's AI outputs are really great. I have not seen any problems or challenges in the AI outputs; they are pretty good and on point, without any hallucinations so far.
What needs improvement?
I feel Tinybird is doing pretty great, and there is no improvement that comes to my mind for now.
I believe everything is alright, and no enhancement opportunities come to my mind since we have seen some really good results after using Tinybird.
For how long have I used the solution?
We have been using Tinybird for close to one year and seven months.
What do I think about the stability of the solution?
Tinybird is very stable, with no challenges or breaches regarding the overall stability of the solution.
What do I think about the scalability of the solution?
Tinybird's scalability, when dealing with increasing data volumes and application users, makes it a very interesting platform for supporting growth and analytical workloads. The ability to build data pipelines and expose query results through an API is very useful whenever we need to consume analytical information efficiently. It can definitely support 10 to 20 million users at a time without any problems or latency issues.
How are customer service and support?
Customer support seems to be really great; we contacted them six months ago, and they resolved our query within 45 minutes. I would rate them 10 out of 10 for customer support.
How was the initial setup?
Regarding the deployment of Tinybird, I feel the initial deployment is pretty straightforward, and we are able to deploy the overall service within our architecture in less than a week, with no challenges in terms of deployment.
Tinybird integrates seamlessly with the other AWS services we use, with no challenges or glitches, resulting in a solid experience on that part.
What about the implementation team?
We find the configuration process easy, with no external support needed; it is configured within three days.
What was our ROI?
I feel that I have already shared our return on investment metrics.
What's my experience with pricing, setup cost, and licensing?
The metering and billing experience seems pretty good; we are charged monthly. We paid on a pay-as-you-go plan rather than by the seat.
Pricing, setup costs, and licensing seem good, with no challenges or surprises. Everything was well-versed while configuring the solution for our organization.
Which other solutions did I evaluate?
Before choosing Tinybird, we did not particularly evaluate any other options; Tinybird's team offered us the solution directly, and after a meeting, our board members liked it. That is why we are just using it for now.
What other advice do I have?
I would rate Tinybird a five on a scale of one to five.
I feel that Tinybird is doing really great in all aspects, which is why I give it a five.
My advice to anyone looking into using Tinybird is that they should definitely consider it when evaluating an exceptional solution for their mid-sized to enterprise-sized organization.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)
reviewer2912532
Analytics filters have powered rich URL insights but have also exposed speed and AI limits
Reviewed on Oct 16, 2026
Review provided by PeerSpot
What is our primary use case?
My main use case for Tinybird is gathering statistics and analytics for the projects. For example, our users enter any specific URL and using Tinybird, we understand their IP addresses, their country, city, device, and we gather this kind of analytics.
I can add that we use filters to understand the period of the URL activity from start to end. We can get analytics for the last 12 months, and its query filters are good and they suit our product.
How has it helped my organization?
Tinybird has impacted my organization positively as we could use a different library, but Tinybird's speed is quite fine, so it helps us to do everything very quickly.
Regarding how the speed has made a difference for my team or business, we compared Tinybird requests with regular SQL requests, and Tinybird is around two times faster.
What is most valuable?
The best feature Tinybird offers is the filter ability with query params.
The query parameter flexibility helps me in my day-to-day work as we use it for simple things such as filtering names, dates, specific countries, and devices. We use the data and convert it to show up in our chart areas, in our pie charts, and in our bar charts, so it is simple to do it with Tinybird.
What needs improvement?
Tinybird can be improved by simplifying it and having more AI features in the future. It can also be much faster because sometimes it can still be a bit laggy.
You can understand it better than me if you start to analyze the things that I mentioned regarding needed improvements.
For how long have I used the solution?
I have been using Tinybird for about one year.
What do I think about the stability of the solution?
Regarding Tinybird's AI capabilities, I think it can sometimes be laggy or sometimes incorrect, but at least it helps to handle a lot of requests, especially if they contain many different filters and parameters.
How was the initial setup?
In my organization, Tinybird is deployed using Vercel.
What other advice do I have?
My advice to others looking into using Tinybird is that if their product contains many specific filters inside and those filters also need some calculations for the database, Tinybird can be a suitable choice.
Regarding Tinybird's AI capabilities, if an AI prompt can make regular query params and send to Tinybird, I do not think there would be any governance issues.
To clarify, Tinybird is deployed on a private cloud in my organization, though I do not exactly know. I would rate Tinybird overall around six or seven because I think it can be faster.
Rose S.
Limited Customization Features
Reviewed on Jan 25, 2024
Review provided by G2
What do you like best about the product?
The platform is not user friendly and intuitive. It lacks to provide performance enabling real time processing and analysis of data. The wide range of connectors and integrations is not impressive making it a breeze to connect with data sources.
What do you dislike about the product?
The limited integrations with platforms hinder the flow of data and collaboration efforts. The lack of scalability and customization options restrict its adaptability to diverse business needs. New users may find it difficult to navigate and fully utilize the features due to the learning curve and complexity of the interface.
What problems is the product solving and how is that benefiting you?
The pricing structure lacks transparency, which is costly, for businesses. Tinybird unable to solve the problems of handling and analyzing datasets in time providing us with instant insights that were previously unattainable. This has decreased my business growth.
Frank B.
Problem of real time data processing
Reviewed on Jan 22, 2024
Review provided by G2
What do you like best about the product?
The platform is less user friendly and not intuitive. It makes analyzing and visualizing data difficult. The performance is not well organized, with slow queries and poor real time updates. The APIs are less flexible not allowing me to integrate with tools.
What do you dislike about the product?
The documentation is not providing instructions and examples. The support team is not always quick to help resolve any issues. Now onto the things I wish need to be improved about Tinybird. While it has benefits, one area that needs improvement is its documentation. It's not as comprehensive as it could be which makes it a bit difficult for new users to navigate and fully utilize.
What problems is the product solving and how is that benefiting you?
The platform's limited integrations make it more difficult to integrate data from sources for analysis. I feel that the user interface needs to be more user friendly as it currently requires some time and effort to navigate and use.
Bhavya J.
Building real-time analytics made simple with Tinybird
Reviewed on Jan 17, 2024
Review provided by G2
What do you like best about the product?
It is easy to integrate with other apps like Confluent Cloud and develop real-time dashboards that provide accurate analytics. It has a simple and easy-to-use interface and turns data into API endpoints, making it easy to scale data warehouses. It is also good for shipping products faster with its CI/CD pipeline implementation. It also offers a powerful testing playground for testing APIs and their functionality.
What do you dislike about the product?
I have been frequently using it for quite some time now and the issue that I faced was with its CLI as it becomes blank and does not show anything, and we have to terminate it and restart the process in some cases.
What problems is the product solving and how is that benefiting you?
It helps in making analytics simple as we have centralized large data sets and multiple teams from different departments use that for their needs to generate analytics. It also makes it easy to scale and provides great performance. Now we do not have to use multiple tools for data analytics and API design, which saves a lot of time and increases productivity to work on real problems.