The Cost Of Firebase Alone Should Steer You Away from Firebase, and Also Supabase . Self Hosted PostgreSQL is Thousands Of Times Less Expensive, and You Are Not Trapped In Google Prison.
Let’s Say Your Website / App Starts Getting Considerable Traffic, Which of Course is The Goal of Every Website / App Owner.
Here’s an extreme example – a cost comparison table of a Firebase and Supabase cost estimate including a calculated bandwidth cost for 0.3 Gbit/s continuous traffic (~810 TB/month – huge company or website):
| Component | Firebase Cost Estimate | Supabase Cost Estimate | Notes |
|---|---|---|---|
| Database Storage | 300 GB × $0.026 = $7.80 | Included in $250/month plan (approximate) | Storage cost roughly comparable |
| Bandwidth (0.3 Gbit/s ≈ 810 TB/month) | 810 TB × $0.12/GB = $97,200 | 810 TB × $0.10/GB = $81,000 | Firebase charged $0.12/GB, Supabase estimated $0.10/GB over included bandwidth |
| Total | $7.80 + $97,200 = $97,207.80 | $250 + $81,000 = $81,250 | Supabase total includes storage + bandwidth |
Notes:
- Supabase base plan with 300GB storage roughly estimated at $250/month (actual pricing depends on plan and region).
- Bandwidth cost for Supabase assumed $0.10 per GB beyond included limits to cover high traffic volume; check with Supabase for precise enterprise pricing.
- Firebase bandwidth pricing at $0.12/GB is generally higher, resulting in a much larger total cost.
- They lure you win with lost cost of storage, then as soon as you get traffic, you may need to get a 2nd mortgage!
Lets compare that to hosting a dedicated server with PostgreSQL to deliver that same traffic:
In July of 2025 you can get a locked in price of $100 per month for a dedicated server that more than meets these demands!
The $100.month server, with Intel Xeon Silver 4123 (8 cores/16 threads, base 3.0 GHz, turbo 3.2 GHz), 96 GB DDR4 ECC RAM, and 4 TB RAID 1 SATA storage, is quite capable of handling 300 GB of database storage with a sustained 1 Gbit/s network bandwidth.
Key points:
- CPU: The Xeon Silver 4123 is a server-grade processor designed for multi-threaded workloads and can handle database operations efficiently, though it is not top-tier high-end but solid for enterprise-grade applications.
- Memory: 96 GB RAM is ample for caching, query performance, and running database workloads alongside application services.
- Storage: 4 TB RAID 1 on SATA drives offers redundancy and enough capacity. SATA speed is slower than NVMe but should be sufficient for many workloads at this scale unless extremely high IOPS are needed.
- Network: 1 Gbit/s unlimited throughput supports sustained traffic well beyond the 0.3 Gbit/s you mentioned, offering room for growth.
Given these specs, this $100 a month server can indeed handle the storage and bandwidth demands equivalent to 300 GB database size and continuous 0.3 Gbit/s traffic without significant bottlenecks, assuming properly configured database software and network stack.
This avoids the extremely high bandwidth costs from managed cloud providers like Firebase or Supabase but requires self-management and maintenance. Find a smart server tech!
In summary, your dedicated server hardware configuration is well-suited to support the database size and bandwidth requirements you discussed and should perform robustly for such workloads.
Supabase just hosts PostgreSQL which is open source, so you can put it on your server for free! PostgreSQL is far superior to Google Firebase, which Google proprietary code. Firebase is like Kindergarden, Firebase is a Masters Program. And then, the big kick in the ass…..
A huge MINUS to Firebase is NOBODY ELSE HOSTS the lame ass database NO-SQL format, so if you create databases on Firebase, you are stuck with them for LIFE. If your app starts getting any traffic you will PAY dearly. You cannot export the data into any other database and you cannot move it to your own server and save $100 grand a month (extreme example above). When the price increases come, you are stuck with them.
This is a disgusting “Do not leave google, do not save money” TRAP. WARNING WARNING WARNING! Then, we find out Firebase is crap compared to PostgreSQL:
Here is a list of Firebase’s negatives compared to PostgreSQL:
- Limited complex querying capabilities: Firebase Realtime Database and Firestore lack advanced query features like complex filters, multi-table joins, aggregations, and ad hoc reporting which PostgreSQL supports extensively through SQL.
- Vendor lock-in: Firebase is a fully managed service hosted only on Google Cloud Platform, creating dependence on Google’s ecosystem, APIs, pricing, and policies. This makes migration to other platforms difficult and costly.
- No direct backend connections: Firebase is mostly accessed via client-side SDKs, which can expose database access logic to clients and complicate secure access control compared to PostgreSQL’s backend server connections.
- Cost model based on reads/writes and bandwidth: Firebase pricing is usage-based, primarily charging for document reads, writes, and bandwidth, which can become expensive under high traffic or read-heavy workloads, unlike typical fixed-rate PostgreSQL hosting.
- More difficult triggers and cascading deletes: Firebase has limited or no native support for database triggers or cascading deletes. Achieving similar functionality requires manual work or Firebase Functions, unlike PostgreSQL with powerful native stored procedures and triggers.
- Migration challenges: Migrating data out of Firebase is complex due to schema-less, JSON-style data formats, lack of standard export tools, and proprietary APIs, whereas PostgreSQL supports standard SQL and mature data export/import utilities.
- Lack of built-in detailed reporting and auditing tools: Firebase offers minimal native auditing, logging, and reporting capabilities, making security audits, compliance tracking, and incident forensics harder to implement compared to PostgreSQL’s mature audit logging.
- Limited support for complex data analysis and large-scale reporting: The absence of complex queries restricts running advanced analytics or aggregated reports inside Firebase, often necessitating external data pipelines or exports.
- Security concerns due to absence of granular reporting and auditing: Without thorough audit trails and monitoring, it is challenging to detect unauthorized access patterns or data breaches, leading to compliance risks with regulations needing detailed logs.
- Limited flexibility and control over infrastructure: Being a fully managed platform means users cannot customize, optimize, or extend the underlying database infrastructure as freely as with PostgreSQL self-hosted or cloud-hosted instances.
- Potential performance and cost inefficiency at scale: While Firebase excels at real-time syncing for mobile/web apps, managing very large datasets or complex operations at scale can be costly and less performant than PostgreSQL’s optimized query engine.
- Limited ecosystem outside of Google tools: Development and operational tooling around Firebase is less diverse than the wide ecosystem supporting PostgreSQL, which benefits from multiple third-party integrations and cross-cloud support.
Read why AICoder technology apps and their programmers prefer PostgreSQL.
Firebase lacks a standard query language similar to SQL, which makes creating custom reports difficult. Firebase databases, such as Realtime Database and Firestore, use proprietary NoSQL query APIs that are limited in complexity and do not support advanced features like multi-table joins, aggregations, or ad hoc querying typical in SQL systems. As a result, reporting directly on Firebase data is constrained to the simple querying capabilities built into its APIs.
Some key points highlighting this challenge include:
- Firebase does not provide built-in, comprehensive reporting tools or standard query languages for flexible reporting, unlike SQL databases where tools like Crystal Reports or standard SQL queries are common practice.
- For small datasets, users often manually export JSON data from Firebase and perform offline analysis, which is impractical for larger datasets or complex reports.
- For larger datasets, Firebase offers background JSON dumps via backups, but pulling complex or customized reports requires additional tooling or moving data to external analytics platforms, adding complexity and latency.
- The lack of a uniform query language means each Firebase database type has its own querying method, none of which are as expressive or standardized as SQL, making complex data retrieval and reporting more cumbersome.
- This limitation impacts real-world use cases requiring advanced analytics and reporting within the database, leading many to export data into SQL-based warehouses or separate tools for such purposes.
In summary, Firebase’s architectural choice to use NoSQL proprietary query APIs without a standard query language like SQL directly results in difficulty building custom reports, especially those involving complex queries, aggregations, or multi-condition filtering. Users often must employ data export and external analytics tools to achieve the desired reporting functionality.
This limitation contrasts notably with PostgreSQL and similar relational databases where rich, standardized SQL queries and mature reporting ecosystems support flexible, complex reporting and analysis out of the box.
Being able to run queries for a variety of tasks, one of which is building reports mentioned above, is a very important need for valuable web applications. If you use any of the new aicoders, they will be building queries into the application that you will be completely unaware of!
STOP: WHO CARES ABOUT ANY OF THIS. THE MOST IMPORTANT THING YOU HAVE LEARNED IS IF YOU USE FIREBASE FOR YOUR DATABASE YOU ARE LOCKED IN GOOGLE PRISON FOR LIFE, AND WILL PAY VERY DEARLY WHEN YOUR WEBSITE OR APP GETS TRAFFIC. ROACHES CHECK IN BUT CANNOT CHECK OUT. THEY OWN YOU. YOU HAVE BEEN WARNED. IF YOU SAY, WELL I WILL WAIT UNTIL I GET TRAFFIC THEN REWRITE MY ENTIRE APPLICATION AND DATABASE, THEN YOU ARE NOT PAYING ATTENTION! Firebase is like the land of misfit toys.
Not that it should influence your decision to not use Firebase due to everything above, here is some info on Firebase NoSQL,
NoSQL stands for “Not Only SQL” and refers to a category of databases designed to store and manage data differently from traditional relational SQL databases. Unlike SQL databases which use rigid tabular structures with pre-defined schemas, NoSQL databases store data in flexible, often schema-less formats such as documents (JSON), key-value pairs, wide-columns, or graphs. This flexibility allows them to handle unstructured or semi-structured data efficiently.
NoSQL databases are optimized for horizontal scalability, meaning they can distribute data across multiple servers to handle large volumes and high traffic better than traditional SQL systems. They often sacrifice some strict transactional guarantees (like ACID properties) in favor of improved performance, scalability, and ease of development for modern applications such as real-time web apps, big data, and mobile platforms.
Regarding NoSQL queries, instead of using the standardized SQL language, NoSQL databases use different, often more flexible APIs or query methods depending on the data model. For example, document databases like MongoDB query JSON-like documents, while key-value stores access data via specific keys.
In summary:
- NoSQL means databases that are not strictly relational and allow more flexible data models.
- They support scalable, high-performance storage of large, unstructured or semi-structured data.
- NoSQL queries differ by database type and are typically not SQL but use model-specific query mechanisms.