What is CloudBoost and what are its top alternatives?
Top Alternatives to CloudBoost
Firebase
Firebase is a cloud service designed to power real-time, collaborative applications. Simply add the Firebase library to your application to gain access to a shared data structure; any changes you make to that data are automatically synchronized with the Firebase cloud and with other clients within milliseconds. ...
Parse
With Parse, you can add a scalable and powerful backend in minutes and launch a full-featured app in record time without ever worrying about server management. We offer push notifications, social integration, data storage, and the ability to add rich custom logic to your app’s backend with Cloud Code. ...
Amazon DynamoDB
With it , you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use. ...
Cloud Firestore
Cloud Firestore is a NoSQL document database that lets you easily store, sync, and query data for your mobile and web apps - at global scale. ...
Azure Cosmos DB
Azure DocumentDB is a fully managed NoSQL database service built for fast and predictable performance, high availability, elastic scaling, global distribution, and ease of development. ...
Google Cloud Datastore
Use a managed, NoSQL, schemaless database for storing non-relational data. Cloud Datastore automatically scales as you need it and supports transactions as well as robust, SQL-like queries. ...
Google Cloud Bigtable
Google Cloud Bigtable offers you a fast, fully managed, massively scalable NoSQL database service that's ideal for web, mobile, and Internet of Things applications requiring terabytes to petabytes of data. Unlike comparable market offerings, Cloud Bigtable doesn't require you to sacrifice speed, scale, or cost efficiency when your applications grow. Cloud Bigtable has been battle-tested at Google for more than 10 years—it's the database driving major applications such as Google Analytics and Gmail. ...
Firebase Realtime Database
It is a cloud-hosted NoSQL database that lets you store and sync data between your users in realtime. Data is synced across all clients in realtime, and remains available when your app goes offline. ...
CloudBoost alternatives & related posts
- Realtime backend made easy357
- Fast and responsive261
- Easy setup233
- Real-time206
- JSON184
- Free126
- Backed by google120
- Angular adaptor80
- Reliable62
- Great customer support36
- Great documentation25
- Real-time synchronization22
- Mobile friendly19
- Rapid prototyping17
- Great security12
- Automatic scaling10
- Freakingly awesome9
- Super fast development8
- Chat8
- Angularfire is an amazing addition!8
- Awesome next-gen backend6
- Ios adaptor6
- Firebase hosting5
- Built in user auth/oauth5
- Very easy to use4
- Great3
- Speed of light3
- Brilliant for startups3
- It's made development super fast3
- Low battery consumption2
- The concurrent updates create a great experience2
- I can quickly create static web apps with no backend2
- Great all-round functionality2
- Easy Reactjs integration1
- Good Free Limits1
- .net1
- Faster workflow1
- Serverless1
- JS Offline and Sync suport1
- Easy to use1
- Large1
- Push notification1
- Can become expensive26
- No open source, you depend on external company14
- Scalability is not infinite14
- Not Flexible Enough9
- Cant filter queries5
- Very unstable server3
- Too many errors2
- No Relational Data2
related Firebase posts



























This is my stack in Application & Data
JavaScript PHP HTML5 jQuery Redis Amazon EC2 Ubuntu Sass Vue.js Firebase Laravel Lumen Amazon RDS GraphQL MariaDB
My Utilities Tools
Google Analytics Postman Elasticsearch
My Devops Tools
Git GitHub GitLab npm Visual Studio Code Kibana Sentry BrowserStack
My Business Tools
Slack
We are starting to work on a web-based platform aiming to connect artists (clients) and professional freelancers (service providers). In-app, timeline-based, real-time communication between users (& storing it), file transfers, and push notifications are essential core features. We are considering using Node.js, ExpressJS, React, MongoDB stack with Socket.IO & Apollo, or maybe using Real-Time Database and functionalities of Firebase.
- Easy setup115
- Free hosting75
- Well-documented61
- Cheap48
- Use push notifications in 3 lines of code46
- Fast40
- Cloud code38
- Good for prototypes31
- Cloud modules30
- Backed by facebook27
- Parse Push7
- Cross Platform6
- Parse Core6
- Parse Analytics6
- Multiplatform5
- Quick chat and profile capabilities5
- Free Tier5
- Cloud Based4
- Geopoints3
- Free3
- Backend as a service3
- Backbone Models3
- Nice security concept3
- Local Datastore2
- Anonymous Users2
- Easy to use2
- About to Die1
related Parse posts
- Predictable performance and cost62
- Scalable56
- Native JSON Support35
- AWS Free Tier21
- Fast7
- No sql3
- To store data3
- Serverless2
- No Stored procedures is GOOD2
- ORM with DynamoDBMapper1
- Elastic Scalability using on-demand mode1
- Elastic Scalability using autoscaling1
- DynamoDB Stream1
- Only sequential access for paginate data3
related Amazon DynamoDB posts

























Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.
I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.
For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.
Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.
Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.
Future improvements / technology decisions included:
Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic
As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.
One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.
Uploadcare has built an infinitely scalable infrastructure by leveraging AWS. Building on top of AWS allows us to process 350M daily requests for file uploads, manipulations, and deliveries. When we started in 2011 the only cloud alternative to AWS was Google App Engine which was a no-go for a rather complex solution we wanted to build. We also didn’t want to buy any hardware or use co-locations.
Our stack handles receiving files, communicating with external file sources, managing file storage, managing user and file data, processing files, file caching and delivery, and managing user interface dashboards.
At its core, Uploadcare runs on Python. The Europython 2011 conference in Florence really inspired us, coupled with the fact that it was general enough to solve all of our challenges informed this decision. Additionally we had prior experience working in Python.
We chose to build the main application with Django because of its feature completeness and large footprint within the Python ecosystem.
All the communications within our ecosystem occur via several HTTP APIs, Redis, Amazon S3, and Amazon DynamoDB. We decided on this architecture so that our our system could be scalable in terms of storage and database throughput. This way we only need Django running on top of our database cluster. We use PostgreSQL as our database because it is considered an industry standard when it comes to clustering and scaling.
- Cloud Storage12
- Easy to use12
- Realtime Database10
- Easy setup10
- Super fast8
- Authentication6
- Realtime listeners6
- Could Messaging5
- Google Analytics integration4
- Adwords, Admob integration3
- Performance Monitoring3
- Crash Reporting3
- Hosting3
- Test Lab for Android3
- Sharing App via invites3
- Dynamic Links (Deeplinking support)2
- Robust ALI0
- Doesn't support FullTextSearch natively6
related Cloud Firestore posts










Fontumi focuses on the development of telecommunications solutions. We have opted for technologies that allow agile development and great scalability.
Firebase and Node.js + FeathersJS are technologies that we have used on the server side. Vue.js is our main framework for clients.
Our latest products launched have been focused on the integration of AI systems for enriched conversations. Google Compute Engine , along with Dialogflow and Cloud Firestore have been important tools for this work.
Git + GitHub + Visual Studio Code is a killer stack.
We are building a social media app, where users will post images, like their post, and make friends based on their interest. We are currently using Cloud Firestore and Firebase Realtime Database. We are looking for another database like Amazon DynamoDB; how much this decision can be efficient in terms of pricing and overhead?
- Best-of-breed NoSQL features26
- High scalability19
- Globally distributed14
- Automatic indexing over flexible json data model13
- Tunable consistency9
- Always on with 99.99% availability sla9
- Javascript language integrated transactions and queries6
- Predictable performance5
- High performance4
- Analytics Store4
- No Sql1
- Rapid Development1
- Auto Indexing1
- Ease of use1
- Pricing15
- Poor No SQL query support3
related Azure Cosmos DB posts
We have an in-house build experiment management system. We produce samples as input to the next step, which then could produce 1 sample(1-1) and many samples (1 - many). There are many steps like this. So far, we are tracking genealogy (limited tracking) in the MySQL database, which is becoming hard to trace back to the original material or sample(I can give more details if required). So, we are considering a Graph database. I am requesting advice from the experts.
- Is a graph database the right choice, or can we manage with RDBMS?
- If RDBMS, which RDMS, which feature, or which approach could make this manageable or sustainable
- If Graph database(Neo4j, OrientDB, Azure Cosmos DB, Amazon Neptune, ArangoDB), which one is good, and what are the best practices?
I am sorry that this might be a loaded question.
Hi Mohamad, out of these two options, I'd recommend starting with MongoDB (on MongoDB Atlas) for a few reasons:
• Open Source & Portability - With MongoDB being open source, you have transparency into how your system will work. Not only can you see how it works, but you later have the option to migrate to self-hosted versions of the platform (decreasing costs and avoiding vendor lock-in) or move to a Mongo-compatible hosted database like Amazon DocumentDB or Azure Cosmos DB.
• Querying & Aggregation - MongoDB has been around a few years longer than Firebase, and in my opinion, that is evident from the great design and flexibility of APIs you have for querying and aggregating data.
• Tooling - MongoDB Atlas monitoring tools and the Compass GUI are great for understanding and interacting with the data in your database as you're growing your platform.
I hope this helps!
- High scalability7
- Serverless2
- Ability to query any property2
- Pay for what you use1
related Google Cloud Datastore posts
Google Cloud Bigtable
- High performance8
- Fully managed7
- High scalability5
related Google Cloud Bigtable posts











Context: I wanted to create an end to end IoT data pipeline simulation in Google Cloud IoT Core and other GCP services. I never touched Terraform meaningfully until working on this project, and it's one of the best explorations in my development career. The documentation and syntax is incredibly human-readable and friendly. I'm used to building infrastructure through the google apis via Python , but I'm so glad past Sung did not make that decision. I was tempted to use Google Cloud Deployment Manager, but the templates were a bit convoluted by first impression. I'm glad past Sung did not make this decision either.
Solution: Leveraging Google Cloud Build Google Cloud Run Google Cloud Bigtable Google BigQuery Google Cloud Storage Google Compute Engine along with some other fun tools, I can deploy over 40 GCP resources using Terraform!
Check Out My Architecture: CLICK ME
Check out the GitHub repo attached
- Very fast1
- Poor query1
related Firebase Realtime Database posts
We are building a social media app, where users will post images, like their post, and make friends based on their interest. We are currently using Cloud Firestore and Firebase Realtime Database. We are looking for another database like Amazon DynamoDB; how much this decision can be efficient in terms of pricing and overhead?