Alternatives to Assertible logo

Alternatives to Assertible

Postman, Runscope, Kibana, Grafana, and Prometheus are the most popular alternatives and competitors to Assertible.
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What is Assertible and what are its top alternatives?

Reduce bugs in web applications by using Assertible to create an automated QA pipeline that helps you catch failures & ship code faster.
Assertible is a tool in the API Tools category of a tech stack.
Assertible is an open source tool with GitHub stars and GitHub forks. Here’s a link to Assertible's open source repository on GitHub

Top Alternatives to Assertible

  • Postman

    Postman

    It is the only complete API development environment, used by nearly five million developers and more than 100,000 companies worldwide. ...

  • Runscope

    Runscope

    Keep tabs on all aspects of your API's performance with uptime monitoring, integration testing, logging and real-time monitoring. ...

  • Kibana

    Kibana

    Kibana is an open source (Apache Licensed), browser based analytics and search dashboard for Elasticsearch. Kibana is a snap to setup and start using. Kibana strives to be easy to get started with, while also being flexible and powerful, just like Elasticsearch. ...

  • Grafana

    Grafana

    Grafana is a general purpose dashboard and graph composer. It's focused on providing rich ways to visualize time series metrics, mainly though graphs but supports other ways to visualize data through a pluggable panel architecture. It currently has rich support for for Graphite, InfluxDB and OpenTSDB. But supports other data sources via plugins. ...

  • Prometheus

    Prometheus

    Prometheus is a systems and service monitoring system. It collects metrics from configured targets at given intervals, evaluates rule expressions, displays the results, and can trigger alerts if some condition is observed to be true. ...

  • Amazon API Gateway

    Amazon API Gateway

    Amazon API Gateway handles all the tasks involved in accepting and processing up to hundreds of thousands of concurrent API calls, including traffic management, authorization and access control, monitoring, and API version management. ...

  • Nagios

    Nagios

    Nagios is a host/service/network monitoring program written in C and released under the GNU General Public License. ...

  • Insomnia REST Client

    Insomnia REST Client

    Insomnia is a powerful REST API Client with cookie management, environment variables, code generation, and authentication for Mac, Window, and Linux. ...

Assertible alternatives & related posts

Postman logo

Postman

55.9K
44.3K
1.7K
Only complete API development environment
55.9K
44.3K
+ 1
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PROS OF POSTMAN
  • 481
    Easy to use
  • 366
    Great tool
  • 274
    Makes developing rest api's easy peasy
  • 154
    Easy setup, looks good
  • 142
    The best api workflow out there
  • 53
    History feature
  • 53
    It's the best
  • 43
    Adds real value to my workflow
  • 41
    Great interface that magically predicts your needs
  • 34
    The best in class app
  • 10
    Can save and share script
  • 9
    Fully featured without looking cluttered
  • 7
    Collections
  • 6
    Global/Environment Variables
  • 6
    Shareable Collections
  • 6
    Dead simple and useful. Excellent
  • 6
    Dark theme easy on the eyes
  • 6
    Option to run scrips
  • 5
    Awesome customer support
  • 5
    Great integration with newman
  • 4
    The test script is useful
  • 3
    This has simplified my testing significantly
  • 3
    Easy as pie
  • 3
    Simple
  • 3
    Documentation
  • 3
    Makes testing API's as easy as 1,2,3
  • 3
    Saves responses
  • 2
    API-network
  • 2
    Mocking API calls with predefined response
  • 2
    I'd recommend it to everyone who works with apis
  • 1
    Pre-request Script and Test attributes are invaluable
  • 1
    Postman Runner CI Integration
  • 1
    Now supports GraphQL
  • 1
    Continuous integration using newman
  • 1
    Easy to setup, test and provides test storage
  • 1
    Graph
  • 0
    <a href="http://fixbit.com/">useful tool</a>
  • 0
    Runner
CONS OF POSTMAN
  • 8
    Stores credentials in HTTP
  • 7
    Poor GraphQL support
  • 6
    Bloated features and UI
  • 5
    Cumbersome to switch authentication tokens
  • 1
    Expensive
  • 1
    Can't prompt for per-request variables

related Postman posts

Noah Zoschke
Engineering Manager at Segment · | 30 upvotes · 1.9M views

We just launched the Segment Config API (try it out for yourself here) — a set of public REST APIs that enable you to manage your Segment configuration. A public API is only as good as its #documentation. For the API reference doc we are using Postman.

Postman is an “API development environment”. You download the desktop app, and build API requests by URL and payload. Over time you can build up a set of requests and organize them into a “Postman Collection”. You can generalize a collection with “collection variables”. This allows you to parameterize things like username, password and workspace_name so a user can fill their own values in before making an API call. This makes it possible to use Postman for one-off API tasks instead of writing code.

Then you can add Markdown content to the entire collection, a folder of related methods, and/or every API method to explain how the APIs work. You can publish a collection and easily share it with a URL.

This turns Postman from a personal #API utility to full-blown public interactive API documentation. The result is a great looking web page with all the API calls, docs and sample requests and responses in one place. Check out the results here.

Postman’s powers don’t end here. You can automate Postman with “test scripts” and have it periodically run a collection scripts as “monitors”. We now have #QA around all the APIs in public docs to make sure they are always correct

Along the way we tried other techniques for documenting APIs like ReadMe.io or Swagger UI. These required a lot of effort to customize.

Writing and maintaining a Postman collection takes some work, but the resulting documentation site, interactivity and API testing tools are well worth it.

See more
Simon Reymann
Senior Fullstack Developer at QUANTUSflow Software GmbH · | 24 upvotes · 1.7M views

Our whole Node.js backend stack consists of the following tools:

  • Lerna as a tool for multi package and multi repository management
  • npm as package manager
  • NestJS as Node.js framework
  • TypeScript as programming language
  • ExpressJS as web server
  • Swagger UI for visualizing and interacting with the API’s resources
  • Postman as a tool for API development
  • TypeORM as object relational mapping layer
  • JSON Web Token for access token management

The main reason we have chosen Node.js over PHP is related to the following artifacts:

  • Made for the web and widely in use: Node.js is a software platform for developing server-side network services. Well-known projects that rely on Node.js include the blogging software Ghost, the project management tool Trello and the operating system WebOS. Node.js requires the JavaScript runtime environment V8, which was specially developed by Google for the popular Chrome browser. This guarantees a very resource-saving architecture, which qualifies Node.js especially for the operation of a web server. Ryan Dahl, the developer of Node.js, released the first stable version on May 27, 2009. He developed Node.js out of dissatisfaction with the possibilities that JavaScript offered at the time. The basic functionality of Node.js has been mapped with JavaScript since the first version, which can be expanded with a large number of different modules. The current package managers (npm or Yarn) for Node.js know more than 1,000,000 of these modules.
  • Fast server-side solutions: Node.js adopts the JavaScript "event-loop" to create non-blocking I/O applications that conveniently serve simultaneous events. With the standard available asynchronous processing within JavaScript/TypeScript, highly scalable, server-side solutions can be realized. The efficient use of the CPU and the RAM is maximized and more simultaneous requests can be processed than with conventional multi-thread servers.
  • A language along the entire stack: Widely used frameworks such as React or AngularJS or Vue.js, which we prefer, are written in JavaScript/TypeScript. If Node.js is now used on the server side, you can use all the advantages of a uniform script language throughout the entire application development. The same language in the back- and frontend simplifies the maintenance of the application and also the coordination within the development team.
  • Flexibility: Node.js sets very few strict dependencies, rules and guidelines and thus grants a high degree of flexibility in application development. There are no strict conventions so that the appropriate architecture, design structures, modules and features can be freely selected for the development.
See more
Runscope logo

Runscope

126
153
59
API Performance Monitoring
126
153
+ 1
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PROS OF RUNSCOPE
  • 16
    Great features
  • 15
    Easy to use
  • 4
    Nicely priced
  • 4
    Free plan
  • 2
    No install needed - runs on cloud
  • 2
    Decent
  • 1
    Collections
  • 1
    Dead simple and useful. Excellent
  • 1
    Awesome customer support
  • 1
    Import scripts from sources including Postman
  • 1
    Shareable Collections
  • 1
    Global & Collection level variables
  • 1
    Graphical view of response times historically
  • 1
    Integrations - StatusPage, PagerDuty, HipChat, Victorop
  • 1
    Run tests from multiple locations across globe
  • 1
    Schedule test collections to auto-run at intervals
  • 1
    Auto Re-run failed scheduled tests before notifying
  • 1
    Makes developing REST APIs easy
  • 1
    History feature - call history and response history
  • 1
    Restrict access by teams
  • 1
    Fully featured without looking cluttered
  • 1
    Can save and share scripts
CONS OF RUNSCOPE
    Be the first to leave a con

    related Runscope posts

    Kibana logo

    Kibana

    13.5K
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    Explore & Visualize Your Data
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    PROS OF KIBANA
    • 87
      Easy to setup
    • 61
      Free
    • 44
      Can search text
    • 21
      Has pie chart
    • 13
      X-axis is not restricted to timestamp
    • 8
      Easy queries and is a good way to view logs
    • 6
      Supports Plugins
    • 3
      Dev Tools
    • 3
      More "user-friendly"
    • 3
      Can build dashboards
    • 2
      Easy to drill-down
    • 2
      Out-of-Box Dashboards/Analytics for Metrics/Heartbeat
    • 1
      Up and running
    CONS OF KIBANA
    • 5
      Unintuituve
    • 3
      Elasticsearch is huge
    • 3
      Works on top of elastic only
    • 2
      Hardweight UI

    related Kibana posts

    Tymoteusz Paul
    Devops guy at X20X Development LTD · | 21 upvotes · 4.3M views

    Often enough I have to explain my way of going about setting up a CI/CD pipeline with multiple deployment platforms. Since I am a bit tired of yapping the same every single time, I've decided to write it up and share with the world this way, and send people to read it instead ;). I will explain it on "live-example" of how the Rome got built, basing that current methodology exists only of readme.md and wishes of good luck (as it usually is ;)).

    It always starts with an app, whatever it may be and reading the readmes available while Vagrant and VirtualBox is installing and updating. Following that is the first hurdle to go over - convert all the instruction/scripts into Ansible playbook(s), and only stopping when doing a clear vagrant up or vagrant reload we will have a fully working environment. As our Vagrant environment is now functional, it's time to break it! This is the moment to look for how things can be done better (too rigid/too lose versioning? Sloppy environment setup?) and replace them with the right way to do stuff, one that won't bite us in the backside. This is the point, and the best opportunity, to upcycle the existing way of doing dev environment to produce a proper, production-grade product.

    I should probably digress here for a moment and explain why. I firmly believe that the way you deploy production is the same way you should deploy develop, shy of few debugging-friendly setting. This way you avoid the discrepancy between how production work vs how development works, which almost always causes major pains in the back of the neck, and with use of proper tools should mean no more work for the developers. That's why we start with Vagrant as developer boxes should be as easy as vagrant up, but the meat of our product lies in Ansible which will do meat of the work and can be applied to almost anything: AWS, bare metal, docker, LXC, in open net, behind vpn - you name it.

    We must also give proper consideration to monitoring and logging hoovering at this point. My generic answer here is to grab Elasticsearch, Kibana, and Logstash. While for different use cases there may be better solutions, this one is well battle-tested, performs reasonably and is very easy to scale both vertically (within some limits) and horizontally. Logstash rules are easy to write and are well supported in maintenance through Ansible, which as I've mentioned earlier, are at the very core of things, and creating triggers/reports and alerts based on Elastic and Kibana is generally a breeze, including some quite complex aggregations.

    If we are happy with the state of the Ansible it's time to move on and put all those roles and playbooks to work. Namely, we need something to manage our CI/CD pipelines. For me, the choice is obvious: TeamCity. It's modern, robust and unlike most of the light-weight alternatives, it's transparent. What I mean by that is that it doesn't tell you how to do things, doesn't limit your ways to deploy, or test, or package for that matter. Instead, it provides a developer-friendly and rich playground for your pipelines. You can do most the same with Jenkins, but it has a quite dated look and feel to it, while also missing some key functionality that must be brought in via plugins (like quality REST API which comes built-in with TeamCity). It also comes with all the common-handy plugins like Slack or Apache Maven integration.

    The exact flow between CI and CD varies too greatly from one application to another to describe, so I will outline a few rules that guide me in it: 1. Make build steps as small as possible. This way when something breaks, we know exactly where, without needing to dig and root around. 2. All security credentials besides development environment must be sources from individual Vault instances. Keys to those containers should exist only on the CI/CD box and accessible by a few people (the less the better). This is pretty self-explanatory, as anything besides dev may contain sensitive data and, at times, be public-facing. Because of that appropriate security must be present. TeamCity shines in this department with excellent secrets-management. 3. Every part of the build chain shall consume and produce artifacts. If it creates nothing, it likely shouldn't be its own build. This way if any issue shows up with any environment or version, all developer has to do it is grab appropriate artifacts to reproduce the issue locally. 4. Deployment builds should be directly tied to specific Git branches/tags. This enables much easier tracking of what caused an issue, including automated identifying and tagging the author (nothing like automated regression testing!).

    Speaking of deployments, I generally try to keep it simple but also with a close eye on the wallet. Because of that, I am more than happy with AWS or another cloud provider, but also constantly peeking at the loads and do we get the value of what we are paying for. Often enough the pattern of use is not constantly erratic, but rather has a firm baseline which could be migrated away from the cloud and into bare metal boxes. That is another part where this approach strongly triumphs over the common Docker and CircleCI setup, where you are very much tied in to use cloud providers and getting out is expensive. Here to embrace bare-metal hosting all you need is a help of some container-based self-hosting software, my personal preference is with Proxmox and LXC. Following that all you must write are ansible scripts to manage hardware of Proxmox, similar way as you do for Amazon EC2 (ansible supports both greatly) and you are good to go. One does not exclude another, quite the opposite, as they can live in great synergy and cut your costs dramatically (the heavier your base load, the bigger the savings) while providing production-grade resiliency.

    See more
    Patrick Sun
    Software Engineer at Stitch Fix · | 11 upvotes · 431.1K views

    Elasticsearch's built-in visualization tool, Kibana, is robust and the appropriate tool in many cases. However, it is geared specifically towards log exploration and time-series data, and we felt that its steep learning curve would impede adoption rate among data scientists accustomed to writing SQL. The solution was to create something that would replicate some of Kibana's essential functionality while hiding Elasticsearch's complexity behind SQL-esque labels and terminology ("table" instead of "index", "group by" instead of "sub-aggregation") in the UI.

    Elasticsearch's API is really well-suited for aggregating time-series data, indexing arbitrary data without defining a schema, and creating dashboards. For the purpose of a data exploration backend, Elasticsearch fits the bill really well. Users can send an HTTP request with aggregations and sub-aggregations to an index with millions of documents and get a response within seconds, thus allowing them to rapidly iterate through their data.

    See more
    Grafana logo

    Grafana

    9.5K
    7.4K
    374
    Open source Graphite & InfluxDB Dashboard and Graph Editor
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    PROS OF GRAFANA
    • 79
      Beautiful
    • 65
      Graphs are interactive
    • 53
      Free
    • 52
      Easy
    • 33
      Nicer than the Graphite web interface
    • 23
      Many integrations
    • 14
      Can build dashboards
    • 9
      Easy to specify time window
    • 8
      Can collaborate on dashboards
    • 8
      Dashboards contain number tiles
    • 5
      Open Source
    • 4
      Authentification and users management
    • 4
      Integration with InfluxDB
    • 4
      Click and drag to zoom in
    • 3
      Threshold limits in graphs
    • 2
      Alerts
    • 2
      Simple and native support to Prometheus
    • 2
      It is open to cloud watch and many database
    • 2
      Great community support
    • 1
      You can visualize real time data to put alerts
    • 1
      You can use this for development to check memcache
    • 0
      Grapsh as code
    • 0
      Plugin visualizationa
    CONS OF GRAFANA
      Be the first to leave a con

      related Grafana posts

      Conor Myhrvold
      Tech Brand Mgr, Office of CTO at Uber · | 14 upvotes · 2.8M views

      Why we spent several years building an open source, large-scale metrics alerting system, M3, built for Prometheus:

      By late 2014, all services, infrastructure, and servers at Uber emitted metrics to a Graphite stack that stored them using the Whisper file format in a sharded Carbon cluster. We used Grafana for dashboarding and Nagios for alerting, issuing Graphite threshold checks via source-controlled scripts. While this worked for a while, expanding the Carbon cluster required a manual resharding process and, due to lack of replication, any single node’s disk failure caused permanent loss of its associated metrics. In short, this solution was not able to meet our needs as the company continued to grow.

      To ensure the scalability of Uber’s metrics backend, we decided to build out a system that provided fault tolerant metrics ingestion, storage, and querying as a managed platform...

      https://eng.uber.com/m3/

      (GitHub : https://github.com/m3db/m3)

      See more
      Shared insights
      on
      Grafana
      Kibana
      at

      For our Predictive Analytics platform, we have used both Grafana and Kibana

      Kibana has predictions and ML algorithms support, so if you need them, you may be better off with Kibana . The multi-variate analysis features it provide are very unique (not available in Grafana).

      For everything else, definitely Grafana . Especially the number of supported data sources, and plugins clearly makes Grafana a winner (in just visualization and reporting sense). Creating your own plugin is also very easy. The top pros of Grafana (which it does better than Kibana ) are:

      • Creating and organizing visualization panels
      • Templating the panels on dashboards for repetetive tasks
      • Realtime monitoring, filtering of charts based on conditions and variables
      • Export / Import in JSON format (that allows you to version and save your dashboard as part of git)
      See more
      Prometheus logo

      Prometheus

      2.2K
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      An open-source service monitoring system and time series database, developed by SoundCloud
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      PROS OF PROMETHEUS
      • 43
        Powerful easy to use monitoring
      • 39
        Flexible query language
      • 32
        Dimensional data model
      • 26
        Alerts
      • 22
        Active and responsive community
      • 21
        Extensive integrations
      • 19
        Easy to setup
      • 12
        Beautiful Model and Query language
      • 7
        Easy to extend
      • 6
        Nice
      • 3
        Written in Go
      • 2
        Good for experimentation
      • 1
        Easy for monitoring
      CONS OF PROMETHEUS
      • 11
        Just for metrics
      • 6
        Needs monitoring to access metrics endpoints
      • 5
        Bad UI
      • 3
        Not easy to configure and use
      • 2
        Requires multiple applications and tools
      • 2
        Written in Go
      • 2
        Supports only active agents
      • 1
        TLS is quite difficult to understand

      related Prometheus posts

      Conor Myhrvold
      Tech Brand Mgr, Office of CTO at Uber · | 14 upvotes · 2.8M views

      Why we spent several years building an open source, large-scale metrics alerting system, M3, built for Prometheus:

      By late 2014, all services, infrastructure, and servers at Uber emitted metrics to a Graphite stack that stored them using the Whisper file format in a sharded Carbon cluster. We used Grafana for dashboarding and Nagios for alerting, issuing Graphite threshold checks via source-controlled scripts. While this worked for a while, expanding the Carbon cluster required a manual resharding process and, due to lack of replication, any single node’s disk failure caused permanent loss of its associated metrics. In short, this solution was not able to meet our needs as the company continued to grow.

      To ensure the scalability of Uber’s metrics backend, we decided to build out a system that provided fault tolerant metrics ingestion, storage, and querying as a managed platform...

      https://eng.uber.com/m3/

      (GitHub : https://github.com/m3db/m3)

      See more

      We have Prometheus as a monitoring engine as a part of our stack which contains Kubernetes cluster, container images and other open source tools. Also, I am aware that Sysdig can be integrated with Prometheus but I really wanted to know whether Sysdig or sysdig+prometheus will make better monitoring solution.

      See more
      Amazon API Gateway logo

      Amazon API Gateway

      1.1K
      799
      39
      Create, publish, maintain, monitor, and secure APIs at any scale
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      PROS OF AMAZON API GATEWAY
      • 35
        AWS Integration
      • 4
        Websockets
      CONS OF AMAZON API GATEWAY
      • 1
        No websocket broadcast
      • 1
        Less expensive

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      A Luthra
      VP Software Engrg at Reliant · | 2 upvotes · 288.1K views
      Shared insights
      on
      Apigee
      Amazon API Gateway

      Amazon API Gateway vs Apigee. How do they compare as an API Gateway? What is the equivalent functionality, similarities, and differences moving from Apigee API GW to AWS API GW?

      See more
      Nagios logo

      Nagios

      759
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      Complete monitoring and alerting for servers, switches, applications, and services
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      PROS OF NAGIOS
      • 53
        It just works
      • 28
        The standard
      • 12
        Customizable
      • 8
        The Most flexible monitoring system
      • 1
        Huge stack of free checks/plugins to choose from
      CONS OF NAGIOS
        Be the first to leave a con

        related Nagios posts

        Conor Myhrvold
        Tech Brand Mgr, Office of CTO at Uber · | 14 upvotes · 2.8M views

        Why we spent several years building an open source, large-scale metrics alerting system, M3, built for Prometheus:

        By late 2014, all services, infrastructure, and servers at Uber emitted metrics to a Graphite stack that stored them using the Whisper file format in a sharded Carbon cluster. We used Grafana for dashboarding and Nagios for alerting, issuing Graphite threshold checks via source-controlled scripts. While this worked for a while, expanding the Carbon cluster required a manual resharding process and, due to lack of replication, any single node’s disk failure caused permanent loss of its associated metrics. In short, this solution was not able to meet our needs as the company continued to grow.

        To ensure the scalability of Uber’s metrics backend, we decided to build out a system that provided fault tolerant metrics ingestion, storage, and querying as a managed platform...

        https://eng.uber.com/m3/

        (GitHub : https://github.com/m3db/m3)

        See more
        Insomnia REST Client logo

        Insomnia REST Client

        508
        420
        34
        The most intuitive cross-platform REST API Client 😴
        508
        420
        + 1
        34
        PROS OF INSOMNIA REST CLIENT
        • 14
          Easy to work with
        • 9
          Great user interface
        • 5
          Works with GraphQL
        • 2
          Preserves request templates
        • 2
          Opensource
        • 2
          Cross platform, available for Mac, Windows, and Linux
        • 0
          Does not have history feature
        • 0
          Vim and Emacs key map
        CONS OF INSOMNIA REST CLIENT
        • 1
          Do not have team sharing options
        • 1
          Do not store credentials in HTTP

        related Insomnia REST Client posts

        Jason Barry
        Cofounder at FeaturePeek · | 4 upvotes · 1.8M views

        We've tried a couple REST clients over the years, and Insomnia REST Client has won us over the most. Here's what we like about it compared to other contenders in this category:

        • Uncluttered UI. Things are only in your face when you need them, and the app is visually organized in an intuitive manner.
        • Native Mac app. We wanted the look and feel to be on par with other apps in our OS rather than a web app / Electron app (cough Postman).
        • Easy team sync. Other apps have this too, but Insomnia's model best sets the "set and forget" mentality. Syncs are near instant and I'm always assured that I'm working on the latest version of API endpoints. Apps like Paw use a git-based approach to revision history, but I think this actually over-complicates the sync feature. For ensuring I'm always working on the latest version of something, I'd rather have the sync model be closer to Dropbox's than git's, and Insomnia is closer to Dropbox in that regard.

        Some features like automatic public-facing documentation aren't supported, but we currently don't have any public APIs, so this didn't matter to us.

        See more