Top 7 AI Tools for API Testing
Published
December 12, 2024
19
min read
Manual API testing takes up too much time. On average, U.S. development teams spend 11+ hours weekly maintaining API tests. That’s why AI-powered tools are becoming essential. They automate repetitive tasks, improve accuracy, and adapt to changes in your APIs.
Here’s a quick overview of 7 AI tools that can help you save time, reduce costs, and deliver better software faster:
| Tool | AI Features | Supported APIs | Best For | Key Strength |
|---|---|---|---|---|
| ACCELQ Autopilot | Self-healing, predictive analytics | REST, SOAP, custom | Enterprise teams | Codeless automation |
| Katalon Studio | AI test generation, NLP | REST, SOAP, GraphQL | Teams of all sizes | Strong community support |
| Testsigma | NLP, AI-driven maintenance | REST, SOAP, GraphQL | Non-technical teams | Plain-English test creation |
| mabl | Auto-healing, ML insights | REST, GraphQL | Reliability-focused teams | Unified testing platform |
| Test.ai | Codeless AI, failure prediction | REST (via apps) | Mobile-first teams | Visual testing with AI |
| Tavern | YAML-based automation | REST | Python teams | Simple and configurable |
| Chakram | No AI, promise-driven | REST (JSON endpoints) | JavaScript developers | BDD-style testing |
These tools can cut testing time by up to 50% and improve productivity by 30%. Choose the one that fits your team’s skills and workflows to streamline your API testing process.
ACCELQ Autopilot takes the spotlight as a game-changing platform for API testing. Powered by AI, it completely transforms the testing process, automating everything from discovery to analysis without relying on traditional script generators.
What sets Autopilot apart is its ability to learn your application and instantly generate executable test cases. Back in April 2025, ACCELQ introduced major updates to Autopilot, pushing the boundaries of autonomous testing. CEO Mahendra Alladi shared the company’s ambitious vision:
"With ACCELQ Autopilot, we have embarked on an ambitious journey to deliver agile testing with autonomous test automation that adapts to application changes. Autopilot is self-driven, and leverages AI for execution resilience focused on boosting productivity and accelerating time to market for software applications."
ACCELQ Autopilot supports a wide range of protocols, including REST, SOAP, and custom protocols, ensuring comprehensive API testing coverage. Its standout features include API chaining and regression suite capabilities, enabling seamless end-to-end validation that integrates both API and UI testing into a single workflow.
The platform’s codeless interface simplifies test creation by transforming plain English into test cases. It automatically connects the dots between front-end, back-end, APIs, and middleware, streamlining the entire process.
One of its most powerful capabilities is autonomous healing, which uses semantic attributes instead of static locators. This feature allows the system to adapt to changes in APIs, drastically cutting down on maintenance efforts and ensuring resilience as applications evolve.
All of these features integrate effortlessly into CI/CD workflows, making continuous delivery smoother and more efficient.
Early adopters of ACCELQ Autopilot have reported outstanding results. Companies have achieved a 65% reduction in test creation time and an 80% drop in maintenance efforts. On top of that, the platform delivers 7.5x faster automation, 72% lower maintenance costs, and an overall 53% cost reduction.
These impressive outcomes underline how Autopilot enables businesses to speed up releases while maintaining high-quality standards through continuous testing.
ACCELQ Autopilot integrates effortlessly with CI/CD pipelines, providing continuous testing capabilities and offering simple change impact analysis for API test suites.
For more complex scenarios, the platform supports messaging services like Apache Active MQ and TIBCO. It also incorporates OAuth 2.0 for access control and tenant policies to ensure security and compliance. Additionally, it includes execution tracking, full visibility, and defect-tracking integrations, delivering a comprehensive approach to test management.
Guljeet Nagpaul, Chief of Products at ACCELQ, highlights what makes Autopilot stand out:
"In today's GenAI landscape, many solutions claim to revolutionize testing but merely generate code snippets that create more problems than they solve. Autopilot's agentic approach leverages our modular design to create automation that truly scales and adapts to your changing applications."
Building on the momentum of ACCELQ's approach, Katalon Studio delivers another robust, AI-powered solution for API testing. Known for its intuitive interface, Katalon Studio reduces the need for extensive coding, making it accessible to teams with varying levels of technical expertise while enabling advanced automation.
One of Katalon Studio's standout features is its AI-powered API Test Generation, introduced in version 9.6.0 (October 2024). This beta functionality automates the creation of test cases directly from OpenAPI and Swagger specifications, saving significant manual effort. The AI engine can also suggest test cases based on user interface behavior and automatically adjust tests to accommodate detected changes.
The platform's use of Natural Language Processing (NLP) simplifies test script creation. By inputting plain English descriptions, users can generate executable test scripts automatically. Additionally, its AI-driven visual testing feature compares screenshots across different builds to identify visual inconsistencies that could impact API performance.
Katalon Studio supports major API protocols, including REST, SOAP, and GraphQL. It streamlines workflows by enabling the easy import of test cases from tools like Swagger and Postman. For CI/CD pipelines, Katalon integrates with Jenkins and Bamboo, automating test execution whenever code changes are pushed. Its TestOps component further enhances integration by supporting third-party tools for project management, test execution, and CI/CD processes.
Katalon Studio excels in delivering efficiency in API automation. For instance, it can validate over 150 data combinations in just 1.5 to 2 hours - a process that might otherwise take several days with manual UI testing. Companies using Katalon have reported testing up to 80% of data combinations in under 2 hours, compared to 8–10 hours for equivalent UI regression testing.
The platform has driven remarkable outcomes for its users. Liberty Latin America, for example, increased its regression test coverage by 60% while halving the time spent on regression testing. SAGA, an insurance company, tripled its testing capacity and reduced testing time from 2 days to just 2 hours. Similarly, Care Logistics automated over 8,000 manual test cases, cutting its regression testing cycle by 50%, and Tunaiku reduced scripting time by 60%.
Diana Cazacu, Quality Manager at GE Digital, highlights the platform's impact:
"Katalon offers everything we ever needed, and more."
The Katalon team also emphasizes the importance of AI in API testing:
"The need for a more efficient method to create API test cases is evident... AI can help achieve higher test coverage and speed up the process."
Katalon Studio is available in both free and paid versions. The Enterprise edition includes advanced AI/ML capabilities and specialized plugins. The platform supports parallel test execution and offers a built-in reporting feature to centralize test results across tools. Its data-driven testing approach integrates with external sources like Excel, CSV files, and databases, enabling thorough test coverage without relying on hardcoded values. This reduces redundancy and simplifies maintenance.
Up next, we’ll take a closer look at Testsigma and its role in advancing AI-driven API testing.
Testsigma takes API testing to the next level with its cloud-first, collaborative approach. By combining web, mobile, and API testing into a single platform, it eliminates the need for multiple specialized tools. This all-in-one setup simplifies workflows and enhances testing capabilities, laying the groundwork for its powerful AI-driven features.
Testsigma Copilot streamlines test creation with automation. Powered by GenAI, it generates test cases directly from requirements, cutting down on the delays often associated with manual test writing. These AI agents handle everything - from creating and running tests to analyzing results and maintaining test cases - all in just minutes.
Using Natural Language Programming, Testsigma allows users to write tests in plain English. This approach makes testing accessible to those without coding expertise, while still delivering the precision needed for enterprise-grade API validation.
Rukmangada Kandyala (KR), Founder & CEO of Testsigma, shares the company’s mission:
"Letting our customers automate and test APIs is a critical step towards our vision of building the Operating System for QA. Software quality should not be a function of coding expertise, but a result of curiosity and an empathetic understanding of user behavior. We truly believe Testsigma is ushering a new way of testing using natural language to deploy quality software, and we are already seeing thousands of empowered QA teams help deploy software 5 times faster with increased confidence."
Testsigma supports a range of protocols, including REST, SOAP, GraphQL, and gRPC, with real-time validation of expected responses. For REST APIs, it thoroughly tests HTTP methods like GET, POST, PUT, DELETE, and PATCH. Any discrepancies are flagged immediately with detailed reports, enabling teams to address issues faster.
Teams using Testsigma report impressive results: 80% time savings and 100% test coverage thanks to its AI-driven features. The platform delivers 10x faster test execution and reduces test maintenance efforts by 90%. Additionally, Testsigma Copilot can cut overall test cycles by 25%.
Bharathi K highlights the impact:
"Testsigma has been an absolute game-changer for us. We've saved time and caught critical issues that would've been missed with manual testing."
Testsigma integrates effortlessly with tools like JIRA, GitHub, Sauce Labs, and Slack, enabling smooth collaboration and streamlined defect management throughout the development process. With support for parallel test execution, it provides quick feedback to development teams, reducing overall testing time.
One standout feature is its adaptive maintenance, which automatically updates tests as APIs evolve. This self-healing capability eliminates the need for manual updates, addressing one of the most tedious aspects of traditional API testing.
With flexible pricing options - including a free tier for teams starting out - and plans like Basic, Pro, and Enterprise, Testsigma caters to a wide range of needs. Its high user rating of 4.5 and enterprise-grade security make it a trusted choice for organizations of all sizes.
Up next, we’ll dive into mabl and its machine-learning-driven approach to API testing.
mabl takes an AI-driven approach to API testing, automating processes at every stage to create a unified and efficient testing experience.
With mabl, generating tests becomes a breeze. The platform can automatically create tests directly from API documentation formats like Swagger, OpenAPI, and Postman collections. It even accepts plain English instructions to generate custom pre- and post-request JavaScript code. On top of that, mabl’s AI organizes reusable steps with intuitive flow naming, making it easy to retrieve and reuse them when needed.
If you're a Postman user, mabl simplifies your workflow by offering a one-click import for Collections and environment variables. This feature significantly speeds up automated API testing. Considering that 92% of developers and API professionals plan to maintain or expand their API investments in 2024, this integration becomes even more valuable.
Dale Cook, VP of Product Engineering, highlights the platform’s efficiency:
"Mabl easily integrates with our workflows and CI/CD pipelines so that developers can run really robust tests without using a lot of engineering resources."
mabl combines API, web, and mobile testing within a single platform. This unified approach allows teams to reuse common sequences - like authentication, test data setup, and cleanup - across different tests. It also integrates seamlessly with popular CI/CD tools like Jenkins, Bamboo, and TeamCity, ensuring smooth adoption into existing workflows.
mabl’s low-code interface is designed to empower team members of all technical skill levels. Its visual design environment simplifies tasks like managing variables and handling data-driven scenarios, eliminating the need for extensive coding expertise. This makes it easier for teams to scale their quality assurance efforts effectively.
Janet Bracewell, Senior Engineering Manager for QA, shares her experience:
"Mabl allows our team to focus on improving our product and user experience",
This statement underscores how mabl enables testers to scale automation without losing sight of enhancing the overall user experience. Up next, we’ll take a look at Test.ai and its unique approach to intelligent test automation.
Test.ai uses artificial intelligence to simplify and speed up test creation and maintenance, eliminating the need for complex scripting. By automating tedious API testing, it transforms what can be a slow and error-prone process into something faster and more reliable.
With Test.ai, creating tests no longer requires hours of manual coding. Instead, the platform's AI engine automatically generates and maintains tests, making API testing accessible to everyone on the team, regardless of their coding skills.
The AI analyzes API behaviors and builds detailed test scenarios. This means teams can spend less time on technical setup and more time focusing on strategic testing decisions. It's a shift that allows for smarter, more efficient workflows.
Test.ai isn’t just about simplicity - it’s built to handle large-scale, enterprise-grade systems. Its AI-driven approach adapts in real time to complex API interactions, ensuring even the most intricate relationships are thoroughly tested.
Digital Marketer Abhaya emphasizes the impact of AI in testing:
"AI is transforming software quality assurance by enhancing the efficiency and precision of automation testing."
The benefits of this transformation are clear. For example, a major e-commerce company that adopted an AI-powered API testing solution reported a 20% improvement in test coverage. This kind of result highlights how AI can elevate testing to new levels of accuracy and efficiency.
Test.ai doesn’t stop at generating tests - it also analyzes data to uncover test cases that might otherwise be missed. By examining API interactions from multiple perspectives, the platform ensures more thorough testing and higher software quality.
This capability is particularly valuable when dealing with complex systems and large data volumes. The AI identifies potential issues early, before they can impact production, and its intuitive interface makes it easy for teams to manage even the most comprehensive test coverage.
One of Test.ai's standout features is its user-friendly design. Both technical and non-technical team members can easily contribute to the testing process, thanks to its straightforward interface. This inclusivity enables organizations to tap into the expertise of their entire team, without requiring extensive programming knowledge.
This ease of use is especially important for companies looking to expand their testing efforts without investing heavily in additional training. It allows teams to work more collaboratively and effectively, ensuring better outcomes across the board.
Next, we’ll take a closer look at how Tavern approaches automation in its own unique way.
Tavern is a Python-based framework designed to make API testing straightforward and efficient. While many tools lean on AI to handle intricate tasks, Tavern takes a different route by focusing on simplicity through its clean, Python-driven methods. Its YAML-based structure, pytest integration, and ability to manage complex workflows make it a standout choice for API testing.
With Tavern, tests are defined using YAML files. This format ensures that test cases are both easy to read and simple to maintain. Even team members who aren't deeply familiar with programming can quickly grasp and adjust these tests. This means less time spent untangling complicated test scripts and more time dedicated to ensuring APIs work as they should.
Tavern works seamlessly as a pytest plugin, supporting both RESTful and MQTT-based APIs. Tests can be run using the familiar pytest command, or they can be incorporated into CI/CD pipelines using the tavern-ci command-line tool. This flexibility makes it a great fit for automated testing workflows.
Though Tavern keeps things simple, it doesn't shy away from handling more intricate API testing needs. It supports workflows where the output of one test feeds into the next as input. Additionally, it validates API responses by checking essential elements like status codes, headers, and data schemas.
Tavern is especially useful for teams already working within Python environments. It requires Python 3.10 or newer and allows developers to create reusable custom validation functions. For Python-focused teams looking for a free, open-source tool that integrates smoothly into their testing pipelines, Tavern’s clear syntax and flexible validation capabilities make it a strong contender.
Up next, we’ll dive into Chakram and its distinctive approach to API testing.
Chakram wraps up our list as a JavaScript-based API testing framework designed specifically for Node.js environments. While some tools in this list lean on AI-powered features, Chakram focuses on delivering a promise-based approach to testing JSON REST endpoints. Its standout feature is how well it integrates into JavaScript development workflows, offering powerful HTTP-specific assertions.
Chakram takes full advantage of JavaScript promises, making asynchronous tests more straightforward by removing the need for callbacks. By extending Chai.js, it provides simple yet effective HTTP assertions for status codes, headers, and JSON structures.
This framework employs a Behavior Driven Development (BDD) style, allowing tests to read like plain language descriptions. With hooks like beforeEach and afterEach, Chakram simplifies the setup and teardown of even the most complex test scenarios.
Chakram enables developers to create custom assertions, making it easy to handle unique edge cases. It also validates cookies, headers, and JSON structures using JSON schema validation. These features make it a versatile tool for handling a wide range of API testing needs.
One notable example of Chakram in action comes from Domain.com.au. The company initially relied on Postman for API test automation, using Newman and TeamCity in its CI process. However, they transitioned to Chakram due to Postman’s limitations in code control and reusability. This shift demonstrates how JavaScript teams can benefit from a more flexible, code-driven approach to API testing.
Chakram fits neatly into JavaScript development stacks, leveraging Node.js, Mocha, Chai, and Request. Its Mocha-based test runner ensures that teams familiar with JavaScript testing can quickly adopt it. Moreover, integrating Chakram tests into CI/CD pipelines is straightforward.
With 100% test coverage on publicly accessible APIs, Chakram delivers a high level of reliability. Its popularity is reflected in its over 900 stars on GitHub, showing strong community support. These factors make Chakram an excellent choice for teams looking to enhance their API testing strategies.
For JavaScript developers, Chakram offers a promise-based, flexible approach to API testing. Its integration capabilities and robust feature set make it a dependable option for building thorough, evolving test suites that align with modern development workflows.
Every tool in this lineup brings something different to the table, tailored to varying technical skills, project requirements, and team needs. The table below highlights the key features and strengths of the top AI tools for API testing, showcasing their role in streamlining modern development workflows.
| Tool | AI Capabilities | Supported API Types | CI/CD Integration | Best For | Key Differentiator |
|---|---|---|---|---|---|
| ACCELQ Autopilot | Advanced AI test generation, self-healing tests, predictive analytics | REST, SOAP, custom protocols | Seamless CI triggers for regression testing | Enterprise teams seeking automation | Codeless testing with strong AI-driven maintenance |
| Katalon Studio | AI-powered test case generation, smart object recognition | REST, SOAP, GraphQL | Full CI/CD pipeline integration | Teams of all sizes | IDE-based tool with a strong user community |
| Testsigma | NLP-powered test creation, AI-driven maintenance, intelligent reporting | REST, SOAP, GraphQL | Cloud-based CI/CD connectors | Non-technical teams and agile setups | Plain-English test creation using NLP |
| mabl | Machine learning for test stability, auto-healing, intelligent insights | REST, GraphQL | Native DevOps integrations | Teams focused on reliability | Self-updating tests that adapt to API changes |
| Test.ai | Computer vision AI, element classification, predictive failure analysis | REST (via mobile/web app testing) | Jenkins, GitHub Actions, Azure DevOps | Mobile-first teams | AI-powered visual testing and element recognition |
| Tavern | Limited AI features, focuses on YAML-based automation | REST | Jenkins, GitLab CI, GitHub Actions | Python teams preferring configuration | YAML-based test definitions with pytest integration |
| Chakram | No AI features, promise-based testing framework | REST (JSON endpoints) | Straightforward CI/CD integration | JavaScript/Node.js developers | Promise-based setup with 100% test coverage capabilities |
When comparing these tools, scalability and AI sophistication are pivotal. For example, Katalon Studio has been recognized as a Gartner Peer Insights Customers' Choice for three years running, reflecting its robust capabilities. Tools like ACCELQ and mabl stand out with self-healing features, while AI sophistication ranges from basic automation to advanced machine learning. One e-commerce company reported faster test execution and improved coverage using NLP-driven API testing, which shortened release cycles and enhanced app performance.
Team expertise also plays a big role. Tools like Testsigma simplify testing for non-technical teams with natural language processing, while Chakram caters to seasoned JavaScript developers seeking maximum flexibility. Meanwhile, Test.ai bridges the gap by delivering AI-powered insights without requiring deep technical knowledge.
Integration complexity is another factor. Some tools, like Tavern, easily plug into Python-based workflows, while others may need more setup effort. Cloud-based options, such as Testsigma, offer plug-and-play convenience, making them ideal for agile teams.
Through our detailed analysis, it's clear that AI tools are reshaping API testing by streamlining repetitive tasks and enhancing precision. For instance, adopting these AI-driven solutions has reduced manual test creation by 35% and slashed pull request review times by 48%.
The financial upside is hard to ignore. AI-powered test automation can cut manual testing efforts by as much as 50%. Beyond saving time, these tools help teams make better use of their resources and improve overall productivity.
Choosing the right tools from our top seven recommendations - whether it’s ACCELQ for its self-healing features, Katalon for its strong community support, or Testsigma for its natural language capabilities - requires evaluating your team’s needs and technical expertise. Starting with clear goals and small pilot projects can help you measure success before scaling up.
For teams ready to fully embrace AI, structured implementation is key to driving tangible results. Services like 2V AI DevBoost offer tailored solutions, including workflow audits and integration support, to help teams achieve measurable gains. Their 5-week sprint process identifies current performance metrics, recommends the right tools and practices, and assists with integration. Pricing for these services ranges from $10,000 for smaller teams (5–10 people) to $40,000 for larger enterprise teams (51–100 people).
"You can expect a 15% increase in productivity after you implement all of the tools and practices that we recommend." - 2V AI DevBoost
AI-driven tools like ACCELQ Autopilot and Katalon Studio are reshaping API testing by automating tedious tasks like test creation, execution, and upkeep. ACCELQ Autopilot leverages AI to automatically generate and update test cases, cutting down on manual work and speeding up testing cycles. On the other hand, Katalon Studio streamlines testing for various API types - such as REST, SOAP, and GraphQL - making it easier to tackle even the most complex scenarios.
These tools offer a clear edge over traditional manual scripting. They enhance test coverage, reduce the time spent on maintenance, and deliver faster feedback. For development teams, this means adapting to changes becomes more seamless, all while ensuring software quality remains top-notch.
When choosing an AI-powered API testing tool, development teams should prioritize a few critical aspects. Start with ease of use - a tool that's intuitive can save time and reduce the learning curve. Next, look for seamless integration with your current workflows, ensuring the tool fits naturally into your processes. And don’t forget scalability - you’ll want a solution that can handle future testing requirements as your projects grow.
It’s equally important to evaluate the tool’s automation capabilities and its support for different types of tests. Make sure it aligns with your team’s technical expertise and fits within your budget. Features like strong data management tools and collaboration options can also play a big role in boosting productivity.
Ultimately, by matching the tool’s capabilities to your team’s unique needs, you’re setting the stage for a smoother and more effective API testing experience.
AI-powered tools like self-healing tests and natural language processing (NLP) are transforming the way API testing is done, making it faster and far less tedious.
Self-healing tests stand out by automatically identifying and fixing issues in test scripts when APIs are updated. This eliminates the need for constant manual adjustments, ensuring your tests stay reliable without extra effort.
NLP, on the other hand, allows teams to write test scripts in plain, everyday language and interpret results more easily. This not only simplifies troubleshooting but also speeds up updates, making the entire process smoother and more efficient.
By combining these features, development teams can shift their focus from repetitive maintenance tasks to building and innovating.