Find and compare the best Artificial Intelligence software for Swagger in 2025
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Use the comparison tool below to compare the top Artificial Intelligence software for Swagger on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.
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Docsie
Docsie
$39 per month (annual)Docsie is an award-winning digital documentation and knowledge management platform based in Ontario, Canada. You can access Docsie through a SaaS web application to create & edit documentation from any location. Then, you can publish content to a dynamic knowledge portal that users can access whenever they need information! Docsie offers powerful business-grade features to write & manage product documentation: - Pilot onboarding service w/ portal design support and workforce training - Internal & external knowledge base options - Create multiple workspaces - Portal analytics & feedback with Docsie Vocally - Custom domain on free tier - Markdown compatible - Docsie Editor with rich formatting and content embeds - iFrame - SwaggerAPI import - Built-in and custom document templates - Help center deployment & in-app help sidebar - Share guided tours & create with our builder Chrome extension - Manage multiple versions, languages, and view change history - Webhook support in Mattermost, Slack, and more - Ghost AI language translation (available) & generative AI (in-progress) - Project management with kanban and task creation - RBAC/JWT/SSO for security, user management, and data protection -
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BentoML
BentoML
FreeDeploy your machine learning model in the cloud within minutes using a consolidated packaging format that supports both online and offline operations across various platforms. Experience a performance boost with throughput that is 100 times greater than traditional flask-based model servers, achieved through our innovative micro-batching technique. Provide exceptional prediction services that align seamlessly with DevOps practices and integrate effortlessly with widely-used infrastructure tools. The unified deployment format ensures high-performance model serving while incorporating best practices for DevOps. This service utilizes the BERT model, which has been trained with the TensorFlow framework to effectively gauge the sentiment of movie reviews. Our BentoML workflow eliminates the need for DevOps expertise, automating everything from prediction service registration to deployment and endpoint monitoring, all set up effortlessly for your team. This creates a robust environment for managing substantial ML workloads in production. Ensure that all models, deployments, and updates are easily accessible and maintain control over access through SSO, RBAC, client authentication, and detailed auditing logs, thereby enhancing both security and transparency within your operations. With these features, your machine learning deployment process becomes more efficient and manageable than ever before. -
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BudgetML
ebhy
FreeBudgetML is an ideal solution for professionals looking to swiftly launch their models to an endpoint without investing excessive time, money, or effort into mastering the complex end-to-end process. We developed BudgetML in response to the challenge of finding a straightforward and cost-effective method to bring a model into production promptly. Traditional cloud functions often suffer from memory limitations and can become expensive as usage scales, while Kubernetes clusters are unnecessarily complex for deploying a single model. Starting from scratch also requires navigating a myriad of concepts such as SSL certificate generation, Docker, REST, Uvicorn/Gunicorn, and backend servers, which can be overwhelming for the average data scientist. BudgetML directly addresses these hurdles, prioritizing speed, simplicity, and accessibility for developers. It is not intended for comprehensive production environments but serves as a quick and economical way to set up a server efficiently. Ultimately, BudgetML empowers users to focus on their models without the burden of unnecessary complications. -
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Swagger Codegen
SmartBear
FreeSwagger Codegen streamlines your development workflow by automatically generating server stubs and client SDKs for any API outlined in the OpenAPI specification, allowing your team to concentrate more effectively on implementing and promoting your API. Transitioning from design to development is remarkably simplified when using Swagger Codegen within SwaggerHub, where you can create stubs in widely-used programming languages such as Java, Scala, and Ruby with minimal effort, requiring just a few clicks to get started. This efficiency not only saves time but also enhances collaboration among team members throughout the API lifecycle. -
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Jovu
Amplication
Seamlessly create new services and enhance your current applications using Amplication AI. Transform your concepts into operational systems in just four minutes. This AI-powered tool generates code that is ready for production, ensuring uniformity, reliability, and compliance with top-tier standards. Experience a swift transition from idea to implementation, with scalable code that is ready for deployment in minutes. Amplication AI goes beyond mere prototypes, providing fully functional and resilient backend services that are primed for launch. It streamlines your development processes, minimizes time spent, and maximizes your resources. Harness the capabilities of AI to achieve more with your existing assets. Simply enter your specifications and observe as Jovu converts them into immediately usable code components. It produces production-ready data models, APIs, authentication, authorization, event-driven architectures, and all necessary elements to get your service operational. You can also integrate architecture components and extend functionalities using the various Amplication plugins available. This allows for greater customization and adaptability in your development projects. -
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Emergence Orchestrator
Emergence
Emergence Orchestrator functions as an independent meta-agent that manages and synchronizes the interactions of AI agents within enterprise systems. This innovative tool allows various autonomous agents to collaborate effortlessly, handling complex workflows that involve both contemporary and legacy software systems. By utilizing the Orchestrator, businesses can efficiently oversee and coordinate numerous autonomous agents in real-time across a multitude of sectors, enabling applications such as supply chain optimization, quality assurance testing, research analysis, and travel logistics. It effectively manages essential tasks including workflow organization, compliance adherence, data protection, and system integration, allowing teams to concentrate on higher-level strategic objectives. Among its notable features are dynamic workflow orchestration, efficient task assignment, direct agent-to-agent communication, an extensive agent registry that maintains a catalog of agents, a specialized skills library that enhances task performance, and flexible compliance frameworks tailored to specific needs. Additionally, this tool significantly reduces operational overhead, enhancing overall productivity within enterprises. -
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SOAtest
Parasoft
PARASOFT SOATEST Artificial Intelligence and Machine Learning Power APIs and Web Service Testing Tools Parasoft SOAtest is based on artificial intelligence (AI), machine learning (ML), and simplifies functional testing across APIs and UIs. The API and web service testing tool is perfect for Agile DevOps environments because it uses continuous quality monitoring systems to monitor the quality of change management systems. Parasoft SOAtest is a fully integrated API and web-service testing tool that automates end-to-end functional API test automation. Automated testing is simplified with advanced functional test-creation capabilities. This applies to applications with multiple interfaces (REST and SOAP APIs as well as microservices, databases, etc.). These tools reduce security breaches and performance issues by turning functional testing artifacts in security and load equivalents. This allows for faster and more efficient testing, while also allowing continuous monitoring of API changes. -
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CognitiveScale Cortex AI
CognitiveScale
Creating AI solutions necessitates a robust engineering strategy that emphasizes resilience, openness, and repeatability to attain the required quality and agility. Up until now, these initiatives have lacked a solid foundation to tackle these issues amidst a multitude of specialized tools and the rapidly evolving landscape of models and data. A collaborative development platform is essential for automating the creation and management of AI applications that cater to various user roles. By extracting highly detailed customer profiles from organizational data, businesses can forecast behaviors in real-time and on a large scale. AI-driven models can be generated to facilitate continuous learning and to meet specific business objectives. This approach also allows organizations to clarify and demonstrate their compliance with relevant laws and regulations. CognitiveScale's Cortex AI Platform effectively addresses enterprise AI needs through a range of modular offerings. Customers can utilize and integrate its functionalities as microservices within their broader AI strategies, enhancing flexibility and responsiveness to their unique challenges. This comprehensive framework supports the ongoing evolution of AI development, ensuring that organizations can adapt to future demands.
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