SmartBear Software introduced API Complete, a solution that enables software developers, testers and IT operations staff to test and monitor the quality of APIs (Application Programming Interfaces) and Web Services in an integrated and streamlined fashion.
API Complete helps organizations improve the quality of the increasing number of APIs and Web Services used in Web applications and sites, and can replace the fragmented approach currently used by separate development and operations teams.
According to Eric Knipp, Managing VP, Gartner Inc., "New applications will increasingly be constructed using agile practices and DevOps — joint initiatives between development and operations to streamline the rapid, continuous improvement of applications. Furthermore, an increased emphasis on analytics will enable more-focused investment in the areas of applications that really matter to improve user experience, productivity and, ultimately, profitability. Our recommendation is to treat a public Web API as a key component of your Web strategy, not as a bolt-on to an existing project and manage the API with the same care you would your enterprise Web presence.” (Predicts 2012: Application Development, December 2, 2011)
Ian McLeod, Executive Vice President & Chief Product Officer, SmartBear, continued, “The speed and availability of Web Services are a crucial component of the larger end-user experience delivered by Web applications but, until today, development and operations teams had to use disparate toolsets for testing and monitoring. Now, API Complete provides a unified DevOps approach to quality for cloud or mobile applications.”
API Complete combines soapUI, an API testing tool, loadUI for load testing, and AlertSite, SmartBear’s Web performance monitoring solution and global monitoring network into an integrated framework for API lifecycle quality management.
Using common test scripts and validation assets, API Complete helps development, IT operations and e-commerce teams ensure that APIs are thoroughly tested pre-deployment and performing well for end-users or business partners around the world once in production. This significantly improves efficiency and collaboration, and lowers costs.
Web APIs are growing exponentially and are required for mobile and social applications, as well as for innovative forms of e-commerce that combine Web Services and contextual information to deliver a compelling user experience. To ensure their quality, it is critical to conduct meticulous functional and load testing during pre-deployment to identify and resolve problems early, as well as continuous monitoring and regression testing post-deployment to ensure ongoing quality of service and availability.
SmartBear’s integrated API Complete:
• Fosters collaboration among development, IT operations and e-commerce teams for the testing and monitoring of APIs. When a monitor fails, the same transaction can be sent back to development or testing teams to investigate and remediate production issues in a process that frequently does not have a single business owner, and is difficult to duplicate in the field.
• Is easy to use, leveraging test scripts created for pre-deployment testing for production monitoring eliminates re-work and custom scripting. With more than 800,000 users worldwide, there is also a good chance development and testing teams are already using soapUI for API testing.
• Enables control, visibility and reproducibility into performance of APIs that live in the “black box” of cloud-dependent and mobile applications.
• Empowers companies to deliver superior experiences to end users and measure API performance and availability against contractual service-level agreements (SLAs) with business partners.
API Complete is available immediately.
The Latest
Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...
In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ...
Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...
Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...
Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...
The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...
The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...
In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...
AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.
The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...