Inngest is used to orchestrate multi-step AI backend workflows, including document upload events, processing, chunking, embedding generation, model calls, job retries, status changes, and user notification. I view the product as part of a delivery system around the product rather than an isolated tool.
Workflow orchestration has transformed AI delivery and creates clear, visible processes
What is our primary use case?
What is most valuable?
Inngest handles orchestrating multi-step AI workflows effectively. The valuable aspect of this product is turning scattered background jobs into a visible workflow that can be planned, estimated, monitored, and discussed with engineering and client stakeholders. In our product, this matters because AI products often look simple at the interface level but contain many moving parts underneath. A user may only see a button or a chat response, but behind the scenes, they may encounter model calls, retrieval logic, state changes, data processing, and monitoring. Inngest helps to make the hidden layer more manageable.
I value that Inngest supports repeatable patterns. Instead of inventing a new solution for each feature for client projects, we can use a more consistent approach that makes future work easier to scope and maintain.
The best features Inngest offers include the ability to visualize the workflow, which allows for easy planning, estimation, and monitoring. Visualization aids greatly with planning and estimation in my projects, saving time and making communication with my team easier. As a delivery owner, I need to communicate with the team, and presenting to stakeholders is essential.
Inngest has positively impacted my organization, allowing us to spend less engineering time on building orchestration patterns. Since using Inngest, I have noticed positive changes such as smoother processes and improved team collaboration. The best impact on my project and team is that it allows us to spend less engineering time on building orchestration patterns, resulting in fewer meetings and a clearer path for async AI features. This product reduces engineering efforts and shortens the delivery cycle. Everyone using Inngest, including myself, can feel the positive outcomes. Visibility is very important.
What needs improvement?
An area for improvement for Inngest could be stronger product reporting, such as cost by client and AI feature. Better project reporting and more clarity on cost by client would be very helpful.
For how long have I used the solution?
Inngest has been used for several months for AI workflow planning and prototype delivery.
What do I think about the stability of the solution?
Inngest is reliable for essential usage. However, when considering AI, this is not part of the product concept, as the language model outputs' stability and reliability depend on how different models are designed and used for the developer's role.
What other advice do I have?
Regarding Inngest's AI capabilities, I do not discuss governance and security extensively because my role is primarily focused on guiding reports and feature usage rather than security and other aspects. My advice to others looking to use Inngest is to give it a try, as it is worth the investment, especially if you do not have a mature, event-driven structure and all the features for traceability. I would rate this product an 8 out of 10.
