Upscaling Fintech Startup with Cloud and DevOps
Innovative fintech startup that utilizes AI for predicting market changes streamlines and upscales their DevOps.
Our client, an innovative fintech startup, pioneers AI solutions to empower traders in making informed decisions in today's volatile stock market. They required a DevOps team to manage and maintain their infrastructure, facing specific challenges. They aimed to run their AI models in Kubernetes on AWS, but were constrained by a tight budget, making cost optimization a key focus of their architecture. Additionally, they sought a comprehensive solution enabling real-time monitoring of model training.
They were facing numerous challenges with their cloud infrastructure due to a lack of internal expertise. As a result, their infrastructure wan’t following best practices, leading to the following problems:
- Slow Time to Roll Out New Infrastructure and Services: Due to inconsistent naming conventions, resources with different configurations, and a lack of infrastructure as code, it took the client a long time to deploy new infrastructure and services. This not only reduced their profits but also delayed their time to market.
- Difficulty Onboarding New Engineers: The client's infrastructure was complex and challenging to understand, making it difficult to onboard new engineers and consequently delaying their productivity.
- Difficulty Maintaining Existing Infrastructure: With complexity of infrastructure came the difficulty to manage it. This lead to frequent outages and performance issues, increasing operational costs and disrupting the business operations and processes.
Our goal was to bring all around improvements, streamline the deployment process, and implement best practices. This would help to reduce costs, improve the speed of delivering new services and software to the market, and scale the business efficiently and easly.
The client's organization encompassed multiple projects, prompting us to strategically select one project as a Minimum Viable Product (MVP). This approach aimed to establish a foundational set of best practices, which could then be applied across the all other projects.
In terms of technology, we primarily utilized systems and tools with which the client was already comfortable. However, we also saw the opportunity to introduce new concepts and technologies that would bring additional benefits. A prime example was our introduction of the infrastructure as code concept, which we implemented in their MVP project. This initiative enabled the client to automate the deployment and management of their infrastructure, leading to significant time and cost savings.
Overall, our objective was to refine and enhance the client's cloud infrastructure in a manner that was both effective and efficient. We achieved this by concentrating on the most critical areas and employing technologies that were well-suited to the client's specific requirements.
The Business Outcome
- Onboarding new engineers is now streamlined and lasts two weeks, compared to 4 months previously.
- Daily deployments went up by a whooping 500%, going from 3 deployments a week to 15 deployments
- Even though number of deployments went up, the cost of the cloud infrastructure was reduced by 47%.
- Introducing new services to the market also benefited, where previously it took 4 months, now it only takes a month, and all of that time is spent refining the service, not the infrastructure.
- Faster deployment of new services also improved customer satisfaction as they could react to market changes quickly and provide the best tools to their customers.
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