For a SaaS product, search operations must scale with both the index and the query load. The team behind this account had been spending engineering time on cluster operations instead of search relevance and product work. Managed Elasticsearch changed that allocation of effort.
The Challenge of Scaling Search Operations
As our user base expanded, so did our data set and the sophistication of our search requirements. We were at one time dedicating more than 40% of our engineering effort simply tweaking nodes and addressing performance issues. That left us with less time to improve search relevance or introduce features that users wanted.
Additionally, as the product grew, search requirements became more complex. Monitoring user behaviour to improve results became necessary, and the existing setup could not keep pace. The team needed a scalable, reliable search platform so engineers could focus on product value instead of cluster maintenance.
Enter Managed Elasticsearch for SaaS
After a technical review, the team adopted Managed Elasticsearch. This reduced the time spent on cluster management, scaling, and infrastructure upkeep.
Simplifying Cluster Management
Managed Elasticsearch reduced the operational work required to run the cluster. Before the change, the team spent about 15 hours each week addressing performance issues. The team could then spend more time on search relevance and product improvements.
By having these complexities taken care of by Managed Elasticsearch, our engineering team diverted their focus towards higher-level initiatives like streamlining our search algorithms according to user requirements.
Automated Scaling for Growing Demands
As our user count grew, so did the number of search queries. Automated scaling from Managed Elasticsearch made sure our search infrastructure could handle growing demands without the administrator needing to make manual tweaks.
During product launches and campaign spikes, user activity sometimes increased by as much as 300%. Automated scaling helped the platform absorb that load while keeping search responsive.
High Availability and Built-in Monitoring
Another essential aspect of Managed Elasticsearch is its integrated monitoring and high availability. This enabled us to monitor our search operations in real time. For example, our team was able to spot potential issues within minutes of their occurrence, avoiding interruptions before they became severe.
High availability ensured our search service remained operational despite unexpected challenges, which is essential for maintaining user trust. For example, during a service outage from a DDoS attack, our search functionality remained accessible due to this feature, allowing us to uphold our service level commitments.
Focusing on Search Relevance and New Features
With infrastructure management complexities removed, our development team turned its attention to what mattered: improving search relevance and adding new features.
The team was then able to test machine learning and more advanced search algorithms. Search accuracy improved by 25%, and users found relevant results more consistently.
Enhancing User Experience
As search relevance improved, user engagement increased by 30%. Users found relevant content more quickly, which improved satisfaction and supported retention.
Our ability to focus on user experience was a direct result of our Managed Elasticsearch move. By optimizing search operations, we focused on creating features that created genuine value.
Driving Innovation
With infrastructure management weight lifted, our engineers were able to focus on innovation. We added personalized search results and sophisticated filtering capabilities that improved user experience and helped drive our growth.
These innovations served as key drivers for winning new customers and keeping existing ones. In particular, we observed a 20% increase in new customer acquisition following the introduction of these features, validating the effect of our improved search capability.
The Results: A Scalable and Efficient Search Operation
The effect of Managed Elasticsearch on our search operations was impressive. The major performance enhancements were the improvement in efficiency, performance, and user satisfaction.
Increased Efficiency
By offloading cluster management tasks, our operational efficiency increased significantly. With our engineering team focused on strategic initiatives rather than infrastructure issues, we accelerated our development cycles and brought new features to market at a rate 50% faster than before.
Enhanced Performance
Automated scaling and high availability supported increasing data volumes and query traffic without service disruption. Search results remained available as usage grew.
Improved User Satisfaction
The real test of our success with Managed Elasticsearch is finally user satisfaction. Users were able to quickly find the data they were looking for, leading to greater engagement and retention. As our user base grew, we were able to keep a high level of service, which became essential for long-term success.
A Successful Path Forward
In short, Managed Elasticsearch has transformed the way our SaaS business conducts search operations. Outsourcing cluster management and infrastructure support allows us to focus on delivering a great search experience for our customers.
We look forward to growth and what Managed Elasticsearch opens up. It has not only streamlined our operations but also kindled innovation and enhanced our product offerings.
If you run a SaaS platform that needs search scale without ongoing cluster overhead, Managed Elasticsearch is worth evaluating.
SquareShift helps teams review architecture, tune relevance, and improve search operations when growth starts to expose infrastructure limits.