Elastic Supercharges Serverless on AWS with Free Graviton Upgrade

SAN FRANCISCO, CA – January 20, 2026 – Elastic, the Search AI Company, has rolled out a significant performance enhancement for its Elastic Cloud Serverless offering on Amazon Web Services (AWS), providing users with a substantial boost in speed and efficiency at no additional cost. The upgrade, which is being deployed automatically across all AWS-based serverless projects, leverages new AWS Graviton instances to deliver up to 50% higher indexing throughput and a 37% reduction in search latency.

This move enables developers and organizations to handle larger data volumes and more complex queries for search, observability, and security use cases without any manual intervention or configuration changes. The enhancement is designed to be seamless, with customers benefiting immediately from the improved performance across a wide range of workloads, from traditional full-text queries to the demanding vector searches required for modern AI applications.

“Elastic Cloud Serverless lets developers focus on building search, observability, security, and AI applications, not managing infrastructure,” said Ajay Nair, general manager of Platform at Elastic, in a statement. “With this upgrade, we’re delivering faster performance and greater efficiency across a broad range of workloads, without added operational complexity.”

The Graviton Advantage: Under the Hood of the Performance Boost

The core of this upgrade lies in Elastic's adoption of AWS Graviton processors. These are custom-designed, ARM-based chips developed by AWS to deliver superior price-performance and energy efficiency for cloud-native workloads. Unlike traditional x86-based processors, Graviton's ARM architecture utilizes a more streamlined instruction set, which translates into faster execution and lower power consumption.

This architectural difference provides several key benefits that directly impact Elastic's serverless performance:

• Enhanced Parallel Processing: Graviton instances typically feature a higher density of processing cores. This allows for more tasks to be run in parallel, a critical factor for high-throughput operations like data indexing. The ability to ingest and process data faster means users can achieve near real-time visibility into their information.

• Increased Memory Bandwidth: The processors also offer significantly higher memory bandwidth, which is crucial for data-intensive applications like search. Faster access to data held in memory dramatically reduces the time it takes to execute queries, resulting in the near-instantaneous response times Elastic is now touting for both keyword and AI-driven vector searches.

• Optimized for the Cloud: Graviton processors are purpose-built for the kind of stateless, containerized, and microservices-based applications that define modern cloud environments. Elastic Cloud Serverless, with its architecture designed for independent and automatic scaling of search and indexing resources, is a natural fit for this technology. The result is more efficient scaling during traffic surges and quieter periods, ensuring consistent low-latency performance.

Redefining the Serverless Competitive Landscape

Elastic's strategic shift to Graviton is not happening in a vacuum. It represents a critical move within a highly competitive serverless data platform market. Key rivals, including AWS's own OpenSearch Serverless, Splunk, and Datadog, have also publicly embraced Graviton to boost performance and manage costs, signaling a broader industry pivot toward ARM-based cloud infrastructure.

By integrating Graviton, Elastic ensures its offering remains competitive on a pure performance basis. However, its key differentiator lies in the seamless, fully managed nature of the upgrade. For thousands of existing customers, including over half of the Fortune 500, these significant performance gains appeared overnight without requiring a single line of code change, migration plan, or budget renegotiation. This hands-off approach reinforces the core value proposition of serverless: empowering developers to innovate on applications rather than managing underlying infrastructure.

Furthermore, this enhancement strengthens Elastic's integrated Search AI Platform, which unifies search, observability, and security capabilities. While competitors may offer point solutions for monitoring or logging, Elastic provides a single, cohesive platform where performance improvements in the core infrastructure benefit all use cases simultaneously. Faster indexing improves log analytics for observability teams, while lower search latency accelerates threat hunting for security analysts.

Beyond Speed: The Economic and Strategic Implications

While the headline figures of 50% throughput gains and 37% latency reduction are impressive, the most compelling aspect for many businesses is that this power comes at no extra cost. This decision taps into a significant trend in cloud computing, where AWS Graviton has become synonymous with cost optimization.

Across the industry, companies migrating to Graviton-based instances have reported substantial financial benefits. AWS itself notes that Graviton can offer up to 40% better price-performance over comparable x86 instances. This efficiency stems from lower power consumption—up to 60% less for the same performance—which reduces operational costs for the cloud provider and helps customers meet their sustainability goals. Case studies from companies like Zendesk, which saved 42% per month after migrating, and Pinterest, which saw 38% savings on compute resources, validate the economic power of the architecture.

By absorbing the engineering effort to transition its serverless platform to Graviton and passing the performance benefits to customers for free, Elastic is making a strategic play focused on long-term value and customer retention. It effectively lowers the total cost of ownership for its users, allowing them to scale their data and AI initiatives more affordably. This move transforms the conversation from a simple cost-per-query model to one about overall efficiency and resource optimization, a crucial consideration for CFOs and IT leaders planning for future growth.

A Strategic Play for the AI-Powered Future

This infrastructure upgrade is more than just a technical refresh; it is a foundational element of Elastic's long-term vision as 'the Search AI Company.' The dramatic reduction in search latency is particularly critical for the next generation of artificial intelligence applications, especially those using Retrieval-Augmented Generation (RAG). RAG models, which retrieve relevant data from a knowledge base to provide more accurate and context-aware answers, depend on the speed and relevance of the underlying search engine. By making vector search faster, Elastic is positioning its platform as an essential component for building responsive and powerful generative AI experiences.

The move also highlights the deepening strategic partnership between Elastic and AWS. Following a five-year Strategic Collaboration Agreement, this upgrade is a tangible outcome of their joint efforts to optimize Elastic's solutions on the AWS cloud. By leveraging AWS's most advanced infrastructure, Elastic not only enhances its product but also solidifies its position within the AWS ecosystem, making it an even more attractive choice for the vast number of enterprises standardized on Amazon's cloud.

Ultimately, this seamless, cost-free performance boost empowers developers to push the boundaries of what's possible with their data. It ensures that as organizations embrace more complex analytics and AI-driven workloads, the underlying platform can scale in both performance and efficiency, providing a reliable and future-proof foundation for innovation.