AWS Slashes Log Analytics Costs by Up to 70% with New OpenSearch Engine
TL;DR: Amazon Web Services has launched a redesigned log analytics engine for Amazon OpenSearch Service that delivers up to 4x better price-performance, 2x faster ingestion, and up to 70% storage cost reduction — all without changing a single line of your existing code.
The Log Data Problem Is Getting Expensive Fast
Every application you run generates logs. Every microservice, every API call, every database transaction, every failed authentication attempt — all of it produces a relentless stream of data that your operations, security, and engineering teams depend on. The problem? Log volumes are growing at 30 to 40 percent per year, and the infrastructure costs to store, index, and query that data are growing right along with them.
For years, organizations have faced an uncomfortable tradeoff: either pay a premium to keep logs fast and searchable, or archive them cheaply and lose the ability to run meaningful analytics in real time. Most enterprises end up doing both, maintaining separate systems for hot and cold log data, which adds operational complexity on top of the already-rising cost curve.
This is the landscape that Amazon Web Services is stepping into with its newly announced log analytics engine for Amazon OpenSearch Service. Rather than forcing teams to choose between performance and cost, AWS is betting on a new architectural foundation built on proven open-source technologies to deliver both simultaneously — and the early numbers are hard to ignore.
What Is the New OpenSearch Log Analytics Engine?
At its core, the new engine is a purpose-built log analytics backend for Amazon OpenSearch Service that replaces the traditional Lucene-based storage and query layer with a modern columnar stack designed specifically for the analytical workloads that log data demands.
Here is what is powering it under the hood:
Apache Parquet for Storage
Instead of storing log data in row-oriented Lucene segments, the new engine writes data in Apache Parquet, the columnar storage format widely used in the data lake ecosystem. Parquet is highly compressed and optimized for analytical queries that touch only a subset of columns — exactly the kind of queries you run when you are scanning billions of log lines to find error rates, latency percentiles, or security anomalies. This single change accounts for the up to 70 percent reduction in storage costs compared to the previous engine.
Apache Calcite for Query Optimization
Query planning and optimization are handled by Apache Calcite, a mature, battle-tested SQL query optimization framework. Calcite enables the engine to push down filters, reorder joins, and prune unnecessary data scans before execution begins, which translates directly into faster query response times and lower compute usage per query.
Apache DataFusion for Analytical Execution
The actual query execution layer is powered by Apache DataFusion, a high-performance, vectorized query engine written in Rust. DataFusion is built for columnar, in-memory analytical operations and is increasingly used across the data engineering ecosystem. It is what enables the 2x improvement in analytical query performance that AWS is reporting for this release.
Crucially, this entire new stack sits behind the existing OpenSearch APIs. If your team is already using OpenSearch Dashboards, the OpenSearch Query DSL, or any compatible log ingestion pipeline, nothing changes from your perspective. The performance and cost improvements are delivered transparently.
Why This Matters for Your Engineering Team
The practical implications here go beyond raw benchmark numbers.
Cost predictability improves dramatically. When storage costs drop by up to 70 percent, your per-gigabyte log retention economics change fundamentally. Logs you previously had to expire after 30 days to control costs can now be retained for 90 days or longer at the same budget. This matters enormously for compliance-heavy industries like financial services and healthcare, where log retention requirements are often mandated by regulation.
Faster ingestion reduces pipeline pressure. A 2x improvement in ingestion throughput means that during traffic spikes — think a major product launch, a security incident, or a regional failover — your log pipeline is far less likely to become a bottleneck. Fewer dropped events, fewer alert gaps, more reliable observability exactly when you need it most.
Analytical queries become interactive. The difference between a query that takes 30 seconds and one that takes 15 seconds might sound modest, but in practice it is the difference between an on-call engineer running exploratory queries during an incident and waiting on dashboards. Speed changes behavior, and faster log analytics leads to faster mean time to resolution.
Real-World Use Cases: Who Benefits Most?
E-Commerce Platforms During Peak Traffic
Consider an e-commerce company running Black Friday promotions. Log volumes can spike 10x or more within minutes. With the new engine, ingestion keeps pace with the surge, and on-call engineers can run real-time queries against fresh log data to detect checkout failures, latency regressions, or fraud patterns without waiting for batch processing windows.
Security Operations Centers (SOCs)
Security teams routinely need to search months of log history to reconstruct attack timelines during incident response. The combination of cheaper long-term retention and faster analytical queries makes threat hunting more practical. A query that scans three months of authentication logs to find lateral movement patterns is exactly the kind of workload that benefits from columnar storage and vectorized execution.
SaaS Companies Managing Multi-Tenant Observability
Multi-tenant SaaS platforms often need to provide per-customer log analytics as a feature of their product. The cost reduction in this new engine makes it economically viable to offer richer log retention tiers to customers without the storage costs eroding margin.
Financial Services Firms with Audit Requirements
Regulatory frameworks like PCI-DSS and SOX require extended log retention and the ability to query historical records. Lower storage costs mean compliance does not have to compete with the infrastructure budget, and faster queries mean audit reports can be generated more quickly.
What You Should Do Next
If you are already running Amazon OpenSearch Service for log analytics, the path forward is straightforward. AWS has designed this engine to be a drop-in improvement — your existing indices, dashboards, ingestion pipelines, and API integrations remain fully compatible. The practical first step is to evaluate your current log retention policies and storage costs, then model what the new pricing structure looks like for your actual log volumes.
For teams currently using alternative log management solutions and evaluating their options, this announcement significantly changes the competitive calculus. The combination of OpenSearch’s mature ecosystem, AWS’s managed infrastructure, and this new engine’s cost-performance profile makes it worth a serious evaluation, particularly for workloads that are already running inside AWS.
AWS has also indicated that this engine maintains full compatibility with OpenSearch’s existing search capabilities, meaning you are not giving up full-text search functionality in exchange for analytical performance. Both workload types are supported on the same infrastructure.
The Bigger Picture
This release reflects a broader trend in the cloud industry: specialized engines for specific workload types are outperforming general-purpose systems by significant margins. The separation of compute and storage, combined with purpose-built execution engines, is delivering cost and performance improvements that would have been impossible to achieve by simply scaling up traditional architectures.
For engineering leaders, the message is clear. Log analytics does not have to be the expensive, slow, and painful part of your observability stack. With the right tooling, you can retain more data, query it faster, and spend less doing it — and that is a combination worth paying attention to.
The original article: Run log analytics for a fraction of the cost with the new engine for Amazon OpenSearch Service.
