Elasticsearch vs. OpenSearch

The only thing worse than no internet is slow internet. Internet speeds in general have increased exponentially in the last two decades, along with users’ expectations. We know that page load times impact conversion rates (a study by Portent found that a site that loads in 1 second has a conversion rate of 5x higher than one loading in 10 seconds). The effect of latency on search results is no different.

But search in 2025 is a lot more than keyword matching. GenAI chatbots and autocomplete functions leveraging hybrid vector and full text search can enhance the user experience, but latency makes these functions feel awkward and unnatural. In chat interfaces, humans typically respond in as little as 200ms, making this the new benchmark to hit.

If you’re comparing Elasticsearch vs. OpenSearch for your search needs, there are three factors to consider: speed, simplicity, and cost. This article will explore the background, strengths and weaknesses of both solutions, and aims to help you understand why their architectural choices matter.

Key takeaways

• For rich analytics, complex queries, and access to Kibana’s visualization ecosystem for data exploration, Elasticsearch comes out on top (but at the cost of operational complexity).
• OpenSearch is ideal for AWS-integrated environments, if a seamless experience with Elasticsearch familiarity is more important than access to the newest features.
• And when sub-millisecond latency is critical for user conversions or real-time GenAI applications, you may want to consider a third option: Redis.

Elasticsearch vs. OpenSearch: Two engines, two philosophies

Elasticsearch and OpenSearch are both distributed search and analytics engines, rooted in the same original open source search codebase. After a public spat over licensing, OpenSearch was developed from a fork of Elasticsearch in 2021, with much of the same baseline functionality as Elasticsearch. But there are also key differences that lend themselves to particular use cases.

About Elasticsearch

Elasticsearch is a distributed, RESTful search and analytics engine built on Apache Lucene. It allows users to store, search, and analyze large volumes of data in “near real-time”. Elasticsearch is commonly used together with Logstash and Kibana (forming the ELK stack), enabling log analytics. A few key components of Elasticsearch:

• Analytics-first DNA: Elasticsearch was purpose-built to efficiently handle and process massive streams of log data, metrics, and other time-series information. Its core strength is in rapidly sifting through vast datasets, largely thanks to its foundation on Lucene’s inverted index structure.
• “Store it all, search later”: Elasticsearch’s architecture is inherently disk-oriented, prioritizing the ability to store immense volumes of historical data. As a search engine it offers excellent search capabilities, but its design favors enabling comprehensive historical analysis rather than achieving the ultra-low, sub-millisecond latencies that might be critical for transactional real-time lookups. This makes it ideal for applications where retaining deep history for audit logging, trending, and retrospective analysis is paramount.

About OpenSearch

In 2021, Elastic changed its licensing model from Apache 2.0 to dual licensing: Server Side Public License (SSPL) and the Elastic License. The move was to prevent cloud service providers from offering Elasticsearch as a service without contributing back—retaliating at Amazon Web Services (AWS) and its Amazon Elasticsearch Service. Out of the fray emerged OpenSearch: an Elasticsearch alternative, based on a fork of the original project. OpenSearch maintains an open source model under the Apache-2 license, with strong community-driven development, and with Amazon OpenSearch Service available as a hosted solution. Some things to know about OpenSearch:

• Cloud-native alignment: OpenSearch is designed for optimal performance within AWS environments, emphasizing integration with other AWS services rather than rapid independent feature expansion. If you’re already bought into the broader AWS ecosystem, OpenSearch is an efficient and compatible component with a consistent user experience.
• Familiarity over novelty: OpenSearch maintains the Elasticsearch look and feel, so teams familiar with Elasticsearch can migrate with minimal retraining, accelerating time-to-value.

Core strengths of Elasticsearch vs. OpenSearch

Elasticsearch core strengths

• A rich analytics and aggregation language: Users can perform complex analytical queries with Elasticsearch’s domain-specific language. This enables deep insights into operational performance, security incidents, and business trends.
• Kibana visualization ecosystem: Kibana, Elastic’s official visualization layer, offers an intuitive platform for exploring and presenting data with many turnkey dashboards. This shortens the time-to-insight for DevOps and security teams, transforming raw data into actionable intelligence.
• Extensible plugin model: Organizations can tailor data pipelines and functionalities to specific needs: Users can leverage ingest processors, alerting, and machine learning (ML) plugins, building sophisticated workflows within the Elasticsearch stack, simplifying architecture, reducing maintenance, and enhancing efficiency.

For enterprise search needs, Elastic Cloud provides Elasticsearch, Kibana, and other Elastic Stack products as a managed service, with built-in security, monitoring, and scaling.

OpenSearch core strengths

• Tight AWS ecosystem integration: If you’re already running an AWS shop, native IAM authorization, CloudWatch metrics, and Kinesis ingest significantly reduce the need for additional infrastructure glue work for improved performance and scalability.
• Drop-in familiarity for Elasticsearch users: With the same REST API and query syntax as Elasticsearch, migration is fast and requires minimal retraining or code refactoring, ensuring a smooth transition for existing Elasticsearch users.

Teams can automate the deployment, scaling, and operation of OpenSearch clusters with Amazon OpenSearch Service.

Elasticsearch vs. OpenSearch: Drawbacks and tradeoffs

Elasticsearch drawbacks

• Shard and replica complexity: Rebalancing data across nodes during scaling events or node failures can lead to minutes-long brownouts, impacting performance, availability, and customer experience (not to mention consuming valuable operations hours).
• Feature gating: Some advanced security features like SIEM, RBAC, and advanced threat detection, sit behind paid tiers. You may quickly need to upgrade for other advanced features and capabilities like machine learning (ML), observability, and scalability.

OpenSearch drawbacks

• Features lag Elasticsearch: OpenSearch’s AWS focus means that new Lucene capabilities and bug fixes aren’t prioritized, and may arrive months later. If such features or functionality are key to your roadmap, this could be a dealbreaker.
• Plugin parity gaps and slower community velocity: OpenSearch’s ML, vector, and security plugins are still maturing, and might not be fit for your purposes yet. Community-driven development velocity doesn’t quite match Elasticsearch’s, and teams may need to build or maintain their own forks.
• AWS-centric bias: If you want a managed service, Amazon OpenSearch Service is available, but only on AWS. Integration with other clouds is limited.

Where both Elasticsearch and OpenSearch fall short

Shared latency ceiling

• Benchmarks highlight a massive performance gap between Redis and OpenSearch and by proxy, Elasticsearch (since its underlying Lucene-based architecture is similar):
  • Single-client benchmarks showing Redis performing up to 18x faster than OpenSearch in vector search queries.
  • Multi-client benchmarks show Redis outperforming OpenSearch by up to 52x in queries per second (QPS).
  • Query latency is up to 106x lower with Redis, enabling real-time AI responses where OpenSearch struggles with delays.
• Elasticsearch and OpenSearch both run on JVMs, which runs the risk of garbage collection stalls on large JVM heaps. When heap memory is not properly sized to the size of the content available to index, JVM can run continuous garbage collection cycles, making search unresponsive. These unpredictable p99 spikes lead to real-time SLA failures, forcing users to over-provision heap sizes to hide latency tails.

The reality is that sub-millisecond targets for GenAI retrieval or instant personalization are functionally out of reach for both Elasticsearch and OpenSearch.

Operational drag

• To avoid latency, performance issues, and service interruption as a result of the above drawbacks, your engineers end up babysitting heap sizes, shard rebalancing, index rollovers, and snapshot lifecycles—every week.
• By Elastic’s own estimate, a typical large deployment of a 25-node cluster of Elastic for logging or observability “will require about 50% of a person’s time to manage and orchestrate.” Without a dedicated DevOps engineer, any overhead slows feature development.

Stale data

When you query in Elasticsearch or OpenSearch, you query a copy of your data. Creating these copies takes time and adds complexity, with the added risk that a fresh update may be missed. Use cases with real-time applications (such as in banking or gaming) need a solution that queries live data so it’s always up to date.

GenAI readiness gap

Users expect the same, real-time experience from GenAI or vector search as traditional search. Unfortunately, Elasticsearch and OpenSearch can’t keep up—both require partial or full reindexing to reflect new or modified fields in vector data and metadata, introducing latency in what should be dynamic AI-driven experiences.

Cost creep

• Instead of addressing the root cause of latency, teams add hot/warm/cold tiers and a side-car query cache to optimize performance—while your node count balloons.
• Inter-AZ egress fees spiral when cache and search clusters live in different zones.
• Your total cost of ownership rises while performance plateaus.

Enter Redis: Fast, unified, real-time search

Search is not a two-horse race. If the drawbacks of Elasticsearch or OpenSearch have raised any red flags for you, Redis is an open source, in-memory data structure store, and can be used as a database, cache, or message broker. Unlike disk-based search engines, Redis works with an in-memory dataset, making it exceptionally fast.

Core strengths

• Sub-millisecond average latency: For on-the-spot queries, Redis’ in-memory indexes deliver blazing fast results, significantly quicker than Elasticsearch and dedicated databases.
• Active-Active five-nines availability: Instead of relying on a primary node, CRDT replication across clouds/regions with sub-second failover ensures high availability and reliable disaster recovery.
• A live, unified data layer: You can have your cache, full text search, JSON, and vector search in a single deployment—no sidecar required. Data is also instantly readable, making Redis a good option for storing mutable data or delivering real-time lookups.
• Hybrid search capabilities: Redis supports full text, vector, and filtered hybrid search, and its high-speed query engine makes it practical to run multiple queries (for example, lexical plus vector) and merge results at the application layer if needed.
• Management simplicity: Redis’ in-memory design avoids disk merges, reindexing overhead, and complex lifecycle policies. Redis Cloud’s automatic sharding distributes data across nodes, without the need for predefined shard layouts or manual rebalancing. Managing ephemeral data is easy to manage by setting time-to-live (TTL) expiry on any keys.

Drawbacks

• Priced for performance, not storage: In-memory data storage means that by default, all data is stored in RAM. This gives Redis its ultra-low latency (sub-millisecond performance)—ideal for handling a lot of data traffic, but potentially expensive for data storage. It depends on what you’re optimizing for: speed and low storage volume, or vast amounts of batch or archival data with a lower queries per second (QPS) requirement. 
• Limited built-in analytics UI: Since Redis is typically used for real-time apps where search is conducted programmatically, it doesn’t have a built-in, user-friendly interface for analytical use cases like exploring data or visualizing results—which Elasticsearch and OpenSearch are ideal for. Teams can pair Redis with Grafana or Apache Superset to provide the visual layer for monitoring, data analysis, or exploring search or vector query results. Redis supports exporting Prometheus metrics for dashboards.
• Complex queries lead to latency: Complex search queries can increase latency, even in Redis. To improve performance, you may need to re-engineer your queries in Redis.
• Blended ranking for hybrid search still in development: While Elasticsearch supports built-in blended ranking hybrid search (such as Convex Combination and Reciprocal Rank Fusion (RRF))—where lexical and vector search results are combined and ranked together—this feature has not yet been released in Redis.

Decision framework—Which is best for your project?

When Elasticsearch or OpenSearch makes sense

There are instances where Elasticsearch or OpenSearch is the better fit for your organization if, for example:

• Your primary use case for search is analytical, working with heavy log or metrics analytics in Kibana or OpenSearch Dashboards.
• You run a lot of complex full text search queries, which Elasticsearch excels at.
• You rely on built-in blended ranking hybrid search (such as Convex Combination and Reciprocal Rank Fusion (RRF)) for GenAI applications.
• Your data primarily consists of large, cold data archives where 10-15 ms latency is acceptable—lookup speed and throughput are less important than storage costs.
• Your organization is already bought into the AWS ecosystem, and OpenSearch’s tight integration relieves the need for infrastructure glue work.

When Redis makes sense

Latency is hurting your applications

• Your throughput or QPS is lagging, impacting customer experience.
• Using a query cache with Redis or Memcached is driving up your architectural complexity.
• Your engineers are spending time babysitting heap sizes and shard counts, or digging into latency outliers, trying to maintain acceptable performance and costs.
• Your infrastructure spend is climbing due to inefficient performance, scaling and complexity.

You’re building GenAI agents or workflows

To keep AI-driven experiences dynamic and responsive, you need fully featured, benchmark-leading vector search. Unlike disk-based systems, which often require partial or full reindexing to reflect changes, Redis updates data in memory, making it instantly available for querying. Redis offers the highest throughput for multiple queries and large datasets, even compared to dedicated vector databases.

Redis combines vector search with full text, filtering, and other robust query capabilities in a single platform, with real-time performance and without the operational complexity of Lucene-based systems. This means you can run hybrid queries without adding another search system, keep vectors and metadata in sync instantly without reindexing, and avoid the stack complexity that comes with bolting a vector store onto another database.

Hybrid search differences

Redis, Elasticsearch, and OpenSearch all include built-in FTS capabilities for:

• Lexical searches with BM25/TF-IDF scoring.
• Metadata filtering on tags, text, numerics, and geo-coordinates.
• Exact match filtering with vector search that delivers fast, relevant results.

Hybrid search capabilities differ amongst the three:

• All three support filtered hybrid search, where vector search results are refined using metadata filtering, exact match lookups, or FTS constraints.
• Elasticsearch and OpenSearch support built-in blended ranking hybrid search (such as Convex Combination and Reciprocal Rank Fusion (RRF)), where lexical and vector search results are combined and ranked together.

While Redis does not offer blended ranking today (it’s coming soon), it can be implemented client-side and also provides real-time indexing (instant updates), low-latency query performance, and efficient exact match filtering. By storing embeddings and session data together, Redis acts as short-term memory for AI apps that frequently update short-lived context. All of the above make it a strong choice for GenAI apps that demand speed and scalability.

You can read more about how Redis for GenAI and vector search compares to both Elasticsearch and OpenSearch.

Get ultra fast, AI-ready search without the complexity

While Elasticsearch and OpenSearch still excel at deep analytics, they struggle to meet the sub-millisecond standard of Redis. With speed, simplicity, and cost as the new holy trinity of search, Redis hits sub-millisecond latency, folds your cache, search, and vector into one stack, and delivers five-nines uptime—making it the standout choice for real-time and GenAI workloads.

Ready to experience sub-millisecond search? Try Redis Cloud free or request a personalized demo today.