Elasticsearch vs. Searchcraft

Elasticsearch Deep Dive

Breaking Down the Costs, Complexity, and Trade-Offs of Elasticsearch

Elasticsearch has been a dominant force in the search landscape for over a decade, powering everything from enterprise applications to log analytics. But is it the right choice for every team? While Elasticsearch offers scalability and rich query functionality, it comes with significant complexity, high infrastructure demands, and mounting costs—especially for teams focused on content search rather than log analysis.

A Brief Introduction to Elasticsearch

Elasticsearch began as a project by Shay Banon, who initially developed it in 2010 while looking for a scalable search solution that could handle large volumes of data efficiently. Built on top of Apache Lucene, Elasticsearch was designed to be a distributed, open-source search and analytics engine, enabling users to store, search, and analyze large amounts of data in near real-time. In 2012, Elastic NV was founded by Shay Banon and others to further develop and support Elasticsearch. Over the years, Elasticsearch has evolved significantly, introducing features like full-text search, distributed indexing, and advanced analytics capabilities. Today, it is widely used across various industries for applications ranging from log and event data analysis to full-text search solutions, forming the backbone of Elastic’s suite of products that include Observability, Security, and Enterprise Search solutions.

Pros

A Powerful, Scalable Solution

Elasticsearch has long been a go-to search engine for large-scale applications, offering impressive scalability, speed, and flexibility. Its distributed architecture enables businesses to expand their data infrastructure with resilience, making it a solid choice for organizations dealing with massive datasets. With a rich query language and powerful real-time analytics, Elasticsearch provides the tools necessary for handling complex search use cases. While it comes with a learning curve, for teams with the right expertise and infrastructure, Elasticsearch delivers high-speed indexing and search capabilities that can power everything from e-commerce platforms to log analysis tools.

Scalability: Elasticsearch is designed for scalability, allowing businesses to grow their data infrastructure without significant constraints. Its distributed architecture enables easy expansion across multiple servers, optimizing performance and resilience.

Speed and Performance: Known for its rapid search capabilities, Elasticsearch can handle large volumes of data efficiently, providing near real-time search results. This is beneficial for applications requiring quick data retrieval and analysis.

Rich Query Language: Elasticsearch offers a flexible query API that supports complex queries and real-time analytics, which can be advantageous for developing sophisticated search functionalities.

Cons

Hidden Costs and Complexity

Despite its strengths, Elasticsearch comes with significant trade-offs. Its resource-heavy architecture demands careful tuning and scaling, often leading to high infrastructure costs. The complexity of setup and maintenance requires backend expertise, making it difficult for front-end teams or smaller organizations to implement without dedicated DevOps support. Additionally, while the core product is free, scaling up to meet business needs often results in hefty licensing and cloud hosting fees. For teams looking for a leaner, easier-to-maintain search solution, these challenges can quickly outweigh Elasticsearch’s advantages.

Resource Intensive: Elasticsearch can be CPU-intensive, requiring careful resource planning to operate efficiently. This might be challenging for small teams with limited infrastructure resources.

Complex Setup and Maintenance: The setup and tuning of Elasticsearch can be complex, requiring expertise to optimize performance and manage infrastructure effectively. This can be daunting for teams without deep technical knowledge.

Cost Considerations: While the basic version is free, scaling up with additional features or using managed services like Elastic Cloud incurs costs that might not fit the budget of small teams or startups.

FEELING BLOATED?

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Elasticsearch and Open Source: A Complicated Relationship

Elasticsearch began as an open-source project, but over the years, its licensing model has taken several turns—leaving developers and businesses in a state of uncertainty. What started as a freely available tool under Apache 2.0 has since moved to the restrictive Server Side Public License (SSPL), limiting how companies can deploy and modify it. Meanwhile, forks like OpenSearch emerged in response to Elastic’s shifting stance on openness. For teams looking for true transparency, flexibility, and long-term stability, Elasticsearch’s evolving licensing landscape raises concerns.

Open Source Support

Elasticsearch

Searchcraft

Yes Licensed under SSPL (for now)

NoNot Open Source

A Brief History of Elasticsearch and Open Source

Elasticsearch has had a bit of a rocky road when it comes to whether or not their product source code is available to the development community. Here’s a quick rundown:

Initial Controversy (2019-2021)

  • AWS launched "Open Distro for Elasticsearch" in 2019, citing concerns about Elastic's "intermingling of proprietary code" in Elasticsearch.
  • Elastic accused AWS of misusing their brand and "hijacking" their work.
  • In 2021, Elastic abandoned the Apache 2.0 license, moving to the Elastic License and Server Side Public License (SSPL).

AWS Fork and Community Reaction

  • AWS forked Elasticsearch, creating OpenSearch based on the last Apache 2.0 version.
  • This move sparked controversy in the tech community, with some viewing Elastic's actions as anti-user and a "rug pull" on contributors.

Recent Developments (2024)

  • Elastic announced plans to reintroduce the AGPL open-source license alongside existing licenses.
  • This decision aims to clarify Elastic's market position and improve relations with AWS.

Community Skepticism

  • Some remain unconvinced by Elastic's explanations for the change.
  • Concerns persist about Elastic's commitment to open source, with questions about the licensing of X-Pack, a proprietary component.

Business Implications

  • Elastic's licensing changes occurred against a backdrop of financial performance concerns.
  • The company cites improved relations with AWS and a desire to reduce market confusion as reasons for the return to open source.

Searchcraft is Committed to Open Source, Without the Uncertainty

We deeply respect and rely on the open-source community—it’s an essential part of modern software development. Like many companies, we benefit from open-source tools daily, and we’re committed to giving back through projects that enhance the broader ecosystem. However, we’ve chosen not to open source the Searchcraft core product itself. Why? Because we’ve seen what happens when companies flip-flop on licensing, creating instability and uncertainty for developers. Instead of following in Elasticsearch’s footsteps, we’re focused on building a sustainable, long-term foundation that fuels innovation and customer support. Our contributions to open source will be in the form of adjacent tools and integrations that make Searchcraft more extensible—ensuring developers get the best of both worlds: reliability and flexibility without the licensing headaches.

Elasticsearch: Powerful, but Painfully Complex

Elasticsearch can be complicated to configure for first-time users, particularly if they lack experience with distributed systems or search engine frameworks. While Elasticsearch is powerful and highly flexible, its complexity can present challenges during the initial setup and configuration.

Factors Contributing to Elasticsearch Complexity

Numerous configuration files: Elasticsearch uses multiple configuration files, including elasticsearch.yml, jvm.options, and log4j2.properties, each serving different purposes.

Large number of parameters: There are many configuration parameters to consider for both searching and indexing performance, making optimization a complex task.

Interdependent settings: Many settings are interrelated, requiring a holistic understanding of the system. For example, the number of shards and replicas can significantly impact cluster performance.

JVM configuration: Proper JVM heap size configuration is critical for performance, but requires understanding of available system memory and Elasticsearch's specific requirements.

Cluster topology considerations: Users need to determine their deployment topology before making configuration decisions, including roles for different nodes (e.g., master, data, or client nodes).

Performance tuning complexity: Achieving optimal performance often requires fine-tuning multiple aspects, such as thread pools, circuit breakers, and node attributes.

Scaling challenges: As the cluster grows, managing indices and shards becomes more complex, with the potential to degrade performance if not handled correctly.

Searchcraft: Search Without the Struggle

Searchcraft was built with front-end developers in mind, removing the complexity that makes Elasticsearch so difficult to manage. Instead of requiring deep expertise in distributed systems, search algorithms, and infrastructure tuning, Searchcraft abstracts away those challenges—so you can focus on building great products. With an intuitive API, pre-built SDKs, and a streamlined setup process, even teams with zero search experience can integrate powerful, high-performance search in minutes.

Built for Front-End Teams

No backend expertise required; integrate search with just a few lines of code.

Fast, Easy Setup

Get up and running in minutes with pre-built SDKs and an intuitive API.

No Infrastructure Headaches

Searchcraft handles scaling, security, and optimization for you.

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Elasticsearch: Analytics… if you build it.

Elasticsearch forces teams to build their own dashboards using Kibana—an outdated, clunky interface that adds complexity and costs thousands in development and maintenance. While powerful, Kibana requires manual setup and integration, making it anything but user-friendly.

Easy Analytics Support

Elasticsearch

Searchcraft

Partial SupportSupport Through Kibana

YesAnalytics Available Out-of-the-Box

Searchcraft Analytics: Insight Without the Hassle

Searchcraft, on the other hand, delivers real-time analytics out of the box with a sleek, intuitive dashboard called Vektron. No extra setup, no wasted development time—just instant visibility into how users are interacting with your search.

With Searchcraft’s built-in analytics, teams can:

Measure User Behavior

See when and how users are searching in real time.

Identify Trends

Discover popular search terms and track click-through rates.

Improve Performance

Uncover gaps in search results and optimize content accordingly.

Unlike Kibana, Searchcraft’s analytics aren’t just for engineers—they’re valuable for marketing, product, and business teams looking for deeper insights. By eliminating unnecessary complexity, Searchcraft makes it easier than ever to turn search data into meaningful action.

Java vs. Rust: A Modern Search Needs a Modern Language

Elasticsearch is built in Java, a language known for its heavy resource consumption and complexity. While Java has its strengths, it comes with memory overhead, garbage collection pauses, and performance bottlenecks—all of which impact search speed and efficiency. For teams without backend expertise, managing Elasticsearch’s Java-based infrastructure can be a time-consuming challenge.

Underlying Language

Elasticsearch

Searchcraft

Java

Rust

Elasticsearch’s Java Complexity

Elasticsearch’s capabilities are extensive, but its Java-based architecture may pose challenges for young or front-end-focused development teams due to the complexity of Java and the need for backend expertise in managing search infrastructure effectively.

Complexity and Learning Curve: Java is a powerful but complex language that can present a steep learning curve for developers who are primarily experienced in front-end technologies like JavaScript or HTML/CSS. Understanding Java’s syntax, object-oriented principles, and memory management can be challenging for those without a background in backend development.

Resource Management: Java applications, including Elasticsearch, require careful management of system resources such as memory and CPU. This can be daunting for teams unfamiliar with Java’s garbage collection and heap size configurations, which are crucial for optimizing Elasticsearch performance.

Development Environment Setup: Setting up a Java development environment can be more involved compared to front-end environments. It requires installing the Java Development Kit (JDK) and understanding build tools like Maven or Gradle, which may not be familiar to front-end developers.

Integration Challenges: While Elasticsearch offers client libraries in various languages, integrating it into a predominantly front-end stack might require additional effort to bridge the gap between the search backend and the user interface. This could involve learning new libraries or frameworks to facilitate communication between the frontend and Elasticsearch.

Maintenance and Upgrades: Managing an Elasticsearch deployment involves regular maintenance tasks such as monitoring performance, updating versions, and tuning configurations—all of which require a solid understanding of both Elasticsearch and Java.

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“I’m not a doctor, but I prescribe Searchcraft every day.”

Don MacKinnon

CTO, Searchcraft

Searchcraft and Rust

Searchcraft takes a different approach. Built in Rust, our search engine delivers high performance, low memory usage, and unmatched stability—without the bloat. Rust’s modern, memory-safe architecture ensures that Searchcraft runs lean, fast, and reliably, making it the smarter choice for teams that need a powerful search solution without the headaches of Java.

High Performance, Low Resource Usage

Rust delivers C++-level speed without the heavy resource demands. Searchcraft runs efficiently, handling high query volumes with fewer servers—saving teams on infrastructure costs.

Memory Safety Without Sacrificing Speed

Searchcraft eliminates common memory issues like data races and null pointer bugs. Rust’s ownership model ensures safe, reliable execution without the unpredictability of Java’s garbage collection.

Effortless Concurrency for High Traffic Loads

Built for multi-threaded performance, Searchcraft effortlessly handles simultaneous queries and traffic spikes without requiring extensive DevOps oversight.

Less Maintenance, More Reliability

Rust’s strict compile-time checks result in fewer runtime errors and reduced debugging time, giving developers a stable, low-maintenance search engine that scales with them.

A Thriving Developer Ecosystem

As one of the fastest-growing languages, Rust offers a modern toolset and an active community—ensuring long-term support, evolving best practices, and seamless integrations.

Future-Proof Search Technology

Rust’s cutting-edge performance and long-term stability make Searchcraft a sustainable choice—built to grow with your business without costly rewrites or tech debt.

Which Search Solution Is Right for You?

Elasticsearch

Elasticsearch and Searchcraft serve different needs. If your team requires log search, vector search, or deep Elastic ecosystem integrations, Elasticsearch remains a powerful choice—especially for organizations with dedicated backend engineers who can maintain its infrastructure and optimize performance. For those already embedded in the Elastic Stack, sticking with Elasticsearch may make sense.

Choose Elasticsearch if you:

Need log search, vector search, or semantic search

Have a dedicated backend team to manage infrastructure

Are already deeply invested in the Elastic ecosystem

Searchcraft

For teams that need a fast, developer-friendly search engine without the complexity, Searchcraft is the better fit. Designed for front-end, Typescript developers, it empowers teams to integrate powerful search without backend expertise. Whether you’re a startup needing speed-to-market or a large company looking to cut infrastructure costs and DevOps overhead, Searchcraft delivers lean, efficient, high-performance search without the bloat.

Choose Searchcraft if you:

Want a fast, easy-to-implement search solution

Need content search that scales effortlessly

Prefer a tool that front-end developers can manage

Search shouldn’t be a bottleneck. Whether you’re scaling a massive enterprise platform or launching a new startup, picking the right search tool is about balancing power with efficiency. If you need a modern, lightweight, and cost-effective search solution, it’s time to give Searchcraft a try. 🚀

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Engineered for modern needs, Searchcraft enables businesses to implement robust, high-performance search functionality into any digital application within minutes. Ditch archaic, bloated earthbound systems like Elasticsearch, OpenSearch, and Algolia. Travel at light-speed to the next frontier in search tools with Searchcraft.

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Elasticsearch is a product of Elastic NV, with whom Searchcraft Inc., is not affiliated. All trademarks and copyrights are property of their respective owners.