If your logs are taking too long to search or costing too much to store, you’re not alone.

As systems get more complex, the amount of logs we generate keeps growing. From microservices to cloud environments, every part of an application is now producing machine data, and teams need to make sense of it quickly. That means faster search, smarter filtering, and better ways to store and manage all this log data without breaking the budget.

For years, Splunk and Elastic (or ELK Stack) have been the go-to options for log management. Both platforms have been used by large enterprises, DevOps teams, and security teams for everything from application monitoring to Security Information and Event Management (SIEM). They have strong reputations, but also come with costs, setup challenges, and growing complexity.

So what still sets them apart in 2025?

This blog compares Elastic and Splunk side by side, focusing on the three things: log search speed, cost of ownership, retention, and storage trade-offs. We will also look at what’s missing and why modern platforms like Kloudfuse outshine both in many setups.

Overview of Elastic and Splunk

What is Elastic?

Elastic is best known for the ELK Stack, which stands for Elasticsearch, Logstash, and Kibana. It is a widely adopted log management and aggregation framework used for everything from basic log analysis to advanced data pipelines and application monitoring.

It supports a modular setup, where logs are collected and parsed through Logstash or Beats, indexed in Elasticsearch, and visualized in Kibana. Some teams also use OpenSearch, which follows a similar architecture and query style.

Elastic is widely adopted in both cloud-native and on-prem setups due to its open-source flexibility and strong community support.

Pros of Elastic

• Open-source base with wide adoption.

• Uses Elasticsearch Query DSL for detailed queries.

• Highly customizable for data analytics and dashboards.

• Supports searchable snapshots and tiered storage for cost control.

• Integrates well with Security Information and Event Management (Elastic SIEM).

Cons of Elastic

• Has a learning curve and requires expertise to configure, scale, and maintain the platform.

• Data ingestion pipelines can become complex.

• Performance may degrade as data volume and cluster size grow.

• Cost increases with storage, support, and high availability setup.

• Licensing can be confusing (Elastic vs OpenSearch forks).

• Resource-intensive when handling multiple tasks like indexing, searching, and data aggregation.

What is Splunk?

Splunk is a commercial log management solution that helps teams monitor, search, and analyze machine data. It uses an indexing system paired with Search Processing Language (SPL) to make logs instantly searchable after ingestion.

Splunk is commonly used for log monitoring, data integration, and Security Information and Event Management (via Splunk Enterprise Security). It is available as both a self-hosted platform (Splunk Enterprise) and a SaaS platform (Splunk Cloud).

Pros of Splunk

• Easy-to-use search interface with SPL.

• Fast setup via Splunk Cloud (managed option).

• Built-in support for security data and compliance use cases.

• Strong alerting, dashboards, and log visibility tools.

• Works well in large enterprise and compliance-heavy environments.

Cons of Splunk

• High licensing costs for large log volumes.

• Complex data management at scale.

• Closed ecosystem with fewer customization capabilities.

• Long-term data storage and retention can get expensive.

Use Case Overlap: Where Both Are Used

Both Splunk and Elastic are used for a wide range of observability and security tasks

Log Search and Log Analytics

Both platforms are designed to ingest, index, and search machine data at scale.

• Elastic relies on Elasticsearch Query DSL and Lucene for fast full-text search, often used with Kibana dashboards.

• Splunk uses Search Processing Language (SPL) with an indexing system that allows for real-time log search within hot data.

Security Information and Event Management (SIEM)

Both platforms are used for detecting policy violations, unauthorized access, threat monitoring, and compliance audits, though they take different approaches:

• Splunk Enterprise Security is a dedicated SIEM product used by security and compliance teams.

• Elastic SIEM is part of Elastic Observability and integrates with Elastic’s broader stack, including endpoint and threat detection tools.

Application Monitoring and Log Monitoring

Application logs from services, containers, and VMs can be streamed into either system for monitoring. Both platforms help teams track down bottlenecks, slowdowns, or outages across microservices and infrastructure layers.

• Elastic Observability integrates APM agents, metrics, logs, and uptime monitoring into a central view.

• Splunk integrates application logs with metrics and traces (via Splunk Observability Cloud), giving DevOps and engineering teams full visibility into system performance and latency issues.

Data Pipelines and Ingestion

Whether using Beats, Logstash, or forwarders, both Elastic and Splunk provide ways to build data ingestion pipelines from various sources:

• Kubernetes

• AWS/GCP/Azure services

• Container logs

• Custom app logs

• Syslogs and audit logs

These logs feed into processing pipelines where parsing, filtering, and enrichment occur before storage or indexing.

A quick summary table:

1. Log Search Speed: Real-Time Queries and Performance

When logs start piling up, search speed becomes the first pain point. Whether you're dealing with a production outage or debugging an incident, nobody wants to wait minutes for results. Here's how Splunk and Elastic handle search, and what slows each down at scale.

Log Storage Architecture

Before diving into indexing and search performance, it is important to understand how each platform stores and structures log data. The underlying storage model determines how logs are written, queried, and managed over time. Splunk and Elastic follow fundamentally different approaches; one is built around time-series events, while the other is document-based. Here’s how their storage models compare side by side:

Indexing Model

Splunk and Elastic both rely on indexing to make logs searchable, but how they build and structure those indexes is fundamentally different. These differences impact everything from ease of implementation to search performance to storage cost and retention trade-offs.

Splunk uses a time-series event-based model where logs are parsed into events, timestamped, and tagged with metadata like host, source, and sourcetype. This data is then indexed at the time of ingestion using a proprietary format, stored in buckets that transition from hot to frozen based on age. The index includes both raw event data and a TSIDX file (a time-series index), enabling fast queries on recent logs.

However, this upfront processing, which uses schema-on-ingest and early field extraction, comes at a cost. Since indexing happens during ingestion, it increases CPU and disk usage early on. Splunk also stores duplicate data (raw + index), which inflates storage requirements. Licensing is tightly coupled with the volume of indexed data, meaning higher ingestion equals higher cost, regardless of how often the data is queried.

Elastic, by contrast, uses a document-based model built on Apache Lucene. Logs are ingested as JSON documents, passed through pipelines (e.g. Logstash or Beats), and indexed into structured fields using a schema-on-write approach. This field extraction process often requires configuration by an operator or developer, especially when working with unstructured log formats. The index is split into shards and segments, allowing for full-text search, field filtering, and aggregations through Elasticsearch Query DSL.

While Elastic gives teams more control over field mappings and indexing behavior, it also requires more upfront planning. Poor mapping decisions or misconfigured shards can lead to slow queries, memory issues, and tuning overhead. Additionally, Elastic’s open data structure, flexible query capabilities, and integration with Kibana dashboards make it highly customizable for log analytics and observability use cases.

Quick summary:

• Splunk prioritizes fast time-based queries with minimal schema planning, but adds cost via ingest-time indexing and proprietary formats.

• Elastic offers transparent indexing and powerful full-text search, but needs careful schema mapping and tuning for performance at scale.

Both tools introduce complexity when working with large datasets, especially when search needs grow beyond hot storage or when logs aren’t tagged consistently. That’s where modern platforms like Kloudfuse start to look more attractive, offering instant search with schema-on-read, no indexing delays, and lower storage overhead.

Query Language: SPL vs DSL

The way you query logs matters, especially when troubleshooting under pressure. Splunk and Elastic take very different approaches to query languages, each with its strengths and trade-offs depending on who’s writing the queries and how the data is structured.

Splunk uses Search Processing Language (SPL), a proprietary, pipeline-based syntax built for event-driven data. It’s command-driven and designed for operational teams who need to run quick searches, stats, and filtering on indexed logs. Field extraction often happens at search time, which adds flexibility but can slow performance on complex queries.

Elastic, on the other hand, uses Query DSL, a JSON-based language that requires field mappings to be defined at index time. It’s powerful, especially for developers who want to integrate queries into code or build dashboards with fine-tuned filters, but it can be verbose and harder to write manually without tooling like Kibana or Postman.

A quick comparison:

As your log volume and use cases grow, these differences can shape how quickly teams adapt and how easily they build automation around their queries. Platforms like Kloudfuse take this further by supporting multiple open query formats, including FuseQL, LogQL, PromQL, and more, so teams aren’t locked into a single query language or ecosystem.

Search Performance at Scale

In the early stages, both Splunk and Elastic perform well, but things shift quickly once log data scales into terabytes, query complexity rises, and longer retention windows become necessary.

Before diving into bottlenecks, it is worth understanding the architectural choices that shape how each platform performs under pressure:

Splunk shines when querying hot data; recent logs stored in active TSIDX indexes are retrieved with minimal delay. Its event-based search engine is optimized for timestamp lookups, making it effective for real-time dashboards and short-term debugging.

However, as logs age or queries span longer time ranges, the cracks start to show:

• Cold or frozen data must be rehydrated before search, adding latency.

• Index-time parsing increases ingest time but helps accelerate downstream queries.

• Scaling search requires additional indexers or clusters, pushing up costs and architectural complexity.

• Performance can dip under concurrent load (multiple users, overlapping dashboards).

Splunk’s architecture is optimized for recent data, analytic-based, and SIEM-style investigations, but scaling it across high cardinality fields or multi-region setups demands heavy investment.

Elastic was built with distributed systems in mind. Its shard-based scaling model allows queries to run in parallel across nodes, boosting performance for structured and full-text searches alike. For teams managing complex observability use cases, that flexibility is a win.

But it’s not without trade-offs:

• Shard count and sizing must be tuned carefully, too many shards slow things down, while oversized ones increase memory pressure.

• Performance drops with unoptimized queries, especially those involving wildcards, nested JSON fields, or dynamic mappings.

• Cold or snapshot-tier data is slower to access unless specifically optimized with caching or lifecycle policies.

• High cardinality fields (e.g., user IDs, version tags) can create index bloat and slow filters or aggregations.

Elastic performs well when managed tightly, but it requires constant tuning to stay efficient as datasets grow and diversify.

As teams move toward high-volume environments with dynamic workloads, both platforms introduce growing complexity, whether it’s scaling hardware or optimizing queries.

That’s why many engineering teams are now exploring alternatives that eliminate indexing bottlenecks, reduce tuning requirements, and deliver predictable performance at any scale.

Cold Storage, Hot Indexes, and Delay

Splunk uses data tiers: hot, warm, cold, and frozen. Only hot and warm data is quickly searchable. Cold data is slower. Frozen data usually isn’t searchable unless restored manually.

Elastic offers tiered storage as well, hot, warm, cold, and searchable snapshots on object storage like S3. While it’s more flexible than Splunk’s model, search performance drops the farther the data is from primary storage.

Limitations with Large Datasets

At scale, teams often hit limits such as:

• Slow queries on high-cardinality data

• Delayed searches across multiple indices

• Memory-intensive aggregation queries

• Inflexible filters due to rigid indexing

These issues become more visible when handling data analytics across multiple services, when logs aren’t tagged properly, or when search clusters are overloaded.

2. Cost Breakdown: Licensing, Storage, and Scaling

When evaluating Splunk vs Elastic, pricing is often where teams get stuck. On the surface, Elastic appears more flexible due to its open-source roots, but the total cost of ownership can still add up. Splunk, on the other hand, is known for performance, but not for affordability.

Let’s break down the hidden and visible costs of each log management solution.

Elastic: Open-Source Base, but Not Free

Elastic starts as an open-source log management platform (ELK Stack), which makes it appealing to teams trying to avoid licensing fees. However, running Elastic in production often requires:

• High-spec infrastructure for Elasticsearch clusters

• Dedicated engineering for performance tuning

• Paid plugins, monitoring, and support from Elastic or third-party vendors

Storage costs also rise with time. Teams often need to offload logs to object storage like Amazon S3 and use searchable snapshots to manage long-term data retention.

The biggest cost driver? Scaling to handle production workloads.

Splunk: Predictable Model, Expensive at Scale

Splunk uses a licensing model based on the daily data volume ingested. While this makes budgeting easy to understand, it becomes expensive for teams with growing logs or longer retention periods.

Even with Splunk Cloud, the pricing includes multiple layers:

• Licensing (based on GB/day)

• Storage beyond hot/warm tiers

• Support contracts

• Extra costs for archiving, apps, and long-term searchability

3. Retention and Storage Efficiency

As logs grow over time, teams need to strike a balance between cost, performance, and compliance. How well a tool handles log retention plays a big role in long-term planning, especially when working with high data volume or strict data policies. This is where both Splunk and Elastic start to show limitations.

Elastic: Tiered Storage and Searchable Snapshots

Elastic uses a tiered storage model that breaks data into hot, warm, cold, and frozen tiers. Logs stored in the hot tier are fast to query, but costly to keep there long-term. As logs age, they can be moved to colder tiers or even to object storage (such as S3) using searchable snapshots.

Searchable snapshots allow teams to run queries without fully restoring the data, which helps reduce data storage costs. But search speed can slow down when you're working with logs from weeks or months ago. Configuring lifecycle policies and managing this flow adds operational overhead, especially for teams with limited resources.

Splunk: Hot, Warm, Cold, and Frozen Tiers

Splunk also splits logs into hot, warm, cold, and frozen data tiers. Only hot and warm data is immediately searchable. Cold data is stored for compliance but often comes with slower access. Frozen data isn’t searchable at all unless it’s rehydrated and re-indexed, which takes time and effort.

Long-term log retention in Splunk results in higher costs. Since Splunk charges based on data volume, storing more logs, especially if you want to keep them searchable, can drive up licensing and infrastructure expenses quickly.

Teams often end up archiving logs outside of Splunk (such as in S3 or Glacier) and lose the ability to query them directly. That creates blind spots during audits, security investigations, or historical debugging.

Trade-offs and Performance Impact

Both tools allow for long-term storage, but neither offers a smooth way to search older logs without compromise. As logs age and shift into colder tiers, teams are often forced to choose between performance and cost.

In Elastic, older logs are typically stored as searchable snapshots on object storage (like Amazon S3). These snapshots reduce cost but introduce query latency. While you can run searches directly on snapshots, they’re noticeably slower than querying hot or warm tiers, and require careful lifecycle management (ILM) to balance speed and storage.

In Splunk, archived logs in the frozen tier are no longer searchable by default. To query them, teams must first rehydrate (thaw) the data, restoring it into searchable form, which adds delay and operational steps. The cost of storing this data, especially if kept searchable, rises quickly with indexed volume.

Here’s the fundamental trade-off both tools present:

• Keep logs in hot/warm tiers for fast access, and pay more for compute and storage.

• Archive to cold/frozen storage to save on cost, but accept slower queries or extra rehydration steps.

For cloud-native teams working across dozens of services and high-volume environments, this trade-off becomes a recurring blocker. It often results in:

1. Missed visibility during investigations

2. Delayed response times

3. Constant tuning of lifecycle policies or storage rules

That’s why many teams are now exploring newer platforms that decouple retention from performance and offer cost-efficient storage without slow restores or indexing overhead.

With both Splunk and Elastic, extending log retention while keeping performance usable means spending more time tuning and more money on infrastructure.

Next, let’s look at which tool fits which type of team and why others are switching.

When to Choose Elastic or Splunk?

The right choice between Splunk vs Elastic depends on how your team handles data pipelines, infrastructure, compliance, and cost control.

Splunk is built for large enterprises that prioritize security and need bundled tools out of the box. It's often used by teams in finance, healthcare, and government who rely heavily on SIEM, compliance dashboards, and centralized log monitoring. 

Elastic appeals to teams that want more flexibility. It's often chosen by companies and developers managing their own infrastructure, especially those already familiar with open-source tools. If you have the in-house capacity to manage clusters and want to control your data ingestion pipelines, Elastic provides more room to customize without the upfront licensing costs of Splunk.

Full-Text Search and Schema Trade-Offs

Many companies choose Elastic for its full-text search capabilities, especially in industries where logs include large volumes of semi-structured or unstructured text data. Fields like eCommerce, media and publishing, and telecom often require precise keyword filtering, fuzzy matches, or natural language search across log entries.

Elastic’s schema-on-write model supports this well, allowing teams to predefine how each field is indexed and analyzed using custom mappings and analyzers. 

The trade-off? You need to build and manage your schemas upfront, which means more tuning, validation, and awareness of how your logs are structured at ingestion.

In contrast, platforms like Splunk use schema-on-read, where field extraction happens during the query phase. This offers more flexibility during ingestion, especially when dealing with unknown or evolving log formats, but can slow down complex queries and limit certain types of analysis.

Hence,

• Choose Elastic if full-text search and deep indexing are core to your use case, and you have the resources to manage schema design and shard tuning.

• Choose Splunk if you want fast onboarding, schema flexibility, and pre-integrated tools, especially in compliance-heavy environments.

In general:

• Splunk fits best in high-security, compliance-driven setups.

• Elastic is better for cost-aware, DevOps-driven teams focused on log analytics and observability.

In terms of retention and storage efficiency, 

Choose Splunk if:

• You need strict index-based retention control for compliance or audit logging.

• You're okay with paying per GB ingested and prefer a fully managed experience.

• You often need fast rehydration of cold data for investigations or forensics.

Choose Elastic if:

• You want flexible tiering to handle large data volumes cost-effectively.

• You’re building a custom ingestion pipeline and can manage retention via ILM policies + snapshots.

• You need long-term archival, but don’t require frequent or real-time access to that data.

Now let’s talk about the growing shift toward newer tools built to solve these issues from the ground up.

Modern Alternative: Why Teams Look Beyond Splunk and Elastic

As log data grows, so do the headaches. 

For many teams, maintaining Splunk or Elastic has become more about managing complexity than solving problems. Index tuning, rising storage costs, query delays, and vendor-specific limitations often slow teams down, especially when logs are needed across systems, regions, and teams.

That’s where modern platforms like Kloudfuse step in.

Kloudfuse is built specifically for engineering teams who’ve outgrown the trade-offs of traditional tools. It skips the need for upfront indexing and offers a different approach: schema-on-read log ingestion. That means logs are stored as-is and made searchable instantly, without needing to define fields or pre-process the data.

Additionally, Log Archival and Hydration helps you store logs in low-cost object storage such as Amazon S3, retain them for as long as needed, and hydrate them instantly, without vendor lock-in, cold-tier delays, or retention-related charges.

Instead of relying on heavy indexing, Kloudfuse uses a patent-pending fingerprinting model that automatically extracts key facets such as severity, source, version, and trace context. These facets make it easier to slice and filter logs by what actually matters without custom tagging or parsing logic.

Search stays fast, even with high data volume, because logs aren’t bloated with manual index overhead and fingerprinting reduces storage by decoupling the static and dynamic parts of each log.

Unlike traditional systems that rely on index structures, Kloudfuse uses a columnar analytics engine (Apache Pinot) and fingerprinted facets for fast filtering and full-text search at scale, without index bloat.

Key Benefits of Kloudfuse (vs Traditional Tools)

• No indexing delays – logs are searchable immediately. You don’t have to wait for parsing or tagging before running a search, even on high-volume data.

• Schema-on-read – works with unstructured or semi-structured logs. This gives your team flexibility to ingest logs without needing predefined fields or mapping.

• Fingerprinting – groups similar logs, deduplicates noise, and auto-structures data. It extracts facets such as severity, version, and trace context to simplify filtering, fast analytics, and alerting.

• Flat pricing – no per-user charges or surprise overages. Pricing scales by log volume, and storage stays low-cost using your own cloud and S3 buckets.

• FuseQL – purpose-built query language, Fuseql is best for deep log filtering. Advanced users can run detailed searches without relying on third-party tooling or custom tags.

• Supports open formats – Engineers can use familiar languages to query logs, metrics, and traces without vendor lock-in with formats like LogQL, PromQL, and more.

• Self-SaaS (self-hosted) – deployed in your cloud for full data ownership. All logs stay within your VPC or cloud account, supporting internal compliance and privacy needs.

• High-Cardinality handling – handles high-cardinality workloads efficiently using Apache Pinot, ensuring performance even with logs that include millions of unique user IDs, versions, or services.

• Retention-friendly – store logs for as long as needed using your own cost-effective S3 buckets. Unlike Elastic Cloud, longer retention in Kloudfuse doesn’t increase cost, keeping your total cost of ownership predictable.

• Compliance-Ready Architecture – Kloudfuse's Self-SaaS model supports privacy and compliance requirements such as HIPAA, PCI-DSS, or GDPR by keeping all logs within your environment

With both Splunk and Elastic, you're constantly choosing between cost, speed, and control and the overhead of managing indexes, performance tuning, and storage tiers.

Kloudfuse was designed to remove that trade-off. For many DevOps, platform, and observability teams, it's a clean break from complex models, offering fast search, full control, and pricing that doesn’t punish growth.

In 2025, when logs are coming in from everywhere and decisions need to be made faster, more teams are looking beyond legacy stacks and choosing tools built for how systems actually work today.

Final Verdict: Splunk vs Elastic vs Kloudfuse Recap

Splunk is fast for recent logs and works well for security-driven teams, but the cost of storing and searching older data adds up quickly. Elastic gives you flexibility and control through open-source tools, but it requires constant tuning, and search slows down as data grows.

Kloudfuse takes a different path. It removes indexing delays by using schema-on-read and fingerprinting to make logs searchable immediately, while significantly lowering the cost of log observability at scale, especially for high-cardinality data and long-term storage. Logs can be easily archived and hydrated from your own object storage buckets, with powerful analytics enabled through facet extraction and a purpose-built query language.

If you're looking for a log management solution that’s fast, easy to scale, and offers advanced, easy-to-use features without dealing with data volume limits or retention trade-offs, then Kloudfuse is worth a serious look.

Start with Kloudfuse today and take control of your logs without the overhead!

Frequently Asked Questions

1. What is the difference between Splunk and ES?

Splunk is a commercial log management solution that uses a proprietary indexing system and SPL for queries. Elasticsearch (ES) is the core search engine behind Elastic Stack (ELK), using open-source Query DSL for full-text search. Splunk is licensed by data volume, while Elastic is self-managed and more customizable.

2. Which platform handles long-term log retention better?

Elastic offers searchable snapshots in object storage, while Splunk relies on frozen tiers that require manual rehydration. Both come with trade-offs, but neither is optimized for easy, low-cost retention at scale.

3. Is Splunk better than Elastic for SIEM?

Splunk has a mature SIEM product and is often used by security and compliance teams. Elastic offers Elastic SIEM as part of its observability suite, which is more flexible but may need more setup and tuning. Whether either is better for SIEM depends on your team’s needs and goals.

4. How does Kloudfuse compare to Splunk and Elastic in terms of cost?

Unlike Splunk and Elastic, which tie pricing to data ingestion and storage tiers, Kloudfuse offers flat pricing. You can choose from S to XL plans depending on your needs. Since it runs inside your own cloud, you can use existing cloud discounts and store logs on low-cost storage like S3.

5. How does Kloudfuse handle indexing?

Kloudfuse doesn’t rely on traditional indexing. Instead, it uses schema-on-read and fingerprinting to make logs searchable instantly. Key facets like severity, source, and trace context are auto-extracted from raw logs, so you can filter and analyze without building indexes or writing custom parsing rules. You go from raw, unstructured logs to structured, filterable facets in seconds, no indexing delays, no complex pipelines.

6. Can I use open query languages with Kloudfuse?

Yes. In addition to FuseQL, Kloudfuse supports popular formats like LogQL, PromQL, and TraceQL for flexible, vendor-neutral querying across logs, metrics, and traces.