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Elasticsearch has been a household name in the tech world for over a decade, launched in 2010, it promised a fast, scalable way to search and analyze massive datasets. For years, it delivered as well, powering everything from log analytics and observability dashboards to eCommerce product search and internal knowledge bases.
In its early days, Elasticsearch's open-source license and thriving community made it incredibly attractive. The ELK stack (Elasticsearch, Logstash, Kibana) became the go-to for organizations wanting powerful analytics without heavy licensing costs.
But as adoption grew, cracks began to appear. By 2018-2019, reports started surfacing of entire Elasticsearch clusters being wiped by ransomware because of unsecured default configurations. Imagine your production search cluster, storing months of operational logs, suddenly disappearing overnight. In some cases, attackers demanded ransom payments; in others, they simply destroyed the data.
Then came 2021. Elastic changed its license from Apache 2.0 to Server Side Public License (SSPL), triggering a very public dispute with AWS. AWS responded by creating OpenSearch. While that preserved an open-source option, the industry was left with fragmentation, uncertainty, and lingering concerns about vendor lock-in.
Fast forward to today: Elasticsearch is still powerful, but it has grown resource-heavy, expensive to operate, and increasingly complex. For many teams, especially those scaling rapidly, it's becoming harder to justify the operational overhead.
The Challenges Companies Face with Elasticsearch
If you've been running Elasticsearch at scale, the following might sound all too familiar.
1. Cluster Management Overhead
Elasticsearch clusters aren't “set and forget.” They require constant tuning: balancing shards, monitoring heap usage, scaling nodes up and down, reindexing when mappings change. For a small dataset, this is manageable. For petabyte-scale workloads, it turns into a full-time job.
2. Cost Spirals
Every gigabyte of data you ingest, every index you maintain, has a cost that's not just in storage, but in compute. Hot nodes running on expensive SSDs, memory-heavy configurations, and overprovisioned clusters for peak loads can quickly blow through budgets. If you're retaining historical data for compliance or analytics, the price tag can feel punitive.
3. Performance Degradation Over Time
The larger the cluster, the more prone it is to bottlenecks. Queries that once took milliseconds start creeping into seconds. Merges run long, shard imbalances cause hotspots, and ingestion starts to lag behind, if your search powers customer-facing applications, it's a direct hit to SLAs.
4. Operational Risk
Elasticsearch has a history of security incidents, often from unsecured clusters being exposed to the public internet. Even with proper configuration, ransomware remains a threat. Restoring from snapshots can take hours or days, which is unacceptable for mission-critical search.
5. Licensing and Vendor Lock-Ink
The SSPL licensing change introduced uncertainty for organizations committed to multi-cloud or on-prem strategies. While OpenSearch exists, it's not a drop-in future-proof guarantee, and migrating between forks or vendors still carries cost and risk.
When Is It Time to Move Away from Elasticsearch?
Moving off a core system like Elasticsearch is a big decision but there are telltale signs it's time.
If yourcloud bills keep climbing despite careful tuning,if your engineers are spending more time firefighting infrastructure than shipping features, or if you're struggling to meet performance targets even after scaling up hardware, you're at a decision point.
Other triggers include compliance mandates that require stronger data isolation, or a shift to data lake architectures where Elasticsearch's hot storage model becomes inefficient.
A good rule of thumb: if your leadership team has had more than three “Why is search so slow/expensive?” conversations in the past quarter, it's time to evaluate alternatives.
What to Look for in an Elasticsearch Alternative
Not all search platforms are created equal. Your choice should reflect your business priorities, not just technical specs. For example, if you're in cybersecurity, you might prioritize real-time analytics and immutable storage for incident forensics. If you're running a SaaS product with global users, latency and multi-region availability may be non-negotiable.
Here are some key factors to weigh when looking for alternative:
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Accuracy vs. Recall
Does your workload require perfect precision, or is breadth of results more important?
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Latency Requirements
Can the platform serve results in milliseconds, even from cold storage?
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Scalability
Will it handle a 10x data growth without a rewrite?
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Integration
Does it fit with your pipelines (S3, Kafka, Iceberg) without fragile glue code?
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Security & Compliance
Will it help you meet GDPR, HIPAA, SOC 2 without patchwork solutions?
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Cost Predictability
Will your bill scale linearly with value, or explode unpredictably with usage spikes?
Three Paths Beyond Elasticsearch in 2025
When companies decide to move on, they typically follow one of these approaches:
1. Build Your Own Search Stack
Some engineering teams roll up their sleeves and build on Apache Lucene or Solr, layering ingestion, storage, and APIs in-house. While this offers ultimate control, you'll still need in-house experts, a robust ops team, and a tolerance for long development cycles.
2. Mach5 Search: A Modern, Purpose-Built Alternative
Mach5 Search was built to give teams Elasticsearch-level power without the operational baggage. Instead of running hot nodes, it's native to object storage meaning you can store everything in S3, GCS, Databricks, Azure Blob and other integrations, and still get lightning-fast search, even on cold data. Workload isolation ensures ingestion never slows down search. Multi-model storage lets you choose row, column, or index formats at the field level. With immutable storage layers and isolated components in the situation of a ransomware, you'll still be able to access your data. Additionally our usage-based pricing keeps costs predictable. The result? You can scale without scaling your DevOps headaches.
3. Use a Niche or Vertical Solution
Some workloads don't need general-purpose search. Algolia shines in eCommerce and site search. Splunk is strong in security analytics, though expensive at large scale. Typesense is developer-friendly for smaller datasets.
Conclusion
Elasticsearch's role in the history of search is undeniable. But in 2025, the question isn't whether it works? it's whether it's the best fit for your next five years.The right alternative will align with your cost, performance, and compliance goalswithout trapping you in the same operational grind you're trying to escape.
If you're tired of constant shard rebalancing, JVM tuning, and 3 a.m. calls for cluster outages, it might be time to explore something better.
1
What are the alternatives to Elasticsearch in 2025 ?
Popular Elasticsearch alternatives include OpenSearch, Algolia, Typesense, Aiven, and emerging cloud - native engines like Mach5.While these tools solve specific parts of the search problem, Mach5 stands apart by delivering Elasticsearch - level speed with 90 % lower storage cost, multi - warehouse compute isolation, and zero cluster maintenance.
Unlike Elastic and OpenSearch, which require large clusters, reindexing, and complex scaling, Mach5 runs natively on object storage(S3/GCS/Snowflake) and offers sub - second search on massive datasets without expensive infrastructure.
2
When should companies start looking for an Elasticsearch alternative ?
Companies should consider moving away from Elasticsearch when the system becomes harder and more expensive to operate as data scales. Clear signals include: - Cluster sprawl (adding more nodes just to maintain performance) - Unpredictable scaling behavior as ingest rates or query volumes increase - Frequent reindexing that slows teams down - High RAM + SSD costs to support Elastic’s hot/warm tiers - Slow search on large or historical datasets - Growing operational overhead from tuning shards, replicas, heap sizes, and JVM settings
These problems typically emerge for teams handling logs, security events, observability signals, or multi-tenant SaaS workloads. At this stage, companies benefit from considering alternatives like Mach5, which offers object-storage indexing, multi-warehouse compute isolation, and 50–90% lower cost—without the operational complexity of Elasticsearch.
3
How does Mach5 compare to Elasticsearch for large - scale search and analytics ?
Elasticsearch tightly couples compute and storage and requires large, memory - heavy clusters to maintain performance, especially for high - cardinality data like logs, SIEM events, and observability signals.
Mach5 takes a different approach :
- Runs entirely on object storage(no hot/warm tiers).
- Uses immutable, compressed indexes for 20×–30× lower storage footprint.
- Offers multi - warehouse architecture so dashboards, analytics, and batch workloads never compete for resources.
- Requires zero cluster tuning and supports SQL + MQL without reindexing.
This makes Mach5 a superior choice for teams wanting Elasticsearch performance without Elasticsearch costs.
4
What factors should I consider when choosing an Elasticsearch alternative ?
The most important criteria include :
- Scalability without constantly adding more nodes
- Cost per TB stored
- Ability to separate compute for different workloads
- Query performance on cold data
- Operational overhead(tuning, reindexing, cluster failures)
- Compatibility with Elastic - style search
Mach5 is built specifically to fix each of these weaknesses by using object - storage indexes, elastic compute warehouses, and a zero - maintenance architecture.
5
Is Mach5 a good Elasticsearch replacement for logs, security, and observability ?
Yes.Mach5 excels for log analytics, SIEM workloads, threat investigations, and observability because it supports :
- Sub - second search on petabyte - scale data
- Real - time ingestion without cluster pressure
- Low-cost retention using object storage instead of Elastic’s hot/warm/cold architecture
- Isolated compute warehouses for dashboards, threat hunts, and heavy analytics
Teams replacing Elasticsearch with Mach5 typically see 50–90% cost reduction and faster search performance across all workloads.
