ClickHouse vs Parseable: Columnar Analytics for Log Data (2026)

Introduction

Columnar storage has become the default architecture for log analytics at scale. The reasons are straightforward: logs are append-only, queries are analytical (aggregations, filters, time-range scans), and compression ratios on columnar formats dramatically outperform row-oriented storage. If you are evaluating columnar engines for your observability stack in 2026, two names come up frequently: ClickHouse and Parseable.

If you are evaluating ClickHouse vs Parseable for your observability stack, the differences run deeper than benchmarks. ClickHouse is a general-purpose OLAP (Online Analytical Processing) database that happens to be excellent at log analytics. Parseable is a unified observability platform purpose-built for logs, metrics, events, and traces from the ground up. Both use columnar storage. Both handle high-throughput ingestion. Both support SQL. But the architectural decisions underneath are fundamentally different, and those decisions have massive implications for your operational burden, storage costs, and long-term flexibility.

This comparison is written for data engineers, platform teams, and SREs who are evaluating columnar stores for observability workloads. We will go deep into the architecture, quantify the cost differences, and address the "SigNoz uses ClickHouse" angle directly. If you have been considering ClickHouse for log storage, whether directly or via a platform built on top of it, this guide will help you understand exactly what you are signing up for.

Architecture: MergeTree on Local Disk vs Parquet on S3

The architectural differences between ClickHouse and Parseable are not surface-level. They represent two fundamentally different philosophies about where data should live, how it should be organized, and who should manage it.

ClickHouse: MergeTree Engine on Local Disk

ClickHouse stores data using its MergeTree family of table engines. MergeTree is a log-structured merge tree that writes data to local disk in sorted, compressed column files. As new data arrives, ClickHouse writes it to "parts" (batches of sorted data). Background merge processes periodically combine smaller parts into larger ones, maintaining sort order and improving query performance.

This architecture requires fast local storage. ClickHouse's query performance is directly tied to disk I/O throughput. In production, this means NVMe SSDs, not spinning disks, not network-attached storage, and certainly not object storage. The MergeTree engine assumes low-latency, high-IOPS local storage for both writes and reads.

A typical production ClickHouse deployment includes:

• Multiple ClickHouse nodes (minimum 3 for replication, often 6-12+ for production workloads)
• ClickHouse Keeper (or ZooKeeper) for distributed coordination, replication management, and DDL operations
• NVMe SSD storage on each node (typically 2-8 TB per node)
• Substantial RAM for query processing, caching, and merge operations (64-256 GB per node)
• Network infrastructure for inter-node replication traffic

The MergeTree engine is undeniably powerful for analytical queries. ClickHouse's vectorized query execution, columnar compression (LZ4, ZSTD), and sparse indexing deliver impressive query performance on well-structured data. But this performance comes with an operational cost: you are managing a distributed database cluster with all the complexity that entails.

Parseable: Apache Parquet on S3 Object Storage

Parseable takes a fundamentally different approach. Instead of managing data on local disks across a cluster, Parseable writes data directly to S3-compatible object storage in Apache Parquet columnar format. The Parseable binary handles ingestion, buffering, columnar conversion, and query execution, while S3 handles durability, availability, and scaling of the storage layer.

A production Parseable deployment consists of:

• A single Parseable binary (or multiple instances behind a load balancer for high availability)
• An S3-compatible object store (AWS S3, Google Cloud Storage, Azure Blob, MinIO)

That is the entire architecture. There is no cluster coordinator. No replication configuration. No shard management. No merge process tuning. No local disk provisioning. The storage layer is S3, which provides 99.999999999% (eleven nines) durability, automatic replication across availability zones, and unlimited capacity without any operational intervention.

Parseable uses Apache Parquet as its storage format. Parquet is an open, standardized columnar format with excellent compression characteristics and broad ecosystem support. Your observability data stored by Parseable can be read by any tool that understands Parquet: Apache Spark, DuckDB, Pandas, Polars, Trino, AWS Athena, and dozens of others. There is no proprietary format lock-in.

The Operational Gap

The operational difference between these two architectures cannot be overstated. Here is what managing a ClickHouse cluster requires on an ongoing basis:

For a platform team running ClickHouse in production, these tasks consume real engineering hours every week. A typical 6-node ClickHouse cluster with replication and Keeper requires 0.5 to 1.0 full-time engineers for ongoing management, capacity planning, and incident response. That is $75,000 to $150,000 per year in personnel costs before you account for the infrastructure itself.

Purpose-Built Observability vs General-Purpose OLAP

ClickHouse is a general-purpose OLAP database. It was designed to run fast analytical queries on structured data across a wide range of use cases: web analytics, business intelligence, financial modeling, ad-tech, IoT telemetry, and yes, log analytics. It is excellent at all of these because it is a general-purpose tool optimized for columnar analytical workloads.

Parseable is purpose-built for observability. Every design decision, from the storage format to the query interface to the ingestion pipeline, was made with logs, metrics, events, and traces as the primary workload. This specialization shows up in several critical areas.

Native OTLP Endpoint

Parseable provides a native OpenTelemetry Protocol (OTLP) endpoint. You can point your OpenTelemetry Collector at Parseable and start ingesting logs, metrics, and traces immediately over both HTTP and gRPC. No adapter, no translation layer, no custom plugin.

ClickHouse has no native OTLP endpoint. To ingest OpenTelemetry data into ClickHouse, you need an adapter or an intermediary service that receives OTLP data, transforms it into ClickHouse's table schema, and inserts it via ClickHouse's native protocol or HTTP interface. Projects like SigNoz and the OpenTelemetry ClickHouse exporter fill this gap, but they add another component to deploy, monitor, and maintain. If you are using ClickHouse directly for log analytics, you need to build or adopt an ingestion pipeline.

Built-In Console and Visualization

Parseable ships with a built-in web console for querying, exploring, and visualizing your observability data. From the moment you start Parseable, you have a functional UI for searching logs, building dashboards, and configuring alerts. There is no separate visualization layer to deploy.

ClickHouse has no built-in visualization layer. You need a separate frontend: Grafana, Metabase, Superset, Redash, or a custom application. Each of these adds deployment complexity, configuration overhead, and another system to maintain. Grafana is the most common pairing, but connecting Grafana to ClickHouse requires the ClickHouse data source plugin, properly configured data source connections, and dashboards built from scratch.

Full MELT Observability in a Single Platform

Parseable provides unified ingestion, storage, and querying for all four pillars of observability: Metrics, Events, Logs, and Traces (MELT). All signal types flow through the same ingestion endpoint, are stored in the same columnar format on S3, and are queried with the same SQL interface. Cross-signal correlation is a JOIN operation, not a context switch between tools.

ClickHouse stores whatever you put into it, but it does not understand the semantics of observability data. It does not know that a trace ID in your logs table corresponds to spans in your traces table. It does not provide automatic correlation between metrics spikes and log error bursts. These capabilities must be built in the application layer above ClickHouse, which is exactly what projects like SigNoz do. But if you are evaluating ClickHouse directly, you are building these capabilities yourself.

AI-Native Analysis

Parseable integrates AI-powered analysis directly into its query workflow. Natural language queries, anomaly detection, and intelligent alerting are built into the platform. Engineers can describe what they are looking for in plain language, and Parseable translates that intent into SQL queries against the observability data. This is not a bolted-on feature; it is integrated into the core query experience.

ClickHouse provides no AI-native analysis capabilities. It is a database engine. AI-powered observability features would need to be built as a separate application layer consuming ClickHouse data.

Storage Cost Comparison: The Math Matters

Storage cost is where the S3-native architecture delivers its most quantifiable advantage. Let us work through the numbers.

ClickHouse Storage Costs

ClickHouse requires NVMe SSD storage on each node. In AWS, NVMe-backed instances (i3, i4i families) or gp3/io2 EBS volumes are the standard choices.

Scenario: 100 GB/day raw log ingestion, 30-day retention

With ClickHouse's compression (typically 5-10x on log data with ZSTD), 100 GB/day compresses to roughly 10-20 GB on disk. Over 30 days, that is 300-600 GB of compressed data per replica.

With 2 replicas (standard for durability), you need 600-1,200 GB of NVMe storage across the cluster, plus headroom for merges and temporary data (typically 2x working set), bringing the total to 1.2-2.4 TB of NVMe.

• NVMe storage cost (i4i instances): i4i.xlarge provides 937 GB NVMe, costs $0.442/hour ($322/month). You need 2-3 instances just for storage.
• Compute cost for 3 ClickHouse nodes: ~$966-$1,288/month (i4i.xlarge x 3-4)
• Keeper cluster (3 nodes, t3.medium): ~$93/month
• Total infrastructure: ~$1,059-$1,381/month, or $12,700-$16,600/year

At 500 GB/day, the numbers scale roughly linearly but with additional complexity: you need more nodes, more careful shard planning, and the Keeper cluster handles more coordination work. Expect $50,000-$80,000/year in infrastructure alone.

At 1 TB/day, you are looking at a 12+ node cluster with dedicated Keeper nodes, multiple shards, and at least one engineer spending significant time on cluster management. Infrastructure costs reach $100,000-$160,000/year, plus $75,000-$150,000 in personnel costs.

Parseable Storage Costs

Parseable stores data on S3 in compressed Parquet format. S3 Standard storage costs $0.023/GB/month in us-east-1.

Scenario: 100 GB/day raw log ingestion, 30-day retention

With Parquet compression (typically 8-10x on log data), 100 GB/day compresses to roughly 10-12 GB on disk. Over 30 days, that is 300-360 GB of compressed data.

• S3 storage: 360 GB x $0.023/GB/month = ~$8.28/month
• S3 PUT/GET requests: ~$5-15/month (depends on query volume)
• Parseable compute (single instance, c6g.xlarge): ~$98/month
• Total infrastructure: ~$111-$121/month, or $1,340-$1,450/year

At 500 GB/day:

• S3 storage: 1,800 GB x $0.023 = ~$41/month
• Parseable compute (2 instances for HA, c6g.2xlarge): ~$392/month
• Total: ~$440/month, or ~$5,280/year

At 1 TB/day:

• S3 storage: 3,600 GB x $0.023 = ~$83/month
• Parseable compute (3-4 instances, c6g.2xlarge): ~$588-$784/month
• Total: ~$671-$867/month, or ~$8,000-$10,400/year

Side-by-Side Cost Comparison

The critical insight is what happens when you extend retention. With ClickHouse, longer retention means more NVMe storage on more nodes, which means proportionally higher costs. With Parseable on S3, longer retention means more S3 objects at $0.023/GB/month. You can also leverage S3 Intelligent Tiering or S3 Glacier for archival data, dropping costs to $0.004/GB/month or lower. There is no equivalent in ClickHouse because the data must remain on local NVMe to be queryable.

For organizations that want to keep 12 months of log data queryable, the cost gap between ClickHouse and Parseable widens from 10x to 30x or more.

Quick Comparison Table

The SigNoz Angle: Choosing ClickHouse Means Choosing Cluster Ops

This section addresses a question that comes up frequently: "SigNoz uses ClickHouse as its backend, and SigNoz is a well-regarded observability platform. Does that validate ClickHouse as the right choice for log analytics?"

SigNoz is a strong project. It provides a polished UI, native OpenTelemetry support, and unified logs, metrics, and traces. It chose ClickHouse as its storage engine because ClickHouse delivers excellent query performance for analytical workloads. That is a technically sound decision for SigNoz's architecture.

But here is what teams discover when they deploy SigNoz (or any ClickHouse-backed platform) in production: you are now operating a ClickHouse cluster. The ClickHouse cluster does not manage itself. It needs:

• Disk space monitoring and capacity planning. ClickHouse nodes that run out of disk space can corrupt data or crash. You need alerting on disk usage and a plan for adding capacity before you hit limits.
• Keeper/ZooKeeper management. The coordination layer for replication and distributed DDL is a separate cluster that must be monitored, upgraded, and occasionally recovered.
• Merge process tuning. ClickHouse's background merge process competes with query workloads for CPU and I/O. At high ingestion rates, you may need to tune merge settings to prevent query latency degradation.
• Schema migrations across distributed tables. Altering table schemas in a distributed ClickHouse deployment requires careful DDL execution across all shards and replicas.
• Backup and disaster recovery. ClickHouse backups involve snapshotting data parts and coordinating with the replication state. Restoring from backup to a different cluster topology is non-trivial.
• Version upgrades. Rolling upgrades across a multi-node cluster require careful sequencing to avoid replication disruptions.

SigNoz abstracts some of this complexity through its application layer, but the ClickHouse cluster underneath still needs care and feeding. Teams that choose SigNoz's cloud offering offload this to SigNoz's ops team. Teams that self-host SigNoz inherit the full operational burden.

The question is not "Is ClickHouse good at analytical queries?" It is. The question is: "Do you want to operate a ClickHouse cluster for your observability data, or would you prefer your observability data to sit on S3 with zero cluster management?"

Parseable answers this question by eliminating the cluster entirely. Your data lives on S3. Parseable is a stateless query and ingestion layer. If a Parseable instance fails, you restart it and point it at the same S3 bucket. No data loss. No cluster recovery. No replication lag. No merge backlog.

Why Parseable: The Purpose-Built Advantage

Parseable is not a general-purpose database pressed into service for observability. It is a platform built from the ground up for the specific requirements of modern observability workloads. This section details why that distinction matters for engineering teams evaluating their options.

S3-Native Storage Eliminates Entire Categories of Problems

When your observability data lives on S3, you stop worrying about storage altogether. S3 provides:

• Eleven nines of durability (99.999999999%). Your data is more durable on S3 than on any local disk configuration, including RAID arrays and replicated ClickHouse clusters.
• Automatic scaling. There is no capacity planning for S3. You never run out of space. You never need to add nodes because your disks are full.
• Cross-region replication with a single configuration toggle. Disaster recovery for your observability data becomes trivial.
• Lifecycle policies that automatically tier data to cheaper storage classes (S3 Infrequent Access at $0.0125/GB/month, Glacier at $0.004/GB/month) based on age.
• Native versioning and audit trails for compliance requirements.

With ClickHouse, you get none of this for free. Durability comes from configuring ReplicatedMergeTree tables with proper replication factors. Scaling requires adding nodes and resharding. Disaster recovery requires backup tooling and tested restore procedures. Tiered storage requires TTL-based data movement between volumes, which adds configuration complexity and potential data availability gaps.

Rust: Performance Without the Operational Weight

Parseable is written in Rust, a systems programming language that combines C-level performance with memory safety guarantees. The practical implications for an observability platform are significant:

• Minimal memory footprint. Parseable starts with less than 50 MB of RAM. A production instance handling thousands of events per second typically uses 200-500 MB. Compare this to a ClickHouse node that recommends 64 GB of RAM minimum and can consume 128-256 GB under heavy query loads.
• No garbage collection pauses. Rust does not use a garbage collector, which means no GC pauses that cause latency spikes during ingestion or queries. ClickHouse (written in C++) also avoids GC, but its memory management requires significantly more headroom for merge operations and query processing.
• Single binary deployment. The entire Parseable platform, ingestion, storage, query engine, web console, alerting, AI analysis, compiles to a single binary. No dependencies, no runtime to install, no library conflicts. Deploy it with docker run or drop the binary on a server.
• Predictable resource consumption. Rust's ownership model and zero-cost abstractions mean Parseable's resource usage is predictable and proportional to workload. There are no surprise memory spikes from background merge operations consuming all available RAM.

For platform teams, this translates to dramatically simpler capacity planning. A Parseable instance on a c6g.xlarge (4 vCPU, 8 GB RAM, $98/month) can handle 100 GB/day of ingestion with responsive query performance. Achieving equivalent throughput on ClickHouse requires multiple nodes with 10-20x the aggregate RAM.

SQL for Observability: Familiar Yet Specialized

Both ClickHouse and Parseable support SQL, but with important differences.

ClickHouse implements its own SQL dialect with extensions for analytical workloads. It is powerful and feature-rich, supporting window functions, array functions, approximate query processing (HyperLogLog, quantiles), and specialized time-series functions. However, it is a general-purpose SQL dialect designed for any analytical workload, not specifically for observability.

Parseable's SQL interface is purpose-built for observability queries. The query engine understands the structure of log data, trace spans, and metric time-series natively. Common observability patterns like "show me all errors in the last hour grouped by service" or "find the slowest trace spans for this endpoint" are expressed naturally without the boilerplate that a general-purpose SQL engine requires.

-- Parseable: Errors by service in the last hour
SELECT service_name, level, count(*) as error_count
FROM application_logs
WHERE level = 'error'
  AND p_timestamp > NOW() - INTERVAL '1 hour'
GROUP BY service_name, level
ORDER BY error_count DESC;
 
-- Parseable: Trace latency percentiles by endpoint
SELECT http_route,
       approx_percentile_cont(duration_ms, 0.50) as p50,
       approx_percentile_cont(duration_ms, 0.95) as p95,
       approx_percentile_cont(duration_ms, 0.99) as p99
FROM traces
WHERE p_timestamp > NOW() - INTERVAL '24 hours'
GROUP BY http_route
ORDER BY p99 DESC;

The query experience feels natural because the platform understands what you are querying. Log streams, trace hierarchies, and metric time-series are first-class concepts, not arbitrary tables you happened to create.

Open Format and Deployment Flexibility

Parseable is available as Parseable Cloud — managed observability starting at $0.37/GB ingested ($29/month minimum) — and as a self-hosted deployment with source code on GitHub. The development process is transparent, and you can audit every line of code that handles your observability data.

Your data is stored in Apache Parquet, an open columnar format. This means:

• No format lock-in. You can read your observability data with DuckDB, Spark, Athena, Pandas, or any Parquet-compatible tool.
• Portable analytics. Want to run a complex analysis that goes beyond what Parseable's SQL supports? Point Spark or Trino at your S3 bucket and query the Parquet files directly.
• Future-proof. If you ever decide to switch from Parseable, your data is already in a universally readable format. There is no export process, no conversion step, no data loss.

ClickHouse stores data in its own native format. While ClickHouse provides export capabilities (to CSV, Parquet, JSON, etc.), the on-disk format is ClickHouse-specific. Your data is effectively locked into the ClickHouse ecosystem as long as it lives on ClickHouse's local disks. Migrating away from ClickHouse means exporting all your data, which at multi-TB scale is a significant operation.

Built-In Console and Alerting

Parseable ships with everything you need to operate an observability platform out of the box:

• Web console for log exploration, trace visualization, and metric dashboards
• Alerting engine with webhook, Slack, PagerDuty, and email integrations
• User management with RBAC for team-based access control
• AI-powered query assistance for natural language log exploration
• Live tail for real-time log streaming

ClickHouse provides none of these. It is a database engine. Building an observability workflow on ClickHouse requires assembling additional components: Grafana for dashboards, Alertmanager or a custom service for alerts, a separate authentication layer, and custom tooling for log exploration. Each component adds deployment complexity, maintenance burden, and potential failure points.

Parseable Cloud: Zero-Ops Option

For teams that want the Parseable experience without managing any infrastructure at all, Parseable Cloud provides a fully managed service. Your data still lives in your cloud account on S3 (Bring Your Own Cloud model), but Parseable manages the compute layer, upgrades, scaling, and availability.

This gives you the cost advantages of S3-native storage with zero operational burden, a combination that ClickHouse cannot match without a managed service provider (and managed ClickHouse services like ClickHouse Cloud still carry the cost premium of NVMe-backed storage).

Migration Guide: Moving from ClickHouse to Parseable

If you are currently running ClickHouse for log analytics and want to evaluate Parseable, here is a practical migration path that minimizes risk.

Phase 1: Parallel Ingestion (Week 1)

Deploy Parseable alongside your existing ClickHouse infrastructure. Configure your OpenTelemetry Collector to export to both destinations simultaneously.

# otel-collector-config.yaml
exporters:
  # Existing ClickHouse exporter
  clickhouse:
    endpoint: tcp://clickhouse:9000
    database: otel
    logs_table_name: otel_logs
 
  # New Parseable OTLP exporter
  otlphttp/parseable:
    endpoint: http://parseable:8000/v1
    headers:
      Authorization: "Basic <base64-credentials>"
 
service:
  pipelines:
    logs:
      receivers: [otlp]
      processors: [batch]
      exporters: [clickhouse, otlphttp/parseable]
    traces:
      receivers: [otlp]
      processors: [batch]
      exporters: [clickhouse, otlphttp/parseable]

This gives you identical data in both systems with zero risk to your production observability.

Phase 2: Query Validation (Week 2-3)

Run your most important queries against both systems and compare results. Since both support SQL, many queries will work with minimal modification.

-- ClickHouse
SELECT service_name, count(*) as error_count
FROM otel_logs
WHERE level = 'error'
  AND timestamp >= now() - INTERVAL 1 HOUR
GROUP BY service_name
ORDER BY error_count DESC;
 
-- Parseable (equivalent)
SELECT service_name, count(*) as error_count
FROM otel_logs
WHERE level = 'error'
  AND p_timestamp >= NOW() - INTERVAL '1 hour'
GROUP BY service_name
ORDER BY error_count DESC;

Document any query differences, validate that result sets match, and measure query latency on both systems. Pay special attention to your most complex queries and high-cardinality aggregations.

Phase 3: Dashboard Migration (Week 3-4)

If you are using Grafana with ClickHouse, you can reconfigure Grafana dashboards to use Parseable as a data source. Alternatively, use Parseable's built-in console to build new dashboards that leverage its native visualization capabilities.

Recreate your alerting rules in Parseable's alerting engine. Test that alerts fire correctly with synthetic data or by temporarily lowering thresholds.

Phase 4: Cutover (Week 4+)

Once you have validated queries, dashboards, and alerts on Parseable:

1. Switch your primary observability workflows to Parseable
2. Keep ClickHouse running in read-only mode for historical data access
3. After your retention period expires on ClickHouse (or after verifying all necessary historical data is accessible via Parseable), decommission the ClickHouse cluster
4. Reclaim the NVMe instances and Keeper nodes

The cost savings begin immediately upon cutover. Most teams report a 80-93% reduction in infrastructure costs within the first month.

ClickHouse vs Parseable: When to Choose ClickHouse

Intellectual honesty requires acknowledging scenarios where ClickHouse is the better choice.

You need a general-purpose OLAP database. If your use case extends beyond observability into business analytics, financial modeling, or ad-tech workloads that share infrastructure, ClickHouse's general-purpose nature is an advantage. Parseable is purpose-built for observability and does not attempt to serve general analytical workloads.

You have deep ClickHouse expertise on your team. If your platform team already operates ClickHouse clusters for other workloads and has established runbooks, monitoring, and capacity planning processes, adding observability data to the existing cluster may be operationally simpler than introducing a new system.

You need sub-second query latency on very large aggregations. ClickHouse's local-disk architecture and vectorized query engine can deliver lower query latency than S3-backed storage for certain query patterns, particularly full-table scans over hundreds of GB. Parseable's query latency on S3 is excellent for interactive exploration, but queries that scan months of data may be faster on ClickHouse's NVMe-backed storage.

You are building a custom observability platform. If your team is building a bespoke observability product and wants full control over the storage and query layer, ClickHouse provides a powerful foundation. Parseable is a complete platform, not a building block.

Frequently Asked Questions

Is ClickHouse good for log analytics?

Yes, ClickHouse is excellent at the query and compression aspects of log analytics. Its columnar storage, ZSTD compression, and vectorized execution engine deliver fast analytical queries over log data. However, ClickHouse is a database engine, not an observability platform. Using it for log analytics requires building or adopting additional components: an ingestion pipeline (ClickHouse has no native OTLP endpoint), a visualization layer (Grafana, Metabase, or custom UI), an alerting system, and user management. You also need to operate a ClickHouse cluster with Keeper, manage replication, monitor disk usage, and handle capacity planning. Parseable provides all of these capabilities in a single binary with S3 storage, eliminating the operational overhead.

How does Parseable compare to SigNoz?

SigNoz and Parseable are both open-source observability platforms that support logs, metrics, and traces. The key architectural difference is the storage layer: SigNoz uses ClickHouse (local NVMe storage), while Parseable uses S3 object storage with Parquet format. This means SigNoz inherits ClickHouse's operational complexity (cluster management, Keeper, replication, disk capacity planning), while Parseable's storage layer is fully managed by S3. Parseable typically costs 60-80% less in infrastructure at equivalent data volumes because S3 storage is dramatically cheaper than NVMe. For a detailed comparison, see our guide on Elasticsearch alternatives for log analytics.

Can Parseable handle the same query performance as ClickHouse?

For interactive observability queries (filtering, aggregation, time-range scans over recent data), Parseable delivers comparable performance. Parseable's query engine is optimized for the access patterns common in observability: filtering by time range, service name, log level, and error codes, then aggregating results. For very large historical scans spanning months of data, ClickHouse's local NVMe storage may provide lower latency. However, most observability queries target recent data (last 1-24 hours), where Parseable's performance is excellent and the 90%+ cost savings make it the more practical choice.

What is the total cost of running ClickHouse for logs?

The total cost includes infrastructure (NVMe instances for ClickHouse nodes, instances for Keeper cluster, network traffic), personnel (0.5-1.0 FTE for cluster management), and opportunity cost (engineering time spent on ClickHouse operations instead of product development). For a 100 GB/day deployment with 30-day retention, expect $14,000-$17,000/year in infrastructure plus $75,000-$150,000/year in allocated personnel costs. Parseable's equivalent deployment costs $1,400-$1,500/year in infrastructure with near-zero operational overhead because there is no cluster to manage.

Does Parseable support ClickHouse SQL syntax?

Parseable uses standard SQL, which shares significant overlap with ClickHouse's SQL dialect. Most SELECT queries with WHERE filters, GROUP BY aggregations, ORDER BY, and LIMIT clauses will work with minimal modification. ClickHouse-specific extensions (ARRAY JOIN, materializing columns, custom codecs) do not apply because Parseable's storage model is different. The migration path for queries is straightforward: adjust time column references and remove any ClickHouse-specific syntax. See the migration guide above for examples.

Why is S3 storage cheaper than NVMe for logs?

S3 Standard costs $0.023/GB/month. NVMe on EC2 (i4i instances) costs effectively $0.10-$0.15/GB/month when amortized across the instance cost and usable storage capacity. That is a 4-7x cost difference on raw storage alone. The gap widens further because ClickHouse requires replication (doubling storage) and headroom for merge operations (typically 2x working set). S3 handles replication automatically at no additional cost and requires no headroom allocation. At scale, S3 also offers Intelligent Tiering and Glacier for archival data at $0.004/GB/month, a cost that NVMe cannot approach. For observability data where most queries target recent data and older data is accessed infrequently, S3's tiered storage model is a natural fit.

Getting Started

Deploy Parseable in 5 Minutes

# Docker quickstart with local storage
docker run -p 8000:8000 \
  parseable/parseable:latest \
  parseable local-store
 
# Production deployment with S3 backend
docker run -p 8000:8000 \
  -e P_S3_URL=https://s3.amazonaws.com \
  -e P_S3_ACCESS_KEY=your-access-key \
  -e P_S3_SECRET_KEY=your-secret-key \
  -e P_S3_BUCKET=your-parseable-bucket \
  -e P_S3_REGION=us-east-1 \
  parseable/parseable:latest \
  parseable s3-store

Access the console at http://localhost:8000 and start querying with SQL immediately.

Recommended: Parseable Cloud — skip infrastructure entirely. Free tier available, starts at $0.37/GB ingested. Start ingesting data in minutes with zero ops.

Resources

• Start with Parseable Cloud — starts at $0.37/GB, free tier available
• Self-hosted deployment — single binary, deploy in 2 minutes
• Read the docs — guides, API reference, and tutorials
• Join our Slack — community and engineering support

Related Reading

• Elasticsearch vs Parseable: Choosing the Right Log Analytics Platform
• Top 10 Splunk Alternatives for Log Management
• The Future of S3-Native Log Analytics

ClickHouse vs Parseable: Key Takeaways

1. ClickHouse is an excellent OLAP database, but it is not an observability platform. Using it for log analytics requires building or adopting ingestion pipelines, visualization layers, alerting systems, and operational processes for cluster management.

2. The storage cost gap is massive. S3 at $0.023/GB/month vs NVMe at $0.10-$0.15/GB/month, compounded by ClickHouse's replication and merge headroom requirements, means Parseable costs 85-93% less in infrastructure at equivalent data volumes.

3. Operational complexity is the hidden cost of ClickHouse. A production ClickHouse cluster requires 0.5-1.0 FTE for ongoing management. Parseable's single-binary architecture with S3 storage requires near-zero operational overhead.

4. Purpose-built beats general-purpose for observability. Parseable's native OTLP endpoint, built-in console, AI analysis, and MELT support eliminate the integration work required when using ClickHouse as a log backend.

5. Open formats prevent lock-in. Parseable stores data in Apache Parquet on S3, readable by any tool. ClickHouse uses a proprietary on-disk format that locks your data into the ClickHouse ecosystem.

6. If you choose ClickHouse for logs, you are choosing cluster ops. Whether directly or via SigNoz, the ClickHouse cluster needs care: disk monitoring, Keeper management, merge tuning, replication config, and capacity planning. Parseable eliminates all of this by delegating storage to S3.