ClickHouse ClickStack vs. Parseable: An Honest Comparison

ClickHouse launching Managed ClickStack is one of the most significant signals the observability market has received in years. It validates a thesis that a growing number of engineering teams have already arrived at independently: the traditional SaaS monitoring model, where you pay $2-8 per GB to ship telemetry to a vendor's cloud, is fundamentally broken at scale. The industry needs cheaper, more open alternatives.

ClickStack, born from ClickHouse's acquisition of HyperDX, is their answer. Parseable is ours. Both platforms aim to deliver observability at a fraction of Datadog's cost, but they take architecturally different paths to get there, and those differences matter enormously once you look past the headline pricing.

This comparison is intended to be honest. ClickStack does some things well, and we will say so. Parseable does other things better, and we will explain why. The goal is to give practitioners -- DevOps engineers, SREs, and platform teams -- enough concrete information to make the right decision for their specific situation.

The Market Context: Why Both Platforms Exist

The observability market has a pricing problem. At 100GB/day of log ingestion, Datadog costs roughly $195,000/year. Splunk Cloud runs approximately $200,000/year. New Relic, Elastic Cloud, and Sumo Logic all land in the same ballpark once you factor in retention, compute, and the various feature-gated add-ons.

These numbers have pushed engineering leaders into uncomfortable territory: sampling aggressively, dropping log retention to 7-15 days, or simply leaving parts of their infrastructure unmonitored. When the observability budget competes with the actual infrastructure budget, something has to give.

Both ClickStack and Parseable exist because this situation is untenable. The core insight is the same: modern object storage and columnar formats can reduce the cost of storing and querying telemetry by an order of magnitude. Where the two platforms diverge is in how they implement that insight, and the implications of those choices ripple through pricing, data ownership, operational complexity, and long-term flexibility.

For a deeper breakdown of why observability economics are broken, see The Traditional SaaS Pricing Model for Observability Is Broken.

Architecture: Data Warehouse vs. Data Lake

The most fundamental difference between ClickStack and Parseable is architectural. Understanding this distinction is essential because it drives every downstream difference in pricing, data ownership, and flexibility.

ClickStack: A Managed Data Warehouse for Observability

ClickStack is built on ClickHouse, a columnar OLAP database originally designed for analytical workloads. ClickHouse stores data in its proprietary MergeTree format, which is highly optimized for fast analytical queries, particularly aggregations, GROUP BY operations, and time-range scans.

With Managed ClickStack, your telemetry data lives on ClickHouse Cloud infrastructure. ClickHouse manages the compute nodes, storage, replication, and scaling. You send data in, you query it through ClickStack's UI (inherited from HyperDX) or via ClickHouse SQL, and ClickHouse handles everything in between.

This is a data warehouse model: tightly coupled storage and compute, managed by the vendor, optimized for query performance within that vendor's ecosystem.

Parseable: An Observability Data Lake

Parseable takes a fundamentally different approach. Data is stored in Apache Parquet, an open columnar format, on object storage that can be S3, GCS, Azure Blob, MinIO, or any S3-compatible backend. The query engine, ParseableDB (built on Apache Arrow DataFusion), runs separately from storage. On the Enterprise plan with BYOB (Bring Your Own Bucket), the Parquet files sit in your own cloud account -- you control the bucket, the encryption keys, the IAM policies, and the access patterns.

This is a data lake model: decoupled storage and compute, open formats, and the option for complete data ownership.

Why This Distinction Matters

The warehouse-vs-lake distinction is not academic. It determines:

• Who owns the data: In ClickStack, your data lives on ClickHouse's infrastructure in MergeTree format. In Parseable (Enterprise with BYOB), your data lives in your own S3 bucket in standard Parquet files.
• What you can do with the data: MergeTree files are queryable only through ClickHouse. Parquet files are readable by DuckDB, Spark, Athena, Presto, Snowflake, and dozens of other tools.
• How costs scale: Warehouse models couple compute and storage costs. Lake models let storage scale on commodity object storage pricing while compute scales independently.
• How hard it is to leave: Migrating from a ClickHouse-proprietary format requires a full data export and transformation. Migrating from Parquet requires pointing a new query engine at existing files.

For a deeper exploration of this trade-off, see Data Lake vs. Data Warehouse for Observability.

Pricing: The $0.03/GB Question

ClickStack's headline pricing is attention-grabbing: $0.03/GB for storage. That number looks extraordinary next to Datadog's effective $2-8/GB. But the headline is only part of the story, and the rest of the story changes the math significantly.

ClickStack Pricing Breakdown

ClickStack separates pricing into two components:

• Storage: $0.03/GB
• Compute: $0.22-$0.39 per compute-unit per hour

The storage number is what gets quoted in marketing. The compute number is what dominates your actual bill. Every query you run, every dashboard that refreshes, every alert that evaluates -- all consume compute units. And at $0.22-$0.39 per compute-unit per hour, those costs accumulate quickly for any production workload with active dashboards, multiple users running queries, and automated alerting.

This is a pattern the industry has seen before. Cloud data warehouses like Snowflake and BigQuery use similar decoupled pricing, and the lesson from those platforms is clear: storage is cheap, compute is where the bill lives. A team that ingests 200GB/day and runs moderate query workloads can easily spend more on compute than on storage by a factor of 5-10x.

The exact total depends heavily on your query patterns, dashboard refresh intervals, alert frequency, and concurrency. ClickStack does not publish a simple all-in per-GB cost because the compute component is usage-dependent. This makes cost prediction harder, which is precisely the problem many teams are trying to escape when they leave Datadog.

For more on how observability pricing works in practice, see The True Cost of Observability: Why $0.03/GB Is Never $0.03/GB.

Parseable Pricing

Parseable's pricing model is designed for predictability:

• Pro plan: $0.39/GB ingested with a 14-day free trial
• Included with Pro: 365 days retention, 99.9% uptime SLA, AI-native analysis, anomaly detection, unlimited users, dashboards, alerts, and API access
• Query scanning: Up to 10x of monthly ingestion volume included; additional scans at $0.02/GB
• Enterprise plan: Custom pricing starting at $15,000/year, adding BYOB, Apache Iceberg support, premium support, and flexible deployment options. Enterprise per-GB rates: $0.25/GB for BYOC, $0.20/GB for self-hosted

The key difference is that Parseable's per-GB price includes compute. There is no separate compute charge that fluctuates based on query activity. Your bill is a function of how much data you ingest, not how actively you query it.

Pricing Worked Examples

Let's run the numbers at three scale points. For ClickStack, we will estimate total cost conservatively, acknowledging that actual compute costs depend on usage patterns. For Parseable, we use the published $0.39/GB Pro plan pricing.

Scenario 1: 50 GB/day (Small-to-Mid Team)

ClickStack (estimated):

• Monthly ingestion: 50 GB x 30 = 1,500 GB
• Storage: 1,500 GB x $0.03 = $45/month
• Compute: Varies. Even modest query workloads (a few dashboards, basic alerting, occasional ad-hoc queries) could run $200-$600/month depending on compute-unit consumption
• Estimated monthly total: $245-$645
• Estimated annual total: $2,940-$7,740

Parseable Pro:

• Monthly ingestion: 1,500 GB x $0.39 = $585/month
• Query scanning: 15,000 GB included (10x ingestion). Most teams stay well within this.
• Monthly total: $585
• Annual total: $7,020

At this scale, ClickStack could be cheaper if your compute usage stays low. Parseable's advantage is predictability: $585/month, no surprises.

Scenario 2: 200 GB/day (Mid-Size Engineering Org)

ClickStack (estimated):

• Monthly ingestion: 200 GB x 30 = 6,000 GB
• Storage: 6,000 GB x $0.03 = $180/month
• Compute: With multiple dashboards, cross-team query workloads, automated alerts, and regular investigations, compute costs grow significantly. Estimated $800-$3,000/month.
• Estimated monthly total: $980-$3,180
• Estimated annual total: $11,760-$38,160

Parseable Pro:

• Monthly ingestion: 6,000 GB x $0.39 = $2,340/month
• Query scanning: 60,000 GB included
• Monthly total: $2,340
• Annual total: $28,080

At 200GB/day, both platforms land in a similar range, but Parseable's number is fixed while ClickStack's depends on compute usage. For teams with heavy query workloads, ClickStack's total cost could exceed Parseable's. For teams that mostly write and rarely query, ClickStack could be cheaper.

Scenario 3: 1 TB/day (Large-Scale Platform)

ClickStack (estimated):

• Monthly ingestion: 1,000 GB x 30 = 30,000 GB
• Storage: 30,000 GB x $0.03 = $900/month
• Compute: At this scale, with dozens of engineers querying, continuous dashboard refreshes, and extensive alerting pipelines, compute dominates. Estimated $5,000-$15,000+/month.
• Estimated monthly total: $5,900-$15,900
• Estimated annual total: $70,800-$190,800

Parseable Pro:

• Monthly ingestion: 30,000 GB x $0.39 = $11,700/month
• Query scanning: 300,000 GB included
• Monthly total: $11,700
• Annual total: $140,400

At 1TB/day, Parseable's all-inclusive pricing becomes more attractive for teams with active query workloads. For Enterprise deployments, contact Parseable for volume pricing that can bring the per-GB cost down further.

Parseable Enterprise (BYOB alternative):

• Enterprise pricing starts at $15,000/year and is custom-quoted. For teams at 1TB/day, the Enterprise plan with BYOB gives you full control over your storage bucket, Apache Iceberg support for interoperability with your broader data stack, and volume-based pricing.

The Cost Comparison Summary

Both ClickStack and Parseable represent dramatic savings over Datadog. The choice between them depends on whether you value cost predictability (Parseable) or potentially lower costs with usage-dependent compute (ClickStack).

Data Ownership and Format: Open vs. Proprietary

This is where the architectural difference has the most practical impact over time.

ClickStack: Data on Vendor Infrastructure

When you use Managed ClickStack, your telemetry data is stored in ClickHouse Cloud. The data is in ClickHouse's proprietary MergeTree format. You can query it through ClickStack's UI or ClickHouse SQL, but:

• You cannot directly access the underlying files. The data lives on ClickHouse's infrastructure, not in a bucket you control.
• The format is proprietary. MergeTree is not readable by external tools. You cannot point DuckDB, Spark, or Athena at your observability data for ad-hoc analysis.
• Migration requires a full export. If you decide to move to a different platform, you need to export your data through ClickHouse's query interface, transform it, and reload it elsewhere.

This is the same vendor-data-custody model that Datadog, Splunk, and other SaaS observability platforms use. ClickStack offers it at a lower price point, but the structural relationship between you and your data is similar.

Parseable: Open Formats, Your Storage

Parseable stores all telemetry in Apache Parquet on object storage. On the Pro plan, this runs on Parseable Cloud's managed infrastructure. On the Enterprise plan with BYOB, the Parquet files sit in your own S3, GCS, or Azure Blob bucket.

This means:

• Your data is in an open format. Parquet is readable by every major data tool: DuckDB, Spark, Athena, Presto, BigQuery, Snowflake, pandas, Polars -- the list is extensive.
• With BYOB, you control the storage. Your IAM policies, your encryption keys, your compliance posture. Parseable accesses the bucket to read and write, but you own it.
• Migration is trivial. To move away from Parseable, point a different query engine at the same Parquet files. No export, no transformation, no data loss.

For organizations where data sovereignty, compliance, or long-term flexibility are concerns, this is a material difference. For more on why this matters, see Bring Your Own Bucket: Data Ownership in Observability and Why Your Observability Data Should Live in Apache Parquet.

Query Performance: Being Honest

ClickHouse is fast. This is not marketing spin -- it is a well-earned reputation built on years of engineering focused on analytical query performance. ClickHouse's MergeTree engine, with its sparse indexing, data skipping, and vectorized execution, is genuinely one of the fastest OLAP engines available for point queries, aggregations, and time-range scans.

For specific query patterns -- aggregating metrics over large time windows, computing percentiles across billions of rows, running GROUP BY over high-cardinality fields -- ClickHouse's performance is excellent. ClickStack inherits this advantage directly.

Parseable's query engine, ParseableDB, is built on Apache Arrow DataFusion. DataFusion is a rapidly maturing query engine with strong performance characteristics, particularly for columnar data on object storage. It handles full-text log search, time-range filtering, and analytical aggregations well. For the kinds of queries that dominate observability workflows -- searching for error patterns, filtering by service and time range, aggregating request counts and latencies -- ParseableDB delivers fast, interactive results.

Where ClickHouse has an edge is in raw query throughput for highly complex analytical queries over very large datasets. If your workflow involves running ClickHouse-style OLAP queries with multiple joins, window functions, and sub-queries across terabytes of data, ClickHouse's engine has more years of optimization for exactly that workload.

Where Parseable has an edge is in the broader accessibility of the data. Because data is in Parquet, you are not limited to a single query engine. You can run interactive queries through ParseableDB, batch analytics through Spark, ad-hoc exploration through DuckDB, and ML pipelines that read directly from the same files. This multi-engine flexibility is architecturally impossible with ClickStack's proprietary format.

The honest summary: for pure OLAP query speed on a single engine, ClickHouse has a slight edge for complex analytical workloads. For practical observability query patterns (log search, trace correlation, metric aggregation) and for multi-engine flexibility, Parseable's approach is more versatile.

Feature Comparison

Session Replay

ClickStack inherits HyperDX's session replay capability, which lets you replay a user's browser session alongside the backend traces and logs associated with that session. This is a genuinely useful feature for debugging user-facing issues, and it is something Parseable does not offer today.

If frontend session replay is a core part of your debugging workflow, ClickStack has a real advantage here.

AI-Native Analysis

Parseable Pro includes AI-native analysis and anomaly detection as part of the base plan. These features use machine learning to identify unusual patterns in your telemetry data, surface anomalies without manual threshold configuration, and assist with root cause analysis. ClickStack does not currently offer comparable AI-native capabilities out of the box.

OpenTelemetry Support

Both platforms support OpenTelemetry. Parseable provides native OTLP ingestion over HTTP and gRPC, accepting logs, metrics, and traces through the standard OpenTelemetry Collector. ClickStack also supports OpenTelemetry data ingestion.

For teams that have standardized on OpenTelemetry (which, in 2026, is most teams), both platforms are well-aligned with the ecosystem.

Deployment Flexibility

ClickStack is available as a managed service on ClickHouse Cloud. There is no self-hosted option for the full ClickStack product, though the underlying ClickHouse database is open source and can be self-hosted separately.

Parseable offers three deployment models:

• Parseable Cloud (Pro plan): Fully managed, multi-tenant SaaS
• BYOC (Bring Your Own Cloud): Parseable manages the software in your cloud account (Enterprise)
• Self-hosted: Single binary deployment on your own infrastructure, open source under AGPL-3.0

For organizations that need to run observability within their own infrastructure -- air-gapped environments, strict data residency requirements, or simply the desire for full control -- Parseable's self-hosted option is a significant differentiator.

Web UI

Parseable's web UI is called Prism. It provides log exploration, dashboards, alerting, and trace visualization. ClickStack's UI is inherited from HyperDX and provides similar capabilities with the addition of session replay.

Both UIs are functional for day-to-day observability workflows. ClickStack's UI benefits from HyperDX's focus on developer experience, particularly the natural language search capability. Prism focuses on SQL-first workflows with a clean, purpose-built interface for log exploration and correlation.

Deployment and Operational Complexity

ClickStack

As a managed service, ClickStack requires no infrastructure management from the user. You sign up, configure data ingestion, and start querying. ClickHouse handles scaling, replication, upgrades, and availability. This is a genuine advantage for teams that do not want to manage observability infrastructure.

The trade-off is that you accept ClickHouse as your infrastructure provider for this data. Scaling decisions, availability, and performance are in their hands.

Parseable

On Parseable Cloud (Pro plan), the operational model is similarly hands-off -- it is a fully managed service. For self-hosted deployments, Parseable's single-binary architecture means operational complexity is minimal. There is no ClickHouse cluster to shard and replicate, no Kafka to keep alive, no ZooKeeper ensemble to manage. The only external dependency is an S3-compatible object store.

A production-grade self-hosted Parseable deployment can be running in under five minutes. For teams that want managed simplicity, Parseable Cloud delivers it. For teams that want full control with minimal operational burden, self-hosted Parseable is hard to beat.

Read more about this architectural approach in Zero Stack Observability.

Summary Comparison Table

When to Choose ClickStack

Be honest about when ClickStack is the better fit:

• You need session replay. ClickStack's HyperDX heritage gives it frontend session replay that Parseable does not offer. If correlating browser sessions with backend traces is central to your debugging workflow, this matters.
• You are already on ClickHouse Cloud. If your organization has existing ClickHouse workloads and expertise, adding ClickStack to your existing ClickHouse account is a natural extension. The query language, operational model, and billing are already familiar.
• Your query workloads are light relative to ingestion. If you ingest large volumes but query infrequently (for example, a compliance logging use case with rare investigative queries), ClickStack's low storage cost with minimal compute usage could result in lower total cost.
• Raw OLAP query speed is your top priority. If you regularly run complex analytical queries with multiple joins and window functions across very large datasets, ClickHouse's mature OLAP engine has an edge.

When to Choose Parseable

• You want predictable pricing. Parseable's all-inclusive per-GB model means your bill is a direct function of ingestion volume. No compute surcharges, no variable costs based on query activity. At $0.39/GB ingested on the Pro plan, you can forecast your monthly spend with confidence.
• Data ownership matters. If your organization requires that telemetry data stays in your own cloud account, Parseable's Enterprise plan with BYOB gives you full bucket-level control. Your Parquet files, your encryption, your compliance posture. ClickStack does not offer this.
• You want open formats for long-term flexibility. Apache Parquet files are readable by the entire data ecosystem. You are not locked into a single query engine or vendor. If you want to run Spark jobs, DuckDB queries, or ML pipelines over your observability data, Parseable makes that possible. See Why Your Observability Data Should Live in Apache Parquet.
• You need deployment flexibility. Parseable offers managed cloud, BYOC, and fully self-hosted options. If you need to run observability in an air-gapped environment, within a specific geographic region, or on your own Kubernetes cluster, Parseable supports all of these. ClickStack is managed-cloud only.
• You value open source. Parseable is open source under AGPL-3.0, with the full codebase on GitHub. You can audit the code, contribute features, and self-host without licensing fees. ClickStack's observability layer is not open source.
• AI-native observability is important. Parseable Pro includes AI-native analysis and anomaly detection out of the box, helping teams identify issues faster without manual threshold configuration.
• You want long retention without cost surprises. Parseable Pro includes 365 days of retention in the base price. With ClickStack, longer retention means more stored data, which is cheap at $0.03/GB, but the compute cost of querying older data adds up.

The Bigger Picture: What ClickStack Validates

It is worth stepping back from the feature comparison to acknowledge what ClickStack's existence means for the market. When ClickHouse -- a company valued at billions, backed by major venture capital -- launches a managed observability product designed to undercut Datadog on price, it validates the core thesis that the observability data lake community has been advocating for years:

1. Observability data belongs on cheap storage, not in expensive SaaS vaults. Both ClickStack and Parseable agree on this.
2. Columnar formats beat row-oriented formats for telemetry. Both use columnar engines (MergeTree and Parquet/DataFusion respectively).
3. OpenTelemetry is the standard. Both support OTel-native ingestion.
4. The market wants alternatives to Datadog pricing. Both exist because $195K/year for 100GB/day is not sustainable.

Where ClickStack and Parseable diverge is on the question of openness. ClickStack uses a proprietary format, stores data on vendor infrastructure, and operates as a managed-only service. Parseable uses an open format, supports customer-owned storage, and offers self-hosted deployment. Both models have valid use cases, but they represent different philosophies about the relationship between an observability platform and the data it manages.

For teams evaluating their next observability platform, the question is not just "which is cheaper?" but "what relationship do I want with my observability data?" If you want a fast, managed service and are comfortable with vendor custody of your data, ClickStack is a strong option. If you want to own your data in open formats with the flexibility to self-host, switch tools, or run multi-engine analytics, Parseable is architecturally designed for that.

Getting Started

Both platforms offer fast onboarding paths.

Try Parseable Cloud free for 14 days: Sign up at app.parseable.com. The Pro plan is $0.39/GB ingested with 365 days retention, AI-native analysis, anomaly detection, unlimited users, dashboards, and alerts included.

Self-host Parseable in 5 minutes:

docker run -p 8000:8000 \
  parseable/parseable:latest \
  parseable local-store

Configure your OpenTelemetry Collector to export to Parseable's OTLP endpoint, and you are up and running with SQL-queryable observability on open formats.

For Enterprise needs -- BYOB, Apache Iceberg support, BYOC, or custom deployment requirements -- visit parseable.com to talk to the team.

Frequently Asked Questions

Is ClickStack cheaper than Parseable?

It depends on your usage pattern. ClickStack's storage cost ($0.03/GB) is lower than Parseable's all-inclusive rate ($0.39/GB ingested on Pro), but ClickStack charges separately for compute at $0.22-$0.39 per compute-unit per hour. For teams with light query workloads, ClickStack may be cheaper. For teams with active dashboards, regular alerting, and frequent ad-hoc queries, the compute costs can push ClickStack's total above Parseable's predictable per-GB rate. Both are dramatically cheaper than Datadog or Splunk at any scale.

Can I self-host ClickStack?

No. Managed ClickStack is available only on ClickHouse Cloud. The underlying ClickHouse database engine is open source and can be self-hosted, but the ClickStack observability UI and features require the managed service. Parseable is fully open source under AGPL-3.0 and can be self-hosted as a single binary, deployed on Parseable Cloud, or run via BYOC.

Does ClickStack support Bring Your Own Bucket (BYOB)?

No. ClickStack stores data on ClickHouse Cloud infrastructure. You do not control the underlying storage bucket. Parseable's Enterprise plan includes BYOB, where your telemetry data is stored in your own S3, GCS, or Azure Blob account in open Apache Parquet format.

What data format does ClickStack use?

ClickStack stores data in ClickHouse's proprietary MergeTree format. This format is optimized for fast analytical queries within ClickHouse but is not readable by external tools. Parseable stores data in Apache Parquet, an open columnar format readable by DuckDB, Spark, Athena, Presto, Snowflake, pandas, and dozens of other tools.

Is ClickStack the same as HyperDX?

ClickStack evolved from HyperDX, which ClickHouse acquired. It retains HyperDX's UI and session replay capabilities, now running on ClickHouse Cloud as the managed backend. The HyperDX open-source project still exists, but Managed ClickStack is the commercial product going forward.

Which platform has better query performance?

ClickHouse has a slight edge for complex OLAP queries (multi-join aggregations, window functions over very large datasets) due to years of optimization for exactly that workload. For typical observability queries -- log search, time-range filtering, error pattern analysis, trace correlation -- both platforms deliver fast, interactive results. Parseable's advantage is multi-engine flexibility: because data is in Parquet, you can query with external tools like DuckDB or Spark in addition to ParseableDB.

How does data portability compare?

Parseable stores data in Apache Parquet, an open format with universal tool support. On the Enterprise plan with BYOB, you have direct file access to your telemetry data. ClickStack stores data in ClickHouse's proprietary MergeTree format on ClickHouse Cloud -- accessing or migrating data requires querying through ClickHouse's interface and exporting. For teams that prioritize avoiding vendor lock-in, Parseable's open format approach provides significantly better portability.