Can’t I just run ClickHouse myself for less?

On paper the software is free; in practice the bill is engineering time: NVMe and query-node sizing, schemas, Kafka buffering, tiering, upgrades, and the on-call. Cloud and Managed ClickStack remove some of it; self-managed shifts it onto your team.

What about HyperDX / ClickStack?

HyperDX is an observability UX over ClickHouse, now the basis of ClickStack. It doesn’t change the operating model for self-hosted deployments; you still run ClickHouse underneath. Managed ClickStack narrows the gap.

Is APL better than SQL for logs?

Not better, better-suited for sequential, log-style analysis, which is most observability work. For arbitrary analytics, ClickHouse SQL is a genuine strength.

Can my AI agents query the data?

Yes, a native MCP lets agents query every byte in APL, managed for you, so you’re not also operating the database, server, and schema the agent depends on.

What does ClickHouse do better?

Maximum control, raw SQL and OLAP power for arbitrary analytics, an open-source core with no lock-in, and full control over data location via self-managed or BYOC.

THE MODERN MACHINE DATA PLATFORM

Axiom vs ClickHouse

ClickHouse is one of the fastest analytical databases ever built, and a superb foundation if you want to build your own observability platform. Axiom is that platform, already built: a fully managed event store, purpose-built for machine and event data, with APL and usage-based pricing. The real question isn’t the engine, it’s whether you want to operate one.

Start free Try APL in the Playground

1['payment-service']

2| where ['event.type'] == "charge.failed"

3| summarize count() by ['decline.reason']

Dataset library

Search datasets

All

Search api-gateway-prod

Quick queries

Fields

SPRIORITY

SSYSLOG_FACILITY

SSYSLOG_IDENTIFIER

S_BOOT_ID

S_CAP_EFFECTIVE

S_CMDLINE

S_COMM

S_EXE

S_GID

S_MACHINE_ID

S_PID

S_SYSTEMD_UNIT

S_TRANSPORT

Search recent queries

Today, 02:553m ago

1['api-gateway-prod']

2| where status >= 500

3| summarize count() by bin_auto(_time)

Today, 02:4513m ago

1['api-gateway-prod']

2| summarize p95 = percentile(req_duration_ms, 95) by route

3| sort by p95 desc

Today, 02:3523m ago

1['payment-service']

2| where ['event.type'] == "charge.failed"

3| summarize count() by ['decline.reason']

Today, 02:2533m ago

1['frontend-web-vitals']

2| summarize users = dcount(session_id) by ['geo.country']

3| sort by users desc

Today, 02:1444m ago

1['nginx-access-logs']

2| where status == 429

3| summarize hits = count() by client_ip

axiom-agent — incident-2317

> Why did checkout p95 spike at 14:21 UTC?

axiom-mcp connected • incident-2317 • zsh 5.9

OVERVIEW

What’s the difference between Axiom and ClickHouse?

ClickHouse is a database engine; Axiom is a complete, fully managed, modern machine data platform. You can build observability on ClickHouse, often with ClickStack (the open-source logs, metrics, and traces stack built on HyperDX), but you become the platform team. Axiom delivers the same keep-everything, query-it-fast outcome as a managed service, with APL designed for log-style pipelines instead of raw SQL.

FEATURE-BY-FEATURE COMPARISON

Axiom vs ClickHouse at a glance

Dimension

Axiom

ClickHouse

What it is

Fully managed, modern machine data platform (logs, events, traces, metrics)

OLAP database engine; observability via ClickStack/HyperDX or DIY

Operating model

Zero cluster ops, Axiom runs everything

Self-managed OSS, ClickHouse Cloud, BYOC, or Managed ClickStack

Query experience

APL: piped, sequential, log-native

SQL (powerful; less ergonomic for log pipelines); HyperDX adds a UX

Ingestion pipeline

Schema-less, OTel-native, Events API + SDKs, managed

You design ingestion (Kafka/Vector/OTel), schema, materialized views

Storage / retention

Managed, 95% compression, configurable, $0.030/GB

You manage tiering (NVMe to object storage), TTLs, part merges

Pricing

Usage-based: load + query + storage, $25/mo platform fee

Cloud: usage-based compute/storage, scale-to-zero; self-managed = infra + engineers’ time

AI-agent access

Native MCP; every byte queryable in APL, SRE skill

ClickHouse ships an MCP; you wire it into your deployment

Best fit

Want the outcome without running the platform

Want to build/own the platform, or need raw SQL/OLAP control

What it is

Axiom

Fully managed, modern machine data platform (logs, events, traces, metrics)

ClickHouse

OLAP database engine; observability via ClickStack/HyperDX or DIY

Operating model

Axiom

Zero cluster ops, Axiom runs everything

ClickHouse

Self-managed OSS, ClickHouse Cloud, BYOC, or Managed ClickStack

Query experience

Axiom

APL: piped, sequential, log-native

ClickHouse

SQL (powerful; less ergonomic for log pipelines); HyperDX adds a UX

Ingestion pipeline

Axiom

Schema-less, OTel-native, Events API + SDKs, managed

ClickHouse

You design ingestion (Kafka/Vector/OTel), schema, materialized views

Storage / retention

Axiom

Managed, 95% compression, configurable, $0.030/GB

ClickHouse

You manage tiering (NVMe to object storage), TTLs, part merges

Pricing

Axiom

Usage-based: load + query + storage, $25/mo platform fee

ClickHouse

Cloud: usage-based compute/storage, scale-to-zero; self-managed = infra + engineers’ time

AI-agent access

Axiom

Native MCP; every byte queryable in APL, SRE skill

ClickHouse

ClickHouse ships an MCP; you wire it into your deployment

Best fit

Axiom

Want the outcome without running the platform

ClickHouse

Want to build/own the platform, or need raw SQL/OLAP control

PRICING STUDIO

Compare your workload across providers

Plug in your current ClickHouse workload and see the cost side by side with Axiom, no sales call. All Axiom data is billed the same way: GB ingested, query compute, and storage.

Open Pricing Studio

Live Monthly Estimate

Ingest / month

2 TB/mo

100 GB1 TB100 TB500 TB5 PB

Query (GB-hrs/mo)

Query settings only affect the Estimate tab — the comparison prices every provider on the same standard workload.

Enterprise add-ons

Comparing providers…

IS AXIOM RIGHT FOR YOU?

Which one is right for you?

Choose Axiom if…

Want keep-everything, query-fast without operating a cluster
Rather spend engineering time on product than storage tiering
Want a log-native query language + managed OTel ingest
Want predictable usage-based pricing

Choose ClickHouse if…

Want to build and own your observability platform
Need raw SQL / OLAP control for broader analytics
Open-source with no lock-in is a hard requirement
Have the platform team to run it well

MIGRATION

From a ClickHouse cluster to a managed event store

Keep your OTel/Vector pipeline; point it at Axiom and query in APL, no schema design, no part-merge tuning, no query-node capacity planning. Move workload by workload and hand back the on-call.

Get the ClickHouse to Axiom guide

1['production-logs']

2| where ['status'] in ("400", "500")

3| extend region = tostring(attributes['aws.region'])

4| summarize errors = count(),p95 = percentile(latency,95) by region

5| order by errors desc

6| take 5

Or use⌘Kto generate query

Region

Errors

p95 (ms)

us-east-1

12,318

1,842

eu-west-1

9,247

612

ap-southeast-2

2,698

388

us-west-2

1,852

287

ap-northeast-1

1,505

sa-mumbai-1

789

143

—

14.2B Events

KEY QUESTIONS, ANSWERED

Common questions

OPERATING AND COST

QUERYING AND CAPABILITIES

MORE COMPARISONS

See how Axiom compares across the ecosystem

See how Axiom compares to leading observability platforms across performance, pricing, scalability, and ease of operation.

Splunk

Same query power, modern infrastructure, no ingest-and-license renewals.

Datadog

Logs + metrics on one usage-based dial. No Standard Indexing SKU.

Elastic

Columnar event store built for observability volumes. No cluster to operate.

See for yourself

Try APL on live data in the Playground, no account required.

Start free Try APL in the Playground