The modern machine data platform.
The modern
machine data platform.Petabyte-scale, schema-less ingest on a fully managed event store, so you keep every byte without the operational cost of running it yourself.
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1`['payment-service']`
2`| where ['event.type'] == "charge.failed"`
3`| summarize count() by ['decline.reason']`
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api-gateway-prodauth-servicebilling-eventscheckout-servicecloudflare-logsfrontend-web-vitalsk8s-eventskafka-consumer-laglambda-invocationsnginx-access-logsotel-spanspayment-servicepostgres-slow-queriesredis-metricsstripe-webhooksvercel-edge-logs
Search api-gateway-prod
Quick queries
Recent eventsEvents over time
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
Recent queries
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
“The beautiful thing about Axiom is it just works. No complications, no rubbish. It's way quicker to find that one thing you want to do.”
Bailey PumfleetCo-founder & CEO of Cal.com
Powering machine data at scale for
[](https://asana.com/)
[](https://bedrockrobotics.com/)
[](https://www.carbon3d.com/)
[](https://www.convex.dev/)
[](https://www.eventim.com/)
[](https://factory.ai/)
[](https://www.framer.com/)
[](https://lumalabs.ai/)
Your old stack is broken by design
What worked at terabytes doesn't work at petabytes.
Costs spiral with volume. Logs get sampled to stay under budget. Older data freezes into rehydration tickets. Every new source means pipeline work. The team managing the stack keeps growing alongside the data.
Data
Visibility
datavisibility
Visibility shrinks as you grow.
The more data your systems produce, the more you sample, drop, or aggregate. The system gets less useful exactly as your workloads get more complex and your incidents get harder to diagnose.
InvoiceQ3 Observability
$0$0
Storage$12,480
Compute$28,650
Retention$9,340
Indexing$14,520
Transfer (egress)$20,734
Cost models break at scale.
Bundled SKUs, indexing tiers that gate visibility, annual renegotiation theater. Finance can't forecast next quarter. Engineers self-censor instrumentation to keep the bill survivable.
The operational surface keeps growing.
Self-managed clusters mean schema migrations, capacity planning, and someone on call for the observability stack itself. The team you hired to ship product spends its time keeping the pipeline alive.
Keep every byte
No data left behind.
Petabyte-scale schema-less ingest on a fully managed event store with 95%+ compression. Keep every log, every dimension, for as long as you want, without standing up a self-managed cluster to do it.
Stream-oriented, schema-on-read architecture. Virtual fields for query-time transformation. No indexes to manage, no ingest-time schema decisions to regret six months later.
Datasets/production-logs
14.2B Events · 1.8 TBOpen in StreamOpen in Query
Name
Events
Data
Fields
Created at
Last ingest
otel-collectorCollects OpenTelemetry logs, metrics, and traces from distributed services for performance monitoring.
55.1M
9.3GB
28/4096
23 Apr 2024
Today
vector.devCaptures telemetry processed by Vector, including logs, transformations, routing metadata, and more.
193.6M
194.6GB
13/4096
23 Apr 2024
Today
fluent-bitIngests logs and telemetry from infrastructure, containers, and applications for centralized observability.
78.3M
103.9GB
8/4096
23 Apr 2024
Today
kinesis-firehoseStreams and delivers high-volume telemetry, logs, and events from AWS services into a unified pipeline.
61.9M
31.7GB
42/4096
23 Apr 2024
Today
github-pull-request-eventCaptures logging events related to GitHub pull requests, including creation, updates, reviews, and merges.
332.9M
1.31TB
8/4096
23 Apr 2024
12 Jun 2024
lambda → events APIRecords requests, responses, latency, and execution details for serverless event processing.
241.7M
1.24TB
4096/4096
23 Apr 2024
Today
github-push-eventCaptures push event logs, detailing repository changes, commits, timestamps, and contributor activity.
2.3M
1.24GB
8/4096
23 Apr 2024
Today
OpenTelemetry/otel-demo-tracesSHAREDContains distributed tracing logs for OpenTelemetry demo applications, useful for observability testing.
N/A
N/A
N/A
23 Apr 2024
Today
Vercel/web-analyticsSHAREDAggregates page-view, web-vital, and routing analytics streamed from Vercel edge deployments.
N/A
N/A
N/A
23 Apr 2024
Today
Fields/otel-collectorView trace
Sservice.name
checkout-api
Sspan.name
POST /cart
Sspan.kind
server
Strace.id
7f3c9a1b4e2d8c05
Sspan.id
a91e2d8c05f4
Nduration.ms
142
Nstatus.code
200
Sk8s.namespace
production
Sk8s.pod
checkout-7d9f4b
Sresource.host
ip-10-0-1-23
Stelemetry.sdk
opentelemetry-js
Sdeployment.env
prod
PRICING THAT SCALES
Predict every bill.
Pay only for what you use. Automatic volume discounts in-console. Permanent free tier. Self-serve enterprise add-ons. Per-unit rates drop at higher tiers, so total cost grows sub-linearly with usage.
One usage-based dial across logs, traces, metrics, and events. No SKU stair-steps. No overage tier. No 30-SKU pricing matrix to decode.
INGEST / MONTH
1.0 TB/mo
100 GB1 TB100 TB500 TB1 PB5 PBINGEST / MONTH
Estimated Pricing
$25$25
↓ 95% vs cheapest alternative
What you'd pay elsewhere · same workload · 12 mo retention
Grafana CloudGrafana Cloud Pro
20.0x More Expensive
Self-HostedGCP/AWS · ELK + S3 archive
20.0x More Expensive
DatadogLogs + Metrics + APM (12 SKUs)
111.4x More Expensive
SplunkObservability Cloud
137.2x More Expensive
“Great product, I see you as the clear market leaders. Not old and bloated like some others. Just enough power while remaining simple.”
— Claras.ai
Queries built for engineers
Modern backend. Classic UX.
APL (piped, sequential, log-friendly) on a purpose-built event store. MPL (same style, built for metrics) on a purpose-built metrics engine. The query experience power users loved about Splunk, on infrastructure modern systems can actually afford to feed. Fully managed.
Sequential processing built into both languages. AI agents use the same APL and MPL primitives because the languages were designed for it. Schema-less ingestion plus virtual fields preserve data-model flexibility without giving up query power.
BuilderEditor
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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
57
sa-mumbai-1
789
143
—
14.2B Events
Land beside Splunk
No rip-and-replace. No renegotiation.
Full Splunk migrations are 18-month projects nobody approves. Axiom shows up beside Splunk via the Splunk App on orphan workloads first (dev, staging, marketing event data), without procurement renegotiating the existing contract. Engineering proves the model, then you expand at the renewal seam.
4Data, our EMEA services partner, supports migrations end-to-end where you want it.
DocumentationExploreDebugSearchSettings
AXIOM FOR SPLUNK
Connect your Axiom organization
Run APL queries against Axiom from inside Splunk and return the results to a Splunk search. Enter an API token — we’ll verify it can list your datasets and run a query before finishing.
Axiom API token
◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦
ConnectedConnectConnected
Store the token in Splunk’s credential store(encrypting…)
List your datasets(org permission needed)
Run a test query(dataset permission needed)
Create an API Token
Create an Axiom API token with query access to the datasets you want to search from splunk:
1. In Axiom, open Settings > API Tokens and click New API token. 2. Give it a name (for example “splunk-app”) and choose an expiry. 3. Under Token permissions > Advance (custom), grant “query:read” on the Axiom dashboard
New Search
| axquery apl="\['shopshock-v3-traces'\] | summarize p50=percentile(duration,50), p95=percentile(duration,95), p99=percentile(duration,99) by \['service.name'\] | order by p95 desc"
| spath
| eval p50\_ms=round(p50/1000000,1), p95\_ms=round(p95/1000000,1), p99\_ms=round(p99/1000000,1)
| table service.name, p50\_ms, p95\_ms, p99\_ms
10 events(6/25/26 4:01 PM to 7/2/26 4:01 PM)
EventsPatternsStatistics (10)Visualization
service.name
p50\_ms
p95\_ms
p99\_ms
api-gateway
0.6
532.2
13689.5
payments-service
305.6
482.2
501.8
orders-service
282.3
482.2
501.8
user-service
0.7
0.8
1.0
inventory-service
0.7
0.8
0.9
notification-service
0.6
0.7
0.8
analytics-service
0.1
0.2
0.2
search-service
0.1
0.1
0.2
product-catalog-service
0.1
0.1
0.1
recommendation-service
0.1
0.1
0.1
AI-queryable by default
Built for engineers and their agents.
AI agents are showing up in the on-call rotation, the eval pipeline, the analytics workflow. They need to query observability data the way engineers do, without custom glue code, without per-vendor adapters, without re-implementing the query layer for every tool.
Native MCP server. SRE skill teaches agents the patterns that move from hypothesis to proof. Metrics skill exposes high- cardinality metrics to agent reasoning in MPL. Every byte queryable in APL and MPL, the same primitives your engineers use.
Claude Code v2.1.180
Opus 4.8 (1M context) with xHigh effort • Claude Max
\~/desktop/axiom.co
\> Why did checkout p95 spike at 14:21 UTC?
▶▶ Bypass permissions on • PR #67 • Axiom MCP Connected
Playground
Run a query. No account required.
No account required. Public dataset. See what schema-less plus pipes feels like.
BuilderAPL
RunCancelClear
1`['http-logs']`
2`| where ['status'] in ("400", "500")`
3`| where ['geo.country'] == "United States"`
Show all 400s and 500s within the United States for http logs
Accept⌘↵Reject⌘⌫
Results
60k40k20k0
13:3513:4013:4513:5013:5514:0014:05
Started18 Apr, 14:05:50.558
0ms4ms8ms12ms16ms20ms
Ended18 Apr, 14:05:50.577
0ms4ms8ms12ms16ms20ms
Compare Axiom with
See how Axiom compares to leading observability platforms across performance, pricing, scalability, and ease of operation.
SplunkSame query power. Modern infrastructure. No renewal-cycle math.DatadogOne dial across every signal. No per-host, no Standard Indexing SKU.ClickHouseA managed data platform, not a query engine you assemble.ElasticColumnar storage for observability volumes. No cluster to run.
Keep every log. Predict every bill.
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