Frontier agentic AI

World’s Leading Agentic AI Observability Platform for Highly Regulated Industries.

Patent-pending compression for 90% cost reduction on observability and SIEM.

~90%
Cost cut
Agentic
AI native
100%
Lossless
SIEM
Built-in

Overview

6s agoProductionM

COSTS SAVED

$4.28M

live · blended SIEM rate

LOGS COMPRESSION

18.2×

+16.2× vs gzip

TRACES COMPRESSION

27.5×

OTLP · tail-sampled

METRICS COMPRESSION

30.1×

OTLP + Prom remote-write

EVENTS PROCESSED

14.3B

all 14.3B cold · 3.1B mirrored hot

TRACES IN CATALOG

42.6M

queryable · 30-day window

STORAGE SAVED

94.1%

218 TB less to store

ACTIVE TROLLS

247

all healthy · 0 stale

Cost Savings — last 24h

00:0004:0008:0012:0016:0020:0023:00
StorageEgressLogs ingestLogs retentionTraces ingestTraces retention

Optimization Ratio — last 24h

10×20×30×gzip baseline00:0004:0008:0012:0016:0020:0000:00
Logs 18.2×Traces 27.3×Metrics 29.8×

Signals in

Firewall
Auth
Syslog
K8s
VM
App
Cold storage
AWS S3Google Cloud StorageAzure Blob

Super-compressed signals, stored in your own cloud.

Snowman

Query firsthand data in your cloud. Zero third-party compute.

Trolls

Talk to your apps & services. Full SRE agents.

Observability
DatadogSplunkDynatrace

Keep your existing stack. No rip-and-replace.

Alerting
PagerDutySlackMicrosoft TeamsServiceNowJira

Route high-severity events to your favorite alerting tools.

AWSAzureGoogle CloudDatadogGrafanaKubernetesSplunkDockerElastic
Markets · Industries we serve

Where every byte must be retained for audit.

Compliance-heavy industries cannot use lossy observability tools. Regulators do not accept “1,247 similar events suppressed” in a forensic investigation. Sasquatch is engineered for the buyers who pay the most and audit the hardest.

01

Finance & Banking

SOX · PCI-DSS · GLBA

Trades, settlements, treasury, AML. Retention measured in years, not days. Every system-of-record event must survive an examiner walk-through. Lossless retention is non-negotiable.

02

FinServ & Insurance

SOC 2 · NAIC · Basel III

Claims, underwriting, brokerage, KYC. Regulator-grade audit trails across every customer interaction. Lossy data is a compliance violation, not an optimization.

03

Healthcare & Pharma

HIPAA · HITECH · GxP

PHI, clinical trials, GMP manufacturing, EHR. Every patient touchpoint and every batch record must be preserved exactly as recorded. Forensic-grade integrity.

04

Government & Defense

FedRAMP · FISMA · IL5

Air-gapped deployments, sovereign clouds, classified workloads. No external SaaS dependency. Observability that stays inside the perimeter, audit-grade by default.

05

Aviation & Aerospace

FAA · EASA · ICAO

Flight ops, maintenance, telemetry, ATC integration. Forensic-grade retention for incident reconstruction. Lossless or it is not evidence.

06

Energy, Utilities & Manufacturing

NERC CIP · SCADA · PI Historian

OT and IT convergence. Plant floor telemetry, grid sensors, asset health. Audit trails that satisfy regulators and incident investigators, including the moments before a fault.

The problem

The bill isn’t one number. You pay at every layer.

Observability and SIEM pricing are both multilayered, metered, and opaque by design. The subscription you approved is the smallest piece — the real cost is everywhere your logs, traces, and security telemetry touch.

Where the money goes
per GB · per EPS
DENY tcp 203.0.113.7 → 10.0.2.5:443DROP udp :53 rule=geo-blockFAILED login user=admin ip=198.51.100.4auth.success uid=4021 mfa=ok<34>1 sshd[2201]: session openedkernel: eth0 link up 10Gbpspod=payments-7f reason=OOMKilledevt restart count=3 ns=prodcpu=84% mem=92% disk=/var 95%systemd: nginx.service restartedERROR 500 /api/pay 512ms trace=7f3cspan db.query dur=210ms · 5xx rate 0.3+ $0.09 / GB egress+ bytes stored × 3 copies+ $4.20 / GB ingested+ $/GB·mo · 12-mo hold+ $2.50 / search-hourPII + secrets leave the perimeter ↗FirewallAuth logsSyslogK8s logsVM logsApp · traces · metricsEgressStorageSIEM ingestRetentionQueryPrivacy riskYourdata
Logs, traces & metrics flow in Metered at every cost layer Sensitive data leaves your perimeter
Two systems, the same trap

You run observability AND a SIEM, and each meters every layer independently. No two invoices line up — one bills reserved compute, another splits ingestion, indexing, storage, and retention — so you can never actually compare what you pay.

SIEM ingest is the brutal one

Security volume is priced per GB — several dollars per GB at the high end — or by events-per-second. Firewall, auth, and syslog throughput becomes a hard cost ceiling on how much you can even afford to watch.

You pay to keep it, and to get it back

Raw, indexed, and archived copies are each metered and compound with every retention extension. Then egress and retrieval land again every time you audit, query, or rehydrate.

You pay to ask questions of it

Search and query compute routinely exceeds the ingestion line for active tenants. Investigating an incident is its own cost center.

It all leaves your perimeter

Incumbents are cloud-only. Shipping firewall, auth, and security logs to a third-party SaaS is a privacy and compliance exposure for regulated buyers — stacked on top of the bill.

The agentic platform Voice-native

Ask out loud. It finds the root cause and files the ticket.

Sasquatch agents reason across every log, trace, span, and metric in your stream. Investigate any incident end to end, talk to any agent about what it is seeing live, and open the ticket in Linear, Jira, or ServiceNow. By voice or one click.

Autonomous investigation

Every signal, one root cause.

Point an agent at an error and it does the legwork: pulls the full trace, walks each errored span, correlates the logs, and checks service health and error-rate metrics. Then it writes the root cause with the evidence and a fix, and files the ticket, pre-filled and linked back to the trace.

  • Reasons across traces, spans, logs, and metrics together
  • Cites the exact spans and log lines it used
  • Files the ticket only when you say so
Root-cause analysis
done
Pulled the trace · 142 spans
Walked 7 errored spans
Correlated 1,204 logs
Checked service health + error-rate
Probable root cause

payments-service exhausted its DB connection pool (50/50); requests waited 3000ms then 503'd, cascading to api-gateway.

Filed in JiraKAN-302
Talk to this Troll
Bound to node-7
listening
Error in context

ERROR payments-service · 503 Service Unavailable · trace 7f3c… · /pay/capture

you
troll
outbound ▶
Investigation
You: why is payments throwing 503s?
Talk to your Trolls

Ask your fleet anything, live.

Every Sasquatch agent (a Troll) sees everything flowing through its node. Talk to it directly: which services are erroring, how payments is doing, any slow traces in the last hour. It answers from the live stream with real numbers. Voice-native, with no query language to learn.

  • Talks straight to the ingestion point, not a stale index
  • Services, incidents, and system health on demand
  • Hand off to a full investigation, then file the ticket
Native two-way integrationsFile the ticket, page on-call, or post the channel — where your team already works.
Linear logo
Linear Live
ENG-412 ↗
Jira logo
Jira Live
KAN-302 ↗
ServiceNow logo
ServiceNow Live
INC0010042 ↗
Slack logo
Slack Live
#incidents ↗
Microsoft Teams logo
Microsoft Teams Live
Posted to channel ↗
PagerDuty logo
PagerDuty Live
Incident triggered ↗
The platform

Pay less. Keep everything.

Sasquatch learns your telemetry shape at the edge, compresses every byte losslessly, and stores the result in your own cloud. Same data. Same compliance. ~91% less spend.

01
Schema-aware

Calibrated to your environment.

The compression model adapts to the shape of your telemetry — the patterns and structure unique to your stack. Not a generic compressor. That calibration is where the 15–18× comes from.

02
Mathematically lossless

Every byte survives.

SHA-256 compare on decompress vs the original, verified on every event. Not “less than 1% data loss.” Not “statistically similar.” Exact bytes. Every time.

03
Instant retrieval

Cold logs are never gone.

Pull any time range from your bucket, decompress on demand, forward to any SIEM in seconds. Re-hydrate for incidents or audits without paying twice to ingest.

One pipeline · three signalsratios on realistic K8s + OTLP corpora · lossless
Logs
OTLP · CRI · Hadoop · Spark
18×
Traces
OTLP · Tempo · Honeycomb · Datadog APM
27×
Metrics
OTLP · Prometheus remote-write
30×
Query anywhere

Use the query language your team already runs.

Sasquatch ships its own query engine, Snowman, that speaks the protocols your existing tools already speak. Drop our endpoint into Grafana, point your Splunk dashboards at it, keep your PromQL alerts. The chunks are yours, in your bucket — we just make them queryable.

Datadog
Logs Search · DQL

The single largest observability surface on the market. Point your existing Datadog Logs and APM searches at Sasquatch — same tag-and-facet syntax, same dashboards, same alerts. Cut the ingest line item, keep the workflow your team already lives in.

service:payments status:error
  @duration:>500ms
  | stats count by host
Splunk
SPL

SPL parser + REST API shim. Splunk-shaped searches resolve against your Sasquatch chunks — no Splunk indexer required to search them.

index=app sourcetype=k8s_pod
  level=error timeout
  | stats count by service
Grafana / Loki
LogQL

Drop in Sasquatch as a Loki datasource. Your existing Grafana dashboards, alert rules, and ad-hoc Explore queries keep working — same LogQL, same response shape.

{namespace="payments",level="error"}
  |~ "timeout"
  | rate(5m)
Elastic / Kibana
KQL · Lucene

Kibana queries (KQL) and Lucene-shaped searches resolve through the same adapter. Your existing Discover boards, Lens visualizations, and alert rules keep working — point them at Sasquatch instead of the Elastic ingest pipeline.

service:"payments" AND level:"error"
  AND @timestamp > "now-5m"
  AND duration > 500
Grafana / Tempo
TraceQL

OTLP traces compressed at the edge, queryable from the same Tempo datasource panel. Trace ID lookup is fast against your cold storage — no full-bucket scan.

{ resource.service.name = "api-gateway"
  && status = error
  && duration > 500ms }
Prometheus
PromQL

PromQL adapter over the metric chunks Sasquatch already compresses. Existing alert rules and recording rules continue to evaluate against the same series labels.

rate(http_requests_total{
  status=~"5.."
}[5m])

No re-indexing

Indexes are baked into the chunk format. No separate ElasticSearch cluster, no nightly rebuild — query directly against your cold storage.

Cost is yours, not the SIEM's

Query compute is the line item that breaks SIEM budgets. With Sasquatch the marginal cost of a search is cloud egress + a slice of CPU — not a licensed search-compute unit.

Migrate without lifting

Run your existing dashboards against Sasquatch in shadow mode. Same Loki / SPL / PromQL output, same result counts. Cut over when you're sure.

Where it runs

Kubernetes, bare metal, big-data clusters. One agent. One wedge.

Whatever shape your telemetry comes in, Sasquatch reads it where it’s generated. Containers, host syslog, Hadoop NameNode, Spark drivers, MongoDB rotated logs — same lossless compression path, your choice of cloud bucket.

01
Cloud Kubernetes

EKS · GKE · AKS · self-managed.

A DaemonSet drops one agent per node. CRI log tail picks up /var/log/containers; an OTLP receiver on :4317 / :4318 takes traces and metrics straight from your apps. Native cloud identity — IRSA on AWS, Workload Identity on GCP, Managed Identity on Azure. No service-account sprawl, no extra credentials.

JSON logsOTLP traces · metricsHelm chartamd64 · arm64
02
Bare metal & Linux

syslog · journald · file tail.

Static-musl binary plus signed DEB and RPM packages on apt + yum repos. Tail rotated logs, listen on syslog (RFC 3164 / 5424 over UDP or TCP), or pull from journald. Datacenter, branch site, air-gapped network — same agent, no Kubernetes required, no internet round-trip on the hot path.

DEB · RPM · tarballsystemd unitair-gapped OKamd64 · arm64
03
Big data & databases

Hadoop · Spark · Mongo · Postgres.

A second agent variant covers two new shapes. Text mode (CLP-T) compresses Hadoop, Hive, OpenStack, and Java application logs. JSON mode (CLP-S) compresses MongoDB, CockroachDB, Elasticsearch, and Spark event logs. Same engine, one --format flag, beats the reference open-source compressor on every published corpus.

CLP-T textCLP-S structured JSONHadoop · Spark · HiveMongo · Cockroach · ES

Compressed chunks land in your bucket of choice — S3, GCS, Azure Blob, R2, MinIO.Hot events mirror to the SIEM you already run. Full destination list on /integrations.

End the rent

See what you stop paying.

Send us a sample of your actual log traffic. We’ll run it through Sasquatch, verify it decompresses byte-for-byte, and hand back a real number — your projected monthly spend on your current stack, vs on us.

No contract, no “qualification call,” no sales funnel. Engineers talking to engineers.

Real cost math
Your bytes, your bill today, vs with Sasquatch — numbers, not percentages.
Proof of lossless
SHA-256 round-trip on every event in your sample. Not a claim, a check.
Architecture review
30 minutes with the engineers who built it. Questions go straight to the source.
No rip-and-replace
Helm install drops in alongside your existing stack. Revert is one command.

Our one promise

“You should not pay more for observability than for the app infrastructure you’re observing. And you should never have to choose between good observation, audit trails, and cost.”

Engineers reply within a business day. No sales funnel, no drip campaign.