Observability,
finally rebuilt around AI.

Squasher unifies errors, logs, traces, and uptime into one observability platform — with AI agents woven into every surface to detect, diagnose, and resolve incidents.

Squasher detective bug — reads the stack trace, finds the offending commit
app.squasher.ai/monitors

Monitors

12
Search ⌘K+ New monitor
Total monitors
12
Uptime · 30d
99.81%
+0.02%
Global p95
184ms
iad · sfo · fra
Active incidents
1
auth-svc
api-prod / healthHTTP
99.998% · 30d · 184ms p95
12s ago
checkout-svc / orderHTTP
99.812% · 30d · 92ms p95
4s ago
auth.squasher.ai / certSSL
38d remaining
1m ago
webhook ingestHeartbeat
100.0% · 30d · 41ms p95
8s ago
auth-svc / loginHTTP
98.412% · 30d · 612ms p95
now
logs.squasher / drainOTLP
99.987% · 30d · 118ms p95
6s ago
edge / dnsDNS
100.0% · 30d · 22ms p95
11s ago
Plugs into your existing stack
Linear logoVercel logoSupabase logoModal logoRailway logoCloudflare logoGitHub logoSlack logoPagerDuty logoOpenTelemetry logo
What AI-native means

Most "AI features" are bolted on. We rebuilt the platform around the agent.

Squasher is not Sentry with a chat sidebar. The AI is in the ingest pipeline, in the alert routing, in the incident timeline, and in the fix loop — because the data shape was designed for it.

01

AI in the ingest path.

Every event is fingerprinted, grouped, and enriched by an LLM on the way in. Stack traces dedupe against your codebase, not a string hash. Logs cluster semantically. The signal is clean before you see it.

02

AI on the alert surface.

When an alert fires, an agent reads the stack trace, cross-references the last 30 days of deploys, and posts the root cause — with file, line, and likely commit — before the page reaches a human.

03

AI in the fix loop.

The agent opens a tight pull request with a regression test. On confirmed sev-1 regressions, it rolls back automatically. Every action is cited and reversible.

Errors

Every exception, grouped by an LLM on the way in.

Stack traces are fingerprinted against your codebase, not a string hash. So three different 'TypeError: undefined' from three different files become three issues, not one.

  • 13 first-party SDKs
  • Semantic grouping, not string hashing
  • Group by deploy + auto-resolve
app.squasher.ai/errors
SignatureEventsUsersTrend
NullPointerException · applyDiscountCode
checkout-svc / validator.ts:184 · deploy 2c91f4a
24789▲ 12.0%
prod
TimeoutError · expected 200, got undefined
api-gateway / proxy.ts:212
2712— flat
prod
TimeoutError · upstream timed out
billing-svc / stripe.ts:88
186▼ 3.1%
prod
ConnectionRefusedError · redis://cache:6379
session-svc / cache.ts:41
83— flat
staging
RedisError · "stream.id"
log-ingest / stream.ts:91
31— flat
prod
Logs + traces

One ingest endpoint. Native OTLP.

No exporters, no Vector, no Fluent Bit. The AI uses the trace graph to walk between services when diagnosing — so you can finally ask 'why is checkout slow?' and get an answer.

  • Native OTLP ingest
  • Log clustering, not log search
  • Trace-aware AI diagnosis
app.squasher.ai/logs
service:checkout-svc level:>=warn last:5mlive
14:23:41.812ERRORcheckout-svcNullPointerException at applyDiscountCode (validator.ts:184)
14:23:41.806INFOapi-gatewayPOST /v1/checkout/apply-promo 200 312ms session=ab12cf
14:23:41.781WARNauth-svcsession refresh slow: 412ms (budget 200ms)
14:23:41.750ERRORcheckout-svcNullPointerException at applyDiscountCode (validator.ts:184)
14:23:41.711INFOsession-svcOTLP: span checkout.process · 184ms · trace=4f2c
14:23:41.642INFOapi-gatewayGET /v1/cart 200 88ms session=ab12cf
14:23:41.611WARNcheckout-svcpromo BLACK_FRIDAY_24 expired_at=2024-12-01 — soft drop
14:23:41.512INFOwebhook-ingstripe.charge.succeeded ch_3x9... 312ms
Uptime

Thirteen monitor kinds. Six regions. One bill.

HTTP, heartbeat, DNS, SSL, TCP, SMTP, port — checked every 30 seconds from iad, sfo, fra, lhr, syd, gru. Public and private status pages stay in sync with the incident.

  • 30-second cadence, 6 regions
  • Status pages auto-sync with incidents
  • SSL + DNS expiry alerts included
API · publicOperational
60 days ago99.98%Today
DashboardOperational
60 days ago99.98%Today
Webhooks ingestOperational
60 days ago99.98%Today
OTLP tracesDegraded performance
60 days ago99.74%Today
Auth · session refreshOperational
60 days ago99.98%Today
Status pagesOperational
60 days ago99.98%Today
On-call

The agent takes the page first.

Schedules, rotations, overrides, escalations — designed for human responders. The AI takes every page first; humans only get woken when judgement is needed.

  • AI takes the page first
  • Native iOS + Android apps
  • Schedule overrides with one tap
14:21•••📶🔋
On-call
SEV 1INC-2814
38s
checkout-svc NullPointerException spike
47 traces / 2m · 12.0% error rate
~ 412 customers affected
AI SRE is handling this
Diagnosed regression in deploy 2c91f4a. Rolling back now, PR #4221 opened.
Incidents

From alert to mitigation, in one timeline.

When something breaks, an incident opens. The AI investigates, the team gets a Slack ping with full context, the fix lands as a PR. The whole arc lives in one place.

  • Auto-correlate alerts → incident
  • PagerDuty, Slack, Discord, statuspage
  • Every action audited + reversible
app.squasher.ai/incidents/2814
INC-2814SEV 1
Mitigating

checkout-svc · NullPointerException spike

Error rate
12.0%
Customers
~412
On-call
AI SRE
EVENT
14:21:04
NullPointerException · checkout-svc · 47 traces in 2m
AI SRE
14:21:18
Regression in deploy 2c91f4a — applyDiscountCode leaves code=undefined on expired promos.
TOOL
14:21:24
gh pr create · #4221 with null-check + regression test
AI SRE
14:21:42
Rolling back 2c91f4a in prod. Error rate 12.0% → 0.4% in 38s.
Every surface, a specialist

Built by an on-call rotation that isn't sleeping anymore.

Behind every Squasher surface is a specialized agent. They share the same data model, hand off cleanly, and only escalate to a human when judgement is required.

Squasher detective bug — reads the stack trace, finds the offending commitInvestigatorReads the stack trace, finds the offending commit.
Squasher stream-watcher bug — tails logs and traces, surfaces the anomalyStream watcherTails logs and traces, surfaces the anomaly.
Squasher mechanic bug — opens the pull request and the regression testMechanicOpens the pull request and the regression test.
Squasher ninja bug — rolls back the deploy in under a minuteFirst responderRolls back the deploy in under a minute.
Squasher intern larva — the new SDK user
Agent install

Give this to your agent.

Install Squasher's agent skills and hosted MCP once, then hand the onboarding prompt to your coding agent. It can add the SDK, drains, or OpenTelemetry path that fits your repo.

agent-terminal · install.sh
# Install Squasher skills + hosted MCP for your agent CLI
bash <(curl -fsSL https://squasher.ai/install.sh) --agents -y

# Then give this prompt to your coding agent:
# Use $squasher-onboard to install Squasher in this repo
# and verify one real runtime event.

# Skills index:
# https://skills.squasher.ai/
Pricing

Priced by event volume. Not by per-seat tax.

Every plan ships the full observability stack. AI triage is included on Team and up — no nickel-and-diming the incident response.

Pro

$29/mo

Production for small teams

Start free
  • AI triage on every incident
  • Source maps + release tracking
  • Replay-linked issue triage
  • All log drains

Team

Recommended
$99/mo

Replace Sentry, replay, and status tooling

Start free
  • Everything in Pro
  • Auto-fix pull requests
  • GitHub integration
  • Status pages + maintenance windows

Scale

$299/mo

High-volume teams + procurement

Talk to us
  • Everything in Team
  • SSO + SCIM + audit log
  • Dedicated VPC option
  • Solutions engineering

Switch to AI-native observability.

Install the SDK in 90 seconds. The agent diagnoses your first real incident the same day.

Squasher QA bug — runs the checks before you ship