Production intelligence for software and AI

When software or AI breaks,
know why.

Squasher connects errors, logs, traces, replays, AI generations, user feedback, and cost in one evidence trail—then helps humans and agents investigate, fix, and verify production.

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ErrorsLogsTracesUptimeReplaysAI generationsFeedbackCost

AskWhat changed before checkout started failing?

incident / checkout-api

investigating

Sev 1 · 2m ago

Checkout failures after deploy

impact

412 users

00:00Deploy 9c4a1f reached production

00:41Payment errors crossed baseline

01:12Incident opened and agent assigned

Squasher agent

Root cause found

Deploy 9c4a1f changed the currency parser. Null locale values now throw before payment authorization.

Fix ready for review

src/payments/currency.ts · +8 −2

Tests passRegression covered

See how AI triage works

One context layer for the stack you already run

From fragmented signals to production truth

Stop stitching production together.
Start improving what users experience.

Before Squasher

Five tools. One tired on-call.

×Search app failures, AI runs, feedback, and deploys in separate tools
×Paste screenshots, prompts, and stack traces into a chat window
×Page the on-call before anyone understands the impact
×Ship a guess and wait to see if the alert clears

With Squasher

One context. A resolved incident.

Connect software failures, AI behavior, and user impact on one timeline
Receive a cited root cause with trace, generation, file, and deploy
Let Squasher assemble the evidence before escalating to a human
Review a regression-tested fix and track whether it stays fixed

AI woven through the platform

Every signal becomes usable context.

AI is in the ingest path, the alert surface, and the fix loop—not bolted onto a search page after the fact.

AI incident response

The agent starts with evidence, not a guess.

Squasher walks the trace graph, recent deploys, logs, replay, and affected users together. Every conclusion links back to the production evidence that supports it.

Trace-aware root cause analysis
Deploy and code correlation
Regression-tested fix pull requests

Explore ai incident response

Pixel-art Squasher investigator converting tangled incident signals into a verified fix

Verified resolution

One record from detection through recovery.

Squasher connects the diagnosis, cited evidence, tested fix, CI result, deployment, and recurrence watch on the same incident record. Humans keep control of consequential changes.

One shared incident timeline
Audited and reversible actions
Human escalation with complete context

Explore verified resolution

Pixel-art Squasher agent team coordinating around an observability network

AI product observability

Know what the model did—and whether users wanted it.

Trace prompts, system instructions, generations, tool calls, tokens, cost, and feedback beside the application services that produced them. Turn scored production evidence into eval and training datasets without losing lineage.

Prompt, response, and tool-call lineage
Token, cost, quality, and feedback evidence
Exportable evaluation and training datasets

Explore ai product observability

Illustrative production trace

support-agent / response

evaluated

Input

linked

Cost

tracked

Feedback

scored

01System + user inputcomplete

02Model generationcomplete

03Tool callcomplete

04User feedbackpositive

Training-ready evidencetrace + score + lineage

Agentic setup

Set it up your way.

Connect the stack yourself in the dashboard, or hand one prompt to your coding agent and let it choose the right SDK, log drain, or OTLP path.

Do it yourself3 steps

Start collecting real production context.

01Create a project and copy the generated DSN.
02Connect an SDK, drain, or OpenTelemetry endpoint.
03Send one event and verify it in the live stream.

Create a project

Let your agent do itRecommended

Paste one prompt. Get a verified integration.

The agent installs Squasher, connects the right telemetry path, and confirms that a real event arrived.

agent setup

Install Squasher using https://squasher.ai/install.sh, then use $squasher-onboard to connect this repo and verify one real runtime event.

One platform · four surfaces

The same production truth, wherever work happens.

The dashboard, API, CLI, and MCP share the same contracts. Teams and agents can move between surfaces without losing context or learning a second product.

Read the API docs

Dashboard01

Explore incidents, traces, logs, replay, and uptime with your team.

app.squasher.ai

API02

Build on the same typed contracts that power Squasher's first-party surfaces.

api.squasher.ai/v1

CLI03

Query production context and automate incident workflows from the terminal.

squasher incidents list

MCP + skills04

Give coding agents bounded access to context, onboarding, and investigation.

$squasher-onboard

Simple pricing

Pay for production volume.
Not every person who helps.

AI triage is part of the platform. Every plan brings errors, logs, traces, uptime, and incident context together.

Pro

$29/mo

Production observability for small teams.

Start free

AI triage on every incident
Source maps + release tracking
All log drains

Recommended

Team

$99/mo

Replace fragmented monitoring and response tools.

Start free

Everything in Pro
Auto-fix pull requests
Status pages + maintenance windows

Scale

$299/mo

High-volume teams with security requirements.

Talk to us

Everything in Team
SSO + SCIM + audit log
Dedicated VPC option

Start with one real event

Give your team—and your agents—the full production story.

Connect an SDK, log drain, or OTLP endpoint. Squasher turns the next incident into cited context and a clear path to resolution.