SquaredUp for DevOps & SRE
Dashboards that tell the full story.
Metrics, logs and traces tell you how your code is behaving. They don't tell you which pipeline deployed it, which third-party API is throttling you, why spend doubled overnight, or that checkout conversion is down. SquaredUp puts all of it on one dashboard, wherever the data lives.
100+ data sources • No agents, no pipelines • MCP-ready
checkout · full-stack view
8 signals · 6 tools · live query
Codep95 182ms · 0.4% errPrometheus
CI/CDrelease 4.21 shipped 14:02GitHub
Infra14/14 pods ready · AKS westAzure
3rd partypayments API 1.9% 429sStatus API
Cost$21.7k MTD · +18% WoWAzure + AWS
Security0 critical CVEs in imageDefender
ExperienceLCP 1.8s · 99.2% successApp Insights
Businessorders/min down 12%Snowflake
Orders/min vs payment-API errors
last 3h
Orders/min429s from payments API
Visibility across every tool in your stack
The problem
Your metrics are green.
Your customers aren't.
Your APM tool only sees the parts of the service it can instrument. Everything else lives somewhere else: the pipeline that shipped the change, the vendor API that's throttling you, last night's jump in cloud spend, the CVE in the running image. But when something goes wrong, you're still the one who has to explain it.
Observability today
- Metrics, logs and traces from your code, and nothing else
- Delivery, cost, security and customer data in four other tools
- Third-party dependencies nobody watches, because you can't instrument them
- Root cause found by opening tabs in order of suspicion
- Ingest bill that grows every time you instrument more
With SquaredUp
- Everything that affects the service, in one place
- Delivery, cost, security and business metrics beside the telemetry
- Vendor APIs watched through their status and response data
- Mapped dependencies, so you can see what affected what
- Queried in place, so watching more doesn't mean storing more
What you can see
One place to see everything
Each of these already lives in a tool you're not about to replace. SquaredUp reads them live and puts them side by side, so the team that owns a service can see everything that affects it.
Code telemetry
Latency, errors, saturation and traces from the services you ship.
Prometheus · App Insights · Datadog
CI/CD & delivery
What shipped, when, and through which pipeline. Plus the DORA metrics that come with it.
GitHub · Azure DevOps · Jenkins
Infrastructure
Clusters, nodes, queues, databases and the resource limits underneath them.
Azure · AWS · GCP · Kubernetes
Third-party dependencies
The payment gateway, auth provider and SaaS APIs you can't instrument but do depend on.
Status APIs · Web API plugin
Cost
Spend by service, team and environment, next to the performance you got for it.
Azure Cost · AWS CUR · GCP Billing
Security posture
CVEs in the image you just deployed, patch level, policy and config drift.
Defender · Tenable · Qualys
User experience
Real-user timings, synthetic checks and the journeys that matter most.
App Insights · Pingdom · RUM
Customer & business metrics
Orders, signups and conversion. The numbers that tell you whether an incident really matters.
Snowflake · SQL · Salesforce
How it works
No data moved.
Watching all of this would normally mean building a dozen data pipelines, which is why most teams stop at metrics, logs and traces. SquaredUp connects to each tool's API and queries it when you open a dashboard, so adding more sources doesn't give you more to run.
Connect, don't ingest
100+ integrations plus a Web API plugin. No agents to deploy, no exporters to maintain, no second copy of your telemetry to pay for.
Joins happen at query time
Put a deploy from GitHub next to a latency series from Prometheus and an order count from Snowflake, without moving any of them.
Map dependencies once
Map how your services, infrastructure and vendors connect, and every dashboard (and every AI agent) can use it.
Query path
GitHub
Azure
Prometheus
Snowflake
Defender
Vendor status
AWS cost
Jira
↓ live query ↓
SquaredUp data meshJoins across your tools at query time. Nothing stored.
↓
Team view
Scheduled digest
MCP / AI agent
Use cases
Every team gets the answers they need.
Seeing everything doesn't mean everyone stares at the same wall of charts. Each team, and each manager above it, picks out the part it cares about from the same live data. No second reporting stack required.
One board per service, including the parts you don't own
See an application alongside everything it depends on: queues, databases, caches and third-party APIs. The team that owns the service owns the dashboard, and SLOs and error budgets roll up to whoever needs them.
- Dependency health, including vendors you can't instrument
- SLOs and KPIs that roll up to management automatically
- Share a link with anyone, no seat needed
checkout-service · dependencies
Livecheckout-apip95 182ms
postgres · orders42 conn
payments-gateway (3rd party)1.9% err
redis · session cache98% hit
sqs · fulfilment queue12 msg
Error budget · 30 day window69% remaining
Report on delivery without anyone filling in a spreadsheet
Deployment frequency, lead time, change failure rate and MTTR, read straight from your source control, pipelines and incident tools. See how your pipelines perform next to what they deliver.
- DORA metrics computed from real events, not estimates
- Pipeline duration, queue time and flaky-test hotspots
- Delivery view for stakeholders who don't read Jira
DORA · trailing 90 days
6 teamsLead time trend
↓ 38% since Q1
Spend and risk beside the service that created them
Cost by team, environment and service, live from Azure, AWS and GCP, plus the CVEs and patch state of what you just shipped. Engineering decisions drive both, so both belong on the engineering dashboard.
- Multi-cloud spend in one view, no cost-tool licence required
- Vulnerability and patch state per service, not per estate
- Scheduled monthly summary for finance and engineering leads
Cloud spend by team · MTD
Azure + AWSData platform$48.2k
Checkout$21.7k
Search$14.1k
Tie what you ship to what customers actually do
Put real-user timings and journey success rates next to orders, signups and conversion, pulled from your analytics warehouse or straight from SQL. It's the quickest way to tell whether an incident matters, and whether a release helped.
- Business metrics on the same board as the release that moved them
- Journey-level success rates, not just endpoint uptime
- One board engineering and the business both read
Checkout journey · today
RUM + SnowflakeLCP p75
1.8s
Journey OK
99.2%
Conversion
3.1%
Orders/min · deploy markers
last 12h
Release 4.21 at 14:02 · orders/min has not recovered
Features
The visibility layer that drops right in.
Flexible dashboards and alerting
Build the view your service needs rather than the one a vendor's template assumes. Alert on the same queries you chart.
Easy sharing
Send a link to anyone who wants to look, or schedule it for people who'd rather it came to them. Nobody needs a seat to read it.
Connects to literally anything
Cloud, CI, source control, issue tracking, metrics stores, security scanners, warehouses, and anything else with an API.
Dependency mapping
Keep track of how services, infrastructure and vendors connect, so you can see the blast radius and root cause without digging.
AI agents over MCP
Let AI agents query everything you've connected, for incident triage, weekly summaries or building dashboards.
Already running Grafana or Datadog?
Keep them for deep code telemetry. Use SquaredUp for the full service view and the dashboards you share outside the team.
Get started for free.
No agents to install and no pipelines to build. Connect your first tool in minutes.
