How is Vantage different from ChatGPT or Claude?

ChatGPT and Claude are great for one-off generation. Vantage is a full workspace. It connects your tools, tracks dependencies across projects, catches conflicts between products, and surfaces what needs your attention every morning. It also remembers every past decision, so a new PM can query across all projects and get cited answers in seconds.

Do I have to switch away from Notion or Linear?

No. You keep all your tools. Vantage connects to them and builds intelligence on top. When you're ready, you can push tickets to Linear or Jira. But the value starts before you create anything — just by connecting your tools and seeing what the intelligence layer surfaces.

What happens on day one?

You connect your tools — GitHub, Linear, Slack — 2 minutes each. Vantage indexes your existing data overnight. By the next morning, you see your first decision queue: what's stale, what conflicts, what changed. No manual setup. No templates to fill out.

What if I only manage one project?

Vantage works for single projects, but it shines when you manage 3–5 or more. Conflict detection, cross-project memory, and the decision queue are most valuable when you have multiple projects that need to stay in sync. If you manage one project, start with the free tier and see if the cited query engine alone saves you time.

All data encrypted in transit and at rest. We never train on your data — the AI providers we use are contractually barred from it. You can disconnect any integration or delete your account at any time. Pursuing SOC 2 Type II.

How does Vantage keep documents and tickets up to date?

When context shifts, you add the new information and Vantage rebuilds every connected deliverable. Documents, prototypes, and tickets all update. You review the changes, accept or reject each one, and approve. Nothing updates silently, and every change is traceable.

How do I get started?

Sign up free at app.vantageos.tech. Connect your tools, and generate your first grounded document within minutes. No credit card required. Upgrade when you need more projects or features.

What is the difference between an SLA and an SLO?

An SLA (Service Level Agreement) is a contractual commitment to customers with penalties for violation. An SLO (Service Level Objective) is an internal target that you set above your SLA threshold to give yourself a safety margin. For example, if your SLA promises 99.9% availability, your SLO might be 99.95% — the gap is your buffer before the contractual penalty kicks in.

How do I calculate the error budget remaining?

Error budget = (1 - SLO target) * total requests (or total time) in the compliance window. If your SLO is 99.9% over 30 days, your total error budget is 0.1% of requests or 43.2 minutes. Subtract the errors that have already occurred to get the remaining budget. Express it as a percentage for the Gauge panel.

Should PMs care about SLO dashboards?

Yes. SLO dashboards tell PMs whether the team has error budget to spend on shipping features or needs to pause and invest in reliability. When the error budget is healthy, you can ship faster and accept more risk. When it is low, pushing new features risks breaching the SLO and degrading user experience. SLOs are the bridge between engineering reliability and product velocity.

How-To2026-09-1111 min read

How to Build an SLO Dashboard in Grafana

Service Level Objectives transform reliability from a vague aspiration into a measurable commitment. Instead of debating whether the system is 'reliable enough,' SLOs give you a concrete threshold — 99.9% availability, p99 latency under 200ms — and an error budget that tells you exactly how much unreliability you can tolerate before users notice. Without SLOs, every performance regression triggers a fire drill; with them, you make informed trade-offs between shipping features and improving reliability.

Grafana is the natural home for SLO dashboards because it connects to virtually every data source where your Service Level Indicators live — Prometheus, Datadog, CloudWatch, custom databases. A well-built SLO dashboard shows the current burn rate, remaining error budget, and historical trends at a glance, enabling both engineering leadership and on-call engineers to make fast, informed decisions about where to invest reliability effort.

Step-by-step guide

Define your SLIs and SLOs before building anything

An SLO dashboard is only as good as the objectives it tracks. For each critical user journey, define a Service Level Indicator (the metric you measure) and a Service Level Objective (the target). Common SLIs are availability (percentage of successful requests), latency (percentage of requests under a threshold), and throughput (successful operations per time period). For each SLI, set an SLO target based on user expectations and business requirements, not engineering aspiration.

List your 3-5 most critical user journeys (e.g., login, checkout, search, API response)
Define one or two SLIs per journey: availability and/or latency
Set SLO targets based on user impact analysis (e.g., 99.9% availability = 43 minutes downtime per month)
Choose a compliance window: 30-day rolling is most common for product teams

Set up data sources in Grafana

Configure the data sources that contain your SLI data. If using Prometheus, ensure your metrics include labels that let you filter by service, endpoint, and status code. Add the data source in Grafana under Configuration > Data Sources and test the connection. If your SLIs come from multiple sources (e.g., availability from Prometheus and latency from a custom ClickHouse table), add each source and note which SLIs map to which source.

Add your primary metrics data source (Prometheus, Datadog, CloudWatch, etc.)
Verify the data source connection returns expected metric names
If SLIs span multiple data sources, document the mapping for future dashboard maintainers

Create the error budget panel

The error budget panel is the most important visualization on the dashboard. It shows how much unreliability you can still afford before breaching your SLO. For a 99.9% availability SLO over 30 days, your error budget is 0.1% of total requests. Create a Gauge panel that shows the remaining error budget as a percentage: 100% means no errors consumed, 0% means the SLO is breached. Color the gauge green above 50%, yellow between 20-50%, and red below 20%.

Calculate error budget: (1 - SLO target) * total requests in the compliance window
Create a Prometheus query: 1 - (sum(rate(http_requests_total{status=~'5..'}[30d])) / sum(rate(http_requests_total[30d])))
Configure gauge thresholds: green > 50%, yellow 20-50%, red < 20%
Add a stat panel next to the gauge showing the exact remaining budget in minutes or request count

Build the SLI trend graph

Create a Time Series panel showing your SLI value over the compliance window. Plot the actual SLI (e.g., 99.94% availability) as a line, and overlay the SLO target (e.g., 99.9%) as a constant horizontal line using the 'Threshold' visualization option. This shows whether you are trending toward or away from your objective. Add a second y-axis for request volume so you can correlate SLI changes with traffic patterns.

Create a time series query calculating the SLI over rolling 1-hour windows
Add the SLO target as a constant threshold line in red
Add total request volume as a secondary series on the right y-axis
Set the time range to match your compliance window (e.g., last 30 days)

Add the burn rate alert panel

Burn rate measures how fast you are consuming your error budget relative to the compliance window. A burn rate of 1.0 means you will exactly exhaust the budget by the end of the window. A burn rate above 1.0 means you are on track to breach the SLO. Create a Stat panel showing the current burn rate, and set up Grafana alerts that fire when the burn rate exceeds critical thresholds — typically 14x for page-level alerts (budget exhausted in 2 hours) and 6x for ticket-level alerts (budget exhausted in 5 hours).

Calculate burn rate: (error rate in window / (1 - SLO target))
Create a Stat panel with color coding: green < 1, yellow 1-6, red > 6
Configure a Grafana alert rule for burn rate > 14 (page) and > 6 (ticket)
Route alerts to your on-call tool (PagerDuty, OpsGenie, etc.)

Organize the dashboard layout

Arrange the dashboard for progressive disclosure. Top row: one Gauge per SLO showing remaining error budget — this is the executive summary view. Second row: SLI trend graphs for each SLO. Third row: burn rate panels and alert status. Bottom section: collapsible rows with detailed breakdowns by endpoint, region, or service for debugging. Add dashboard-level variable filters for service and environment so the same dashboard works for all teams.

Create a top row of Gauge panels — one per SLO — for the at-a-glance view
Group detailed panels in collapsible rows so the dashboard is not overwhelming
Add template variables for service, environment, and time range
Set the dashboard refresh interval to 1 minute for real-time monitoring

Common mistakes

Setting SLO targets too aggressively

A 99.99% availability target sounds impressive but gives you only 4.3 minutes of error budget per month. If your infrastructure cannot realistically achieve this, you will be in perpetual SLO breach, which makes the dashboard useless because the team learns to ignore it. Start with achievable targets based on historical performance and tighten them over time.

Not aligning SLOs with user-facing journeys

Tracking SLOs for internal microservices rather than user-facing journeys produces metrics that do not correlate with user experience. A backend service might be 99.99% available while the user-facing checkout flow is at 99.5% because of a frontend bottleneck. Define SLOs from the user's perspective.

Building the dashboard without alerting

A dashboard that nobody looks at between incidents is passive monitoring. Burn rate alerts transform SLO dashboards from passive displays into active reliability management tools. Without alerts, the team only checks the dashboard after users complain — which defeats the purpose.

Tips

Add an annotation layer to the SLI trend graph that marks deployment events — this makes it trivial to correlate SLI changes with specific releases.

Create a weekly SLO review habit where the team reviews error budget consumption and decides whether to invest the next sprint in features or reliability.

Use Grafana's 'Library panels' feature to create reusable SLO panel templates so adding a new SLO to the dashboard takes minutes, not hours.

Include a 'Historical SLO Compliance' table that shows monthly SLO attainment for the past 12 months to track long-term reliability trends.

How Vantage helps

Vantage integrates reliability context into the product planning process. When generating PRDs and tickets, Vantage can surface relevant SLO and performance constraints, ensuring that new features are planned with reliability requirements built in from the start rather than bolted on after launch.