When most organizations say “we have disaster recovery,” what they usually mean is “we have backups.” Backups are important, but they are not disaster recovery. Backups are data copies. Disaster recovery is the ability to restore service within a defined time window, under pressure, with repeatable steps and predictable outcomes.

For SMBs, the risk is straightforward: downtime kills revenue, productivity, and customer confidence. For federal civilian programs, the risk is broader: you need to demonstrate recoverability with documented decisions, runbooks, and test evidence that stands up to oversight.

This guide breaks down RTO and RPO in practical terms—and shows you how to turn DR from a hopeful assumption into a provable capability.

RTO and RPO: what they actually mean

• RTO (Recovery Time Objective): the maximum acceptable time a service can be down before the impact becomes unacceptable.

• RPO (Recovery Point Objective): the maximum acceptable amount of data loss measured in time (for example, “up to 15 minutes of data loss”).

These are not technical settings. They’re business decisions that drive architecture, cost, and operational requirements. The common failure pattern is choosing a DR approach before leadership agrees on RTO/RPO—then discovering later that the design is either too expensive or too slow.

The DR patterns that matter (and when to use them)

Disaster recovery is primarily a tradeoff between cost, complexity, and speed. A simple way to choose is to map RTO/RPO to one of these patterns:

1) Backup and Restore

• Best for: non-critical systems where hours-to-days recovery is acceptable

• Tradeoff: lowest cost, slowest recovery

• Reality check: many teams think they have DR when they only have this

2) Pilot Light

• Best for: important systems where you need a faster start, but can tolerate scaling during recovery

• Tradeoff: moderate cost, moderate recovery speed

• Key detail: core components are always on, but capacity ramps during failover

3) Warm Standby

• Best for: systems that need faster recovery and operational stability

• Tradeoff: higher cost, faster recovery

• Key detail: a functional environment is already running at reduced capacity

4) Multi-site / Active-Active

• Best for: mission-critical systems where downtime is unacceptable and budget supports it

• Tradeoff: highest cost and complexity

• Key detail: this isn’t the default option—use it only when requirements justify it

The goal isn’t to pick the “best” pattern. The goal is to pick the right pattern for each workload tier, based on actual business tolerance for downtime and data loss.

Backups are not proof — tests are proof

A dashboard that says “backup succeeded” is not evidence you can recover. The only proof is a tested recovery motion with documented outcomes. Proof looks like:

• an agreed RTO/RPO per workload tier

• a DR pattern selected per tier (with rationale)

• runbooks that name steps and owners

• a DR test plan

• executed test results

• a remediation backlog with owners and dates

If you can’t produce those artifacts, you’re relying on heroics, not a recovery program.

A practical DR readiness approach (what to do next)

You don’t need a 6-month strategy exercise. You need a controlled readiness motion.

Step 1: Tier your workloads

Group systems into 3–4 tiers such as:

• Mission Critical

• Important

• Standard

• Non-critical

Then assign target RTO/RPO ranges per tier. This prevents every application owner from declaring “we need zero downtime,” which is usually untrue and always expensive.

Step 2: Select a DR pattern per tier

Match each tier to an appropriate DR pattern and document assumptions:

• what’s in scope

• what is not in scope

• expected recovery sequence

• what dependencies could slow recovery

Step 3: Build executable runbooks

Runbooks should include:

• prerequisites (access, credentials, approvals)

• roles/responsibilities (“who runs what”)

• failover steps

• failback steps

• validation checks (“how we know we’re back”)

• communications and escalation path

A runbook must be runnable by someone other than the person who wrote it. If it’s not, it’s not operational.

Step 4: Test in phases

Start with what you can execute now and mature over time:

• Tabletop exercise (walkthrough)

• Partial recovery test (restore key components)

• Full failover test (when ready)

Each test should produce a documented result and a backlog of remediation actions.

Step 5: Capture evidence (especially for federal stakeholders)

Treat evidence capture as a deliverable:

• timestamps and test windows

• screenshots/log snippets for key steps

• validation outcomes (pass/fail)

• gaps identified and corrective actions

This turns DR from “we believe it works” into “we can prove it works.”

What good looks like

When leadership asks, “Can we recover?” the right answer is not a guess. The right answer is:

• “Yes—here is our tiered RTO/RPO table, our DR patterns by tier, our runbooks, and the latest test results.”

That’s operational credibility.

Want this implemented with predictable scope and clean handoff?

If you want to right-size DR, build runbooks, and run a test that produces evidence-ready results, start here:
Explore AWS Disaster Recovery Services