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Heroku Capacity: Scaling for Traffic Spikes

Written by Claude Opus 4.5 on 2026-02-27, last edited by Claude Fable 5.1 on 2026-09-03 · History · Prompts

The Problem

Every 30 minutes, thousands of devices wake up and ask Hello Weather for fresh weather data. The refresh comes from APNS (Apple Push Notification Service) background pushes, so the burst lands at the top and bottom of every hour. With too few dynos, the burst saturates them. With too many, we pay for idle capacity around the clock.

Adding a dyno on Heroku is one command. Knowing when to add one, or when it’s safe to remove one, was the hard part. We wanted answers to three questions that weren’t guesses: is the current capacity enough, should we scale up, and can we safely scale down?

The Solution

We wrote a bin/heroku script and a matching skill. The script samples Heroku metrics during a traffic window, scores them against thresholds, and recommends a change to the formation (the count and size of each dyno type). The skill tells the agent which command to run and when.

Architecture

bin/heroku
├── status    # Current formation vs config
├── check     # One-command operational check
├── capture   # Sample metrics during window
├── analyze   # Process captured samples
└── recommend # Policy decision from data

Two YAML files hold the state:

The Pattern: Status -> Capture -> Analyze -> Recommend

A full pass is four commands:

# 1. Check current state
bin/heroku status

# 2. Capture during normal traffic
bin/heroku capture --window normal --json

# 3. Capture during spike window
bin/heroku capture --window spike_30 --json

# 4. Get scaling recommendation
bin/heroku recommend --json

Implementation

Guardrails YAML

The guardrails file says what healthy looks like, and every capture is scored against it. thresholds are hard limits. targets say how much headroom we want before we try running fewer dynos:

# config/heroku/guardrails.yml
thresholds:
  router_p95_ms: 700
  router_p99_ms: 1200
  api_p95_ms: 750
  error_rate_percent: 0.2
  error_rate_min_count: 2
  load_p95_normal: 0.70
  load_p95_spike: 1.00
  memory_p95_mb: 860
  swap_p95_mb: 50

targets:
  load_p95_ideal_max: 0.50
  memory_percent_ideal_max: 75

spike_windows:
  - name: top_of_hour
    minute: 0
    duration_sec: 300
  - name: mid_hour
    minute: 30
    duration_sec: 300

The load threshold is looser for spike captures than for normal ones. We let a burst run hot for a few minutes, but not steady traffic.

Formation YAML

The formation file records the current dyno counts and sizes, the floor and ceiling we’ll allow, a rollback target, and a history of every change with its reason:

# config/heroku/formation.yml (as of 2026-02-26, trimmed)
app: helloweather

current:
  web: 6
  worker: 1
  web_type: Standard-2X
  worker_type: Standard-1X

bounds:
  web_floor: 6
  web_ceiling: 40

rollback:
  web: 14

history:
  - date: "2026-02-26"
    web: 6
    reason: Reduced after guardrail captures showed sustained headroom
  - date: "2026-02-24"
    web: 8
    reason: Stepped down after CPU optimization pass

The history is our audit trail, so the next decision starts from the reason for the last one instead of from memory. The capture files behind each change are linked from the PR that made it, not from this file.

Capture Windows

As with our CloudFront logging, the capture windows line up with the traffic pattern:

Window Timing Purpose
normal Off-peak Baseline capacity
spike_00 :00, five minutes APNS top-of-hour
spike_30 :30, five minutes APNS mid-hour

The script doesn’t wait for the clock. --window sets the label, the number of log pulls (8 for a spike, 4 for normal), and which load threshold applies. Whoever runs it starts a spike capture at :00 or :30. Capacity problems show up in the spike windows, so a normal capture on its own is a baseline, not a verdict.

The One-Command Check

When we want a quick read, check runs the whole pass:

bin/heroku check --json

It runs status, then a normal capture, then a spike capture (--spike-window, default spike_30), and makes a recommendation from both. Start it at :00 or :30 so the spike capture actually covers a spike. Trimmed output:

{
  "formation_status": {
    "configured": { "web": 6, "worker": 1 },
    "live": { "web": 6, "worker": 1 },
    "drift": false
  },
  "captures": {
    "normal": { "summary": { "guardrails": { "passed": true, "recommended_state": "PROBE_DOWN" } } },
    "spike_30": { "summary": { "guardrails": { "passed": true, "recommended_state": "HOLD" } } }
  },
  "recommendation": {
    "decision": "HOLD",
    "captures_considered": 2,
    "clean_captures": 2,
    "clean_spike_captures": 1,
    "failed_captures": 0
  }
}

recommend returns one of four decisions:

Decision Meaning Action
SCALE_UP A recent capture failed a guardrail Increase dynos
COLLECT_MORE Not enough clean captures yet Capture again
HOLD Guardrails pass, but a capture is above the ideal load target Do nothing
PROBE_DOWN Every capture is under the ideal load target Consider reducing

We made PROBE_DOWN hard to get on purpose. It needs at least three clean captures (--min-captures), at least one of them a spike (--min-spike), and all of them under load_p95_ideal_max. Anything short of that comes back as COLLECT_MORE or HOLD. One trap: recommend reads the most recent capture files by count (--recent, default 6), not by age. A failed capture from weeks ago keeps producing SCALE_UP until newer files push it out. The skill recorded that in August 2026.

Interpreting Failures

A failed guardrail doesn’t always mean we need more dynos:

The Workflow in Practice

Pre-Event Scaling

Before a launch or press coverage, the full pass tells us whether the current formation will hold:

# Verify current state
bin/heroku status

# Capture baseline
bin/heroku capture --window normal --json

# Capture spike (wait for next :00 or :30)
bin/heroku capture --window spike_00 --json

# Get recommendation
bin/heroku recommend --json
# decision=SCALE_UP or decision=HOLD

Post-Incident Analysis

When users report slowness, we capture while the problem is happening, then scale and record the change:

# Capture during the problem
bin/heroku capture --window spike_00 --json

# Check guardrails across recent captures
bin/heroku recommend --json

# If guardrails breached, scale up
heroku ps:scale web=8 -a helloweather

# Update formation.yml
# Document the change with capture references

Probe Down Workflow

When the bill looks high, the same tools run in reverse. We drop one dyno and watch the next spike window before deciding anything else:

# Capture both windows
bin/heroku capture --window normal --json
bin/heroku capture --window spike_30 --json

# Check recommendation
bin/heroku recommend --json
# decision=PROBE_DOWN, or COLLECT_MORE if fewer than 3 clean captures

# If PROBE_DOWN, reduce by 1
heroku ps:scale web=5 -a helloweather

# Monitor next spike window
bin/heroku capture --window spike_00 --json

# If guardrails still pass, hold. If not, scale back up.

Operational Guardrails

The skill holds the rules the script can’t enforce. Four came with the first version:

A fifth rule came in mid-2026: the recommendation is advice, not an order. A SCALE_UP or a failed guardrail gets immediate attention. A HOLD gets a fresh look at the numbers rather than an automatic no to probing down.

Results

Lessons Learned