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Implement email rate limiting and volume controls that protect sender reputation. Use when setting up send throttling, handling ISP deferrals, configuring per-domain limits, or debugging 421 errors and throttled delivery.
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基于 SOC 职业分类
Diagnose email problems and plan email infrastructure. Use when emails land in spam, bounce rates spike, deliverability drops, open rates decline, providers throttle sending, you need to set up email for a new product, or you're not sure which email skill to use.
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Build dashboards, alerts, and monitoring systems for email sending operations. Use when setting up deliverability monitoring, configuring alert thresholds, checking blocklists, building email metrics dashboards, or responding to deliverability incidents.
Classify inbound email replies by intent and route them appropriately. Use when building reply handling for outreach, support, or transactional email - detecting interested, not interested, OOO, bounce, unsubscribe, forwarded, and question replies.
Maintain email conversation context across messages using threading headers. Use when building thread reconstruction, linking replies to conversations, detecting thread hijacking, stripping quoted content, or providing thread context to AI agents.
Receive, parse, and process incoming email via provider webhooks. Use when setting up inbound email handling, parsing MIME messages, extracting content from replies, detecting threads, filtering spam on inbound, or routing incoming messages.
| name | rate-limiting |
| description | Implement email rate limiting and volume controls that protect sender reputation. Use when setting up send throttling, handling ISP deferrals, configuring per-domain limits, or debugging 421 errors and throttled delivery. |
| license | MIT |
Volume controls that protect your sender reputation from runaway sends, ISP throttling, and agent-driven spikes.
bounce-handling - processing the soft bounces that result from ISP throttlingemail-warmup - ramping volume on new domains/IPs (rate limiting during warmup is critical)sender-reputation - understanding the reputation signals that rate limiting protectssender-monitoring - dashboards and alerts for catching rate limit issues earlysuppression-lists - managing bounces and complaints that compound rate limit problemsSender reputation does not degrade linearly. It cliff-drops. A sudden spike in outbound volume - whether from a bug, a batch job, or an AI agent stuck in a retry loop - triggers automated defenses at mailbox providers that can take weeks to recover from. During that recovery window, even your legitimate human-sent email lands in spam.
Gmail, Microsoft, and Yahoo track engagement per sender domain across rolling time windows. A sudden volume spike with no corresponding engagement history reads as a spam blast, regardless of your intent. The sequence is predictable: soft filtering first (messages land in spam), then hard reputation damage (messages get rejected), then potential blocklisting.
Rate limiting is the infrastructure that prevents this. It converts an unbounded sending surface into a predictable, reputation-safe operating envelope.
Effective email rate limiting requires enforcement across multiple time horizons. A single "messages per hour" limit is not enough because different time windows protect against different failure modes.
Hourly limits are your first defense against bugs and accidents. A loop that escapes, a retry function that doubles back on itself, a batch job that runs twice - these all show up as hourly spikes before they surface anywhere else.
A well-calibrated hourly limit should never trigger during expected usage. It should only activate when something has gone wrong. Think of it as a circuit breaker, not a throttle.
Typical ranges:
Mailbox providers track daily volume per domain against engagement signals (opens, replies, clicks, complaints). A day where you send 10x your normal volume looks suspicious even if each individual message is legitimate.
Daily limits also give you a predictable cost ceiling. AI agents that trigger sends in response to events can generate surprising volume if the event stream is unexpectedly large. A daily cap converts an unpredictable variable cost into a bounded one.
Typical ranges:
Monthly limits align your sending behavior with your plan capacity. Running out of monthly quota on the 15th is a product problem that needs attention - you either need to upgrade or throttle outbound activity for the rest of the period.
Monthly limits also protect against slow-burn problems that stay under daily limits but accumulate over weeks.
Per-minute limits are the tightest control. They prevent short bursts that can overwhelm a receiving server's connection pool or trigger immediate throttling. Most useful for:
The simplest production-ready approach. Track send counts in time-bucketed counters and check them atomically before each send.
-- Atomic increment-and-check pattern
-- Returns the new counter value so you can compare against the limit
INSERT INTO send_counters(tenant_id, window_key, counter)
VALUES($1, $2, 1)
ON CONFLICT (tenant_id, window_key)
DO UPDATE SET counter = send_counters.counter + 1
RETURNING counter;
Window keys encode both the time window and its granularity:
hourly:2025-03-15T14 -- hourly window
daily:2025-03-15 -- daily window
monthly:2025-03 -- monthly window
This approach is simple, durable (survives restarts), and works with any SQL database. The tradeoff is a database write per send, which is acceptable for most volumes.
Rollback on rejection: If a send is blocked by a downstream limit (e.g., it passes the hourly check but fails the daily check), decrement the hourly counter so you don't "use up" capacity on blocked sends:
UPDATE send_counters
SET counter = GREATEST(counter - 1, 0)
WHERE tenant_id = $1 AND window_key = $2;
Token buckets are better for high-volume systems where you need smooth rate control rather than hard cutoffs. A bucket refills at a steady rate (e.g., 10 tokens/second) and each send consumes one token. Short bursts are allowed (up to the bucket size) but sustained throughput is capped at the refill rate.
Use Redis for token bucket implementations - the atomic operations (DECR, EXPIRE) map naturally to the algorithm:
BUCKET_KEY = "ratelimit:{tenant_id}:sends"
MAX_TOKENS = 100 # burst capacity
REFILL_RATE = 10 # tokens per second
REFILL_INTERVAL = 1 # seconds
-- Pseudocode: check and consume
tokens = GET BUCKET_KEY
if tokens > 0:
DECR BUCKET_KEY
allow_send()
else:
reject_send("rate_limited")
Token buckets are ideal when you want to allow brief spikes (a batch of 50 sends) without blocking, while still enforcing an average rate over time.
Fixed window (e.g., "1,000 sends between 2:00 and 3:00") is simpler but has a boundary problem: 999 sends at 2:59 and 999 sends at 3:01 yields 1,998 sends in 2 minutes while staying under the 1,000/hour limit for both windows.
Sliding window avoids this by checking the count over a rolling period. More accurate but more expensive to compute. For most email use cases, fixed windows with conservative limits are sufficient - the boundary problem is a theoretical concern that rarely matters in practice because ISPs use fuzzy heuristics, not exact counters.
Account-level rate limits protect your overall reputation. Per-domain throttling protects your relationship with specific receiving mail servers.
If your agent sends to 200 contacts at bigcorp.com in 10 minutes, that corporate mail server sees a sudden blast from your domain. Even if every message is legitimate, many corporate mail servers will:
The soft bounces that result from these deferrals accumulate as negative signals, even though the messages were legitimate.
Set limits per sender domain at minute, hour, and day granularity:
domain: outreach.example.com
max_per_minute: 5
max_per_hour: 50
max_per_day: 200
These limits are checked independently from your account-level limits. A send can pass your hourly account limit but still be blocked by a per-domain minute limit.
Counter keys for per-domain limits use the domain ID and time window:
domain_minute:{domain_id}:2025-03-15T14:30
domain_hour:{domain_id}:2025-03-15T14
domain_day:{domain_id}:2025-03-15
Beyond throttling your own sending domains, also consider throttling per recipient domain. If you're sending to many recipients at gmail.com, that's different from sending to many recipients across diverse domains. Gmail has published guidelines, but most corporate mail servers have lower, unpublished thresholds.
A conservative default: no more than 50-100 messages per hour to any single recipient domain from a single sending domain, unless you have established sending history with that domain.
When a mailbox provider throttles you, they don't reject your email outright. They defer it with a 421 SMTP response code, which means "try again later." Understanding these responses is critical for building correct retry logic.
| Code | Provider | Meaning |
|---|---|---|
421-4.7.28 | Gmail | Unusual rate of unsolicited mail from your IP or domain |
421-4.7.26 | Gmail | Unauthenticated mail (missing SPF/DKIM) is being rate-limited |
421 4.7.0 | Gmail | Temporary deferral due to reputation, rate, or policy concerns |
421 4.3.2 | Microsoft | Service not available, too many concurrent connections |
421 TS03 | Yahoo | Too many concurrent SMTP connections from your IP |
A 421 is a warning shot, not a permanent block. But a consistent pattern of 421s escalates to worse outcomes:
The transition from stage 1 to stage 2 is often invisible - you stop getting 421s because messages are "accepted" but routed to spam. Monitor inbox placement, not just delivery status.
Do: Back off. Reduce sending rate immediately. Wait the suggested interval if a Retry-After value is provided.
Don't: Retry at the same rate. The 421 is telling you to slow down - retrying at full speed makes the problem worse and accelerates the transition to hard blocks.
Exponential backoff with jitter is the standard pattern:
retry_delay = min(base_delay * 2^attempt + random_jitter, max_delay)
-- Example progression:
-- Attempt 1: ~2 minutes
-- Attempt 2: ~4 minutes
-- Attempt 3: ~8 minutes
-- Attempt 4: ~16 minutes
-- Max: 1 hour
Every email service provider (ESP) enforces their own rate limits on top of whatever you configure. Know these limits before you hit them.
Throttling error. SES does not retry for you - your application must catch and retry with backoffX-Ratelimit-Limit, X-Ratelimit-Remaining, X-Ratelimit-Reset (not returned on mail/send)Retry-After headerX-Ratelimit-Limit, X-Ratelimit-Remaining, X-Ratelimit-ResetAI agents introduce failure modes that human senders don't. They don't warm up, they don't space out follow-ups naturally, and when something goes wrong - a runaway retry loop, a misread condition, a test hitting production - the volume spike is instant and steep.
The most common AI agent over-send scenario: a batch of emails soft-bounces, the agent interprets "delivery failed" as "try again," and retries 200 times per recipient across 150 recipients in under an hour. 30,000 sends from a domain that normally does 200 a day.
Prevention: Rate limits alone don't catch this. You also need:
When a send is blocked by a rate limit, return a structured response the agent can act on - not just an error code:
{
"status": "blocked",
"reason": "hourly_limit_exceeded",
"hourly": {
"used": 1000,
"limit": 1000,
"remaining": 0
}
}
The reason field matters for retry logic. If the reason is hourly_limit_exceeded, the agent should wait for the hourly window to reset. If it's daily_limit_exceeded, waiting until the top of the next hour accomplishes nothing. If it's monthly_limit_exceeded, the agent needs operator attention.
Before running a large batch send, check current usage and compare remaining capacity to planned volume:
GET /v1/me/usage
{
"monthly": { "used": 2341, "limit": 3000, "remaining": 659 },
"daily": { "used": 187, "limit": 500, "remaining": 313 },
"hourly": { "used": 23, "limit": 75, "remaining": 52 }
}
If your batch has 400 recipients but your daily remaining is 313, you know upfront that not all sends will succeed. Plan accordingly rather than sending until you get blocked.
Some platforms (including molted.email) offer simulation endpoints that evaluate all policy rules - rate limits, cooldowns, suppression lists, negative signal budgets - and return the decision without actually sending. Use these to dry-run a batch before committing.
Automated notifications when approaching or exceeding limits prevent surprises:
Deduplicate these notifications per billing period - sending the same "you're at 80%" alert every hour is counterproductive.
The most common mistake. Generous limits feel safe during development but provide no protection in production. A 100,000/hour limit doesn't protect you from anything - no legitimate use case needs that rate, and by the time you hit it, your reputation is already destroyed. Set limits based on your actual expected volume plus a reasonable buffer (2-3x), not on what your infrastructure can theoretically handle.
A 421 from Gmail because your domain is new is different from a 421 because you're on a blocklist. The error message string matters. Parse it and route to different retry strategies.
If your retry logic bypasses rate limits, a burst of soft bounces generates a burst of retries that bypasses the very protection you built. Retries must count against the same rate limit windows as original sends.
Account-level limits of 1,000/hour feel safe until your agent sends 500 of those to a single corporate domain in 10 minutes. The receiving server throttles you, generating soft bounces that hurt your reputation. Always add per-domain limits on top of account-level limits.
A new domain with a 10,000/day rate limit will still get throttled by ISPs if it jumps from 0 to 5,000 in day one. Rate limits are a ceiling, not a target. During warmup, your actual sending should ramp gradually regardless of what your limits allow.
When a rate limit is hit, some systems silently queue the excess sends for later delivery. This hides the signal that something is wrong. A loop that sends 50,000 messages doesn't trigger any alert - the queue just grows. Prefer explicit blocking with a reason code over silent queueing, especially for AI agent integrations where the agent needs to understand and react to the constraint.
A single "sends per hour" limit misses slow-burn problems (gradually increasing daily volume) and doesn't protect against monthly budget overruns. Use at least hourly and daily windows. Monthly is important for billing alignment.
If you increment an hourly counter, then the send fails a daily limit check, the hourly counter is now inflated. Roll back all counters that were incremented when a downstream check fails, or you'll "use up" rate limit capacity on sends that never went out.