How to Handle Traffic Spikes Before, During, and After a Launch
Learn how to handle traffic spikes with caching, capacity limits, bot controls, useful analytics, load tests, and a practical launch response plan.
Key takeaways
- Cache public responses before they reach the application, then protect dynamic routes according to the cost and risk of each request.
- Measure edge requests, origin work, errors, latency, saturation and completed product actions separately so bots are not mistaken for users.
- Test the real user journey at controlled load, define pass and stop conditions, and rehearse rollback or feature reduction before launch.
- During a spike, preserve the core path first, contain abusive patterns narrowly, and record every intervention for the review afterward.
On this page 19
- TL;DR: how to handle traffic spikes
- Recent note: an HN post exposed an AI bot flood
- How to handle traffic spikes without guessing
- Define the core path and the degradation order
- Set a resource and spend boundary
- Cache the work you do not need to repeat
- Prevent a cache stampede
- Protect dynamic routes by cost and identity
- Decide which bots you want before the launch
- Keep an emergency challenge switch, but know its cost
- Measure people, machines and server load separately
- Protect the database and paid dependencies
- Load test the launch path, not the homepage
- How to handle traffic spikes during the launch
- Talk to the people who showed up
- After the spike: turn evidence into one tested change
- A pre-launch checklist for a bootstrapped maker
- Launch somewhere you can handle, then widen
- Sources
To learn how to handle traffic spikes, prepare the request path before promotion: cache safe public content at the edge, put explicit limits around expensive dynamic work, record enough telemetry to separate people from automation, and rehearse a reduced-service mode. During the spike, protect the product's core action first, make one reversible change at a time, and judge recovery by user outcomes as well as server health.
Disclosure: ZeroRaised is a live launch and discovery site for makers who have never taken institutional VC or accelerator equity. It publishes this guide and does not promise that a launch on any site, including ZeroRaised, will produce a traffic spike, users or revenue.
TL;DR: how to handle traffic spikes#
| Moment | First question | Best first move | Avoid | Evidence to watch |
|---|---|---|---|---|
| Before launch | Which request can be served without application work? | Set correct cache behavior for public HTML and assets, then verify cache hits from outside your account | Assuming a CDN caches every response automatically | Cache status, origin request count and a fresh-user test; Cloudflare documents which response headers and methods are cacheable by default (Cloudflare cache behavior, checked 18 Oct 2026) |
| At the first surge | Is this real use, a crawler, or repeated abuse? | Compare edge traffic, origin logs, user agents, routes and completed product actions | Treating page requests as active users | Request patterns plus product completion; Cloudflare exposes bot-related actions in Security Analytics, while NGINX can write request information to access logs (Cloudflare bot analytics and NGINX logging, checked 18 Oct 2026) |
| When latency rises | Which shared dependency is saturated? | Preserve the core route, shed optional work, and scale only the constrained layer when safe | Adding web workers while database connections are already exhausted | Latency, errors, concurrency, CPU, memory, database connections and provider failures; AWS documents how RDS Proxy pools and controls database connections during surges (Amazon RDS Proxy, checked 18 Oct 2026) |
| When one pattern dominates | Can it be limited without harming normal use? | Apply a narrow route-and-identity limit, log its effect, and keep a rollback condition | A site-wide rule copied from someone else's workload | Allowed, challenged and denied requests plus core-action completion; Cloudflare and Vercel both document configurable rate-limiting rules (Cloudflare rate limiting and Vercel rate limiting, checked 18 Oct 2026) |
| After recovery | What failed first, and what only looked alarming? | Reconstruct the timeline, retain evidence and convert one observed bottleneck into a tested change | Buying capacity without finding the constrained resource | A timestamped incident record and a repeat test; Grafana k6 supports pass-or-fail thresholds for request metrics (k6 thresholds, checked 18 Oct 2026) |
This is an operating framework, not a claim that one stack fits every product. The right control depends on whether you serve static pages, server-rendered pages, authenticated application requests, uploads, webhooks, AI inference, database queries, or some combination of them.
Recent note: an HN post exposed an AI bot flood#
Recent hook, 13 Oct 2026: a Hacker News submission titled “I posted to HN, and then my website was flooded with AI bot traffic” reached 351 points and 186 comments according to the official Hacker News API record. The HN discussion is the source for the public thread, checked 18 Oct 2026.
The linked account came from developer Sammy Straus, who had previously shared Sprinklz, a customizable feed reader, on Hacker News. Straus reported that the site became painfully slow, the slowness continued and worsened several days after the post, and the limited metrics did not match the handful of active, signed-in users. After adding request counts for every endpoint, Straus found that most requests never tried to fetch news articles. Aggregate NGINX logs then showed that nearly half of the traffic came from one service that queried the site many times per minute to summarize changes; the remaining automation included self-identified crawlers and vulnerability scanners probing paths the site did not use. Straus then described blocking the dominant source, adding robots.txt, introducing rate limits and improving the underlying infrastructure (Sammy Straus's post, checked 18 Oct 2026).
That is one named maker's account, not a benchmark for launch traffic or bot share. Its practical value is the diagnostic sequence. User activity, raw requests and server load were different signals. Endpoint-level and web-server evidence made the mismatch visible. The response combined policy, enforcement and performance work rather than assuming that every request represented a potential customer.
The case also shows why robots.txt is not an access-control system. Cloudflare's managed robots.txt documentation says compliance is voluntary and that the file expresses preferences without technically preventing access. A maker who needs enforcement must use an actual security control, such as a firewall, crawler-control product or application authorization (Cloudflare managed robots.txt, checked 18 Oct 2026).
How to handle traffic spikes without guessing#
Start with a request map, not a server-size guess. List what happens between a visitor's click and the first useful result, then mark where work can multiply.
| Request class | Named example | Can it usually be cached? | Scarce resource to protect | Sensible failure behavior |
|---|---|---|---|---|
| Public asset | Logo, stylesheet, product screenshot | Yes, when the content and response headers permit it | Origin bandwidth and request handling | Serve an older safe asset or a simple placeholder |
| Public page | Homepage, documentation, launch page | Often, when it is not personalized and its cache rules permit storage | Rendering compute and database reads | Serve a cached or simplified page |
| Authenticated read | Account page, project list | Usually not as a shared response unless the cache design safely keys identity and authorization | Application concurrency and database reads | Return a clear retry state without exposing another user's data |
| Authenticated write | Sign-up, save, purchase initiation | Do not treat it like a public cacheable response | Database writes, idempotency state and third-party quotas | Reject safely, queue only where semantics allow, and never imply success before it is known |
| Expensive job | Export, import, report generation, model call | Cache the result only when its identity and freshness rules are explicit | Worker time, memory and paid provider quota | Accept the job once, show its status and defer optional work |
| Inbound machine request | Webhook, API client, crawler | Depends on the contract and method | Authentication, request verification, queue depth and downstream writes | Verify, deduplicate where the protocol permits, then return the documented status |
The table is ZeroRaised editorial advice. “Usually” is deliberate. HTTP caching depends on method, headers and cache configuration. For example, Cloudflare says it does not cache a response by default when Cache-Control is private, no-store, no-cache or max-age=0, when Set-Cookie is present, or when the request method is not GET; it does cache when Cache-Control is public with a positive max-age or when Expires is in the future (Cloudflare cache behavior, checked 18 Oct 2026).
Define the core path and the degradation order#
Write the one path that must remain useful. For a self-serve SaaS it might be homepage, account creation, verification, first project and first result. For a public tool it might be landing page, input, processing and download. These are examples of the method, not universal funnels.
Then rank everything else in a degradation order:
- Keep the core read and write path accurate.
- Remove optional recommendations, previews, avatars, animations and background refreshes.
- Delay exports, enrichment, indexing and non-urgent notifications.
- Close or queue the most expensive optional action with an honest message.
- Make the whole product read-only only if writes cannot remain safe.
This order is ZeroRaised editorial advice. It converts “the site is slow” into a choice about product value. A launch page that remains fast while every sign-up fails is not a successful response. A dashboard missing decorative data while users can complete the promised job may be an acceptable temporary mode.
Set a resource and spend boundary#
ZeroRaised editorial advice: Make a list of the resources you intend to watch during launch: web execution, database connections, storage operations, email, payments, maps, search, AI inference and each third-party API. For each, record the current account limit shown in the provider dashboard or documentation, the pressure metric, who can change the limit and the acceptable emergency action.
Autoscaling belongs inside this map. AWS target-tracking policies adjust capacity around a chosen metric and target; Vercel documents automatic function concurrency scaling and a temporary burst mechanism. Neither fact means an application is unbounded, because the request can still reach a database or external provider with a separate constraint (AWS target tracking and Vercel concurrency scaling, checked 18 Oct 2026).
For a bootstrapped team, add a money stop. Decide in advance which usage alert triggers investigation, which optional feature can be disabled, and who may raise a provider limit. This is editorial advice, not a financial benchmark. Review maker perks when infrastructure credits or operating tools are relevant, but design a service that remains understandable when a credit ends.
Cache the work you do not need to repeat#
ZeroRaised editorial advice: Evaluate caching first for public launch traffic. Treat it as a route-by-route decision: each cached response must be safe to share, fresh enough for its purpose and varied on every request property that changes the content.
Start with versioned static assets. Give a changed asset a changed filename or version, set a long browser and edge lifetime appropriate to that versioning scheme, and keep the HTML's freshness separate. Then examine public HTML, documentation, images and public API reads. Cloudflare documents Cache-Control and CDN-Cache-Control behavior, while Vercel documents CDN caching through Cache-Control, CDN-Cache-Control and Vercel-CDN-Cache-Control response headers (Cloudflare cache control and Vercel CDN cache, checked 18 Oct 2026).
Do not cache authenticated or personalized content merely to improve a hit ratio. First prove how the cache key separates authorization, cookies, language, device variants and query parameters that materially change the response. If that proof is awkward, keep the response private and optimize the underlying read instead. This is ZeroRaised editorial advice based on the risk of serving one user's response to another.
Test the cache from outside an authenticated browser:
- Request the same public URL twice and inspect the provider's cache-status response header.
- Confirm that the second request avoids the origin or renderer in the platform telemetry.
- Change the source content and verify the intended purge or expiry path.
- Test a query string, cookie and language variant that should change the result.
- Confirm that an authenticated response never appears to a signed-out request.
These are editorial test steps. Header names and cache states vary by platform, so use the selected provider's current cache documentation. The goal is evidence that the intended response is reused, not simply evidence that a CDN hostname exists.
Prevent a cache stampede#
When a popular cached object expires, many simultaneous requests can try to regenerate it. Use stale-while-revalidate or an equivalent single-flight mechanism where the content can safely be served stale for a bounded period. The HTTP stale-while-revalidate directive allows a cache to reuse a stale response while revalidating it asynchronously, and Cloudflare documents its implementation and request-collapsing behavior (RFC 5861 and Cloudflare revalidation, checked 18 Oct 2026).
If the page cannot be stale, serialize regeneration or precompute it before the launch. This is editorial advice. The design aim is to prevent a cache expiry from turning one required refresh into many identical database queries or renders.
Protect dynamic routes by cost and identity#
A single global request limit is easy to configure and difficult to justify. A homepage GET, a password attempt, a search, a file conversion and a paid model call impose different costs and risks. Put controls closest to the expensive or abuse-prone action.
| Control | Good fit | Important limitation | Evidence after enabling |
|---|---|---|---|
| Per-IP rate limit | Anonymous scraping, repeated probes and simple public abuse patterns | Shared networks and address rotation can make IP an imperfect identity | Requests allowed, challenged or denied by path and source |
| Per-account or API-key limit | Authenticated API and expensive user actions | Requires trustworthy authentication before counting | Limit events, affected accounts and completion of legitimate work |
| Concurrency limit | Slow exports, model calls and work that holds scarce resources | A queue can still grow without a maximum | Active work, queue age, queue depth and rejection count |
| Request-size limit | Uploads and request bodies with processing cost | Does not control many small requests | Rejected bytes and valid upload completion |
| Bot challenge or block | Automated traffic that matches a defensible signal | False positives can exclude people or useful crawlers | Challenge solve rate, blocked patterns and core-action completion |
robots.txt | Communicating crawl preferences to compliant crawlers | Voluntary, so it does not enforce access | Requests by declared crawler and compliance over time |
The recommendations are ZeroRaised editorial advice. Product mechanics are documented by Cloudflare, which supports rate-limiting expressions and characteristics, and Vercel, which supports rate-limit rules with configurable conditions and keys. Vercel also notes that its counters are tracked per region, so traffic matching a key across regions can exceed a single-region threshold (Cloudflare rate-limiting rules and Vercel WAF rate limiting, checked 18 Oct 2026).
Choose the response intentionally. A challenge may be suitable for suspicious browser traffic. An API should return the status and retry information its contract defines. HTTP 429 Too Many Requests indicates rate limiting, and a response may include Retry-After to tell a client how long to wait (RFC 6585, section 4 and HTTP Retry-After, checked 18 Oct 2026).
Never let the user repeat a charge or create duplicate work because the first response timed out. Stripe recommends idempotency keys on POST requests and stores the first result for a key so a retried request does not repeat the operation; its documentation also explains key reuse and parameter checks (Stripe idempotent requests, checked 18 Oct 2026). Apply the equivalent mechanism offered by the service you actually use, and test your own write path rather than assuming every dependency behaves like Stripe.
Decide which bots you want before the launch#
A launch link can attract search crawlers, AI crawlers, link-preview fetchers, uptime checkers, feed readers, scrapers and vulnerability scanners alongside people. Some of that automation is useful. Some only costs money. The mistake is deciding under pressure, when every unfamiliar user agent looks like an attacker and every blocked request feels like a lost visitor.
Write a short bot policy before you post anywhere:
| Automation type | Named example | Usual reason to allow it | Usual control | Source |
|---|---|---|---|---|
| Search crawler | Googlebot | Pages can be discovered through Google Search | Allow in robots.txt; verify identity before trusting the user agent | Google crawlers overview, checked 18 Oct 2026 |
| AI search crawler | OAI-SearchBot | Pages can appear in ChatGPT search results | Separate robots.txt rule from training crawlers | OpenAI crawlers, checked 18 Oct 2026 |
| AI training crawler | GPTBot | Optional; a business decision, not an operational need | Disallow in robots.txt if you do not want content used for training | OpenAI crawlers, checked 18 Oct 2026 |
| Unidentified high-frequency fetcher | The change-monitoring service in the Straus account | Rarely worth its cost if it dominates traffic | Rate limit or block at the edge | Sammy Straus's post, checked 18 Oct 2026 |
| Vulnerability scanner | Requests for WordPress paths on a site that is not WordPress, as Straus observed | None for you | Return a cheap 404, block repeated probes and confirm nothing sensitive is exposed | Sammy Straus's post, checked 18 Oct 2026 |
OpenAI documents that OAI-SearchBot and GPTBot are controlled independently, so a site can allow search visibility while disallowing training use, and it notes that a robots.txt change can take about 24 hours to affect search results (OpenAI crawlers, checked 18 Oct 2026). Google says its crawlers can be identified by user agent, source IP address and reverse DNS hostname, which matters because any client can claim to be Googlebot in a header (Google crawlers overview, checked 18 Oct 2026).
Remember the limit of the file itself. The Robots Exclusion Protocol standard states that its rules "are not a form of access authorization" (RFC 9309, checked 18 Oct 2026). Use robots.txt to state preferences for crawlers that honor it, then use edge controls for the rest. Cloudflare's AI Crawl Control documentation describes features to analyze AI traffic and manage AI crawlers, and Cloudflare's bot documentation covers broader bot management products (Cloudflare AI Crawl Control and Cloudflare bots, checked 18 Oct 2026). Choose the tool your host supports rather than adding a second proxy on launch day.
Keep an emergency challenge switch, but know its cost#
Both large edge providers offer a site-wide challenge mode for attacks. Cloudflare's Under Attack mode shows an interstitial challenge and requires JavaScript, and Cloudflare warns that this can affect third-party analytics tools (Cloudflare Under Attack mode, checked 18 Oct 2026). Vercel's Attack Mode makes visitors complete a security challenge while known bots such as search engines and webhook providers are allowed through, and Vercel says requests blocked by Attack Mode do not count towards usage limits (Vercel Attack Mode, checked 18 Oct 2026).
That makes a global challenge a last resort for a launch, not a first response. It adds friction for every real visitor at the moment you most want a clean first impression, and API clients that cannot run a browser challenge may fail. Know where the switch is, who can flip it, and which narrower rule you would try first. This is ZeroRaised editorial advice.
Measure people, machines and server load separately#
The Straus account turned on one observation: active users, request volume and server load told different stories. Your launch dashboard should make that difference visible before anyone posts the link.
Google's SRE book names four golden signals for monitoring a user-facing system: latency, traffic, errors and saturation (Google SRE, Monitoring Distributed Systems, checked 18 Oct 2026). Those are a solid base. For a launch, add two product signals and one source split.
| Signal | What it answers | Where it usually comes from | Common misreading |
|---|---|---|---|
| Edge requests by route | How much traffic reached your domain, and where | CDN or host analytics | Counting requests as visitors |
| Cache status by route | How much of that traffic avoided your application | CDN response headers or analytics | Assuming a CDN in front means a cache hit |
| Origin requests and latency | How much work your application actually did, and how fast | Application or web-server logs | Reading only the average and missing the slow tail |
| Errors by route and status | Which path is failing and how | Logs, error tracking, host function logs | Treating 404s from scanners as a product failure |
| Saturation | Which resource is close to its limit | Database connections, CPU, memory, queue depth, provider quotas | Watching web CPU while the database pool is full |
| Core-action completions | Whether real people are getting the promised result | Product events you define | Using page views as a proxy for success |
| Traffic by declared agent and source | How much load is automation, and which kind | Edge bot analytics or web-server access logs | Trusting a user-agent string without verification |
The first five rows map to the golden signals; the last two are ZeroRaised editorial additions for launches. NGINX documents configurable access logs that record request information, which is enough to count requests by path and user agent if you have no edge analytics (NGINX logging, checked 18 Oct 2026). Cloudflare surfaces bot actions in its Security Analytics view for Super Bot Fight Mode (Cloudflare bot analytics, checked 18 Oct 2026).
Two practical notes. First, a client-side analytics script only counts clients that run JavaScript, and Straus found that most requests to the single-page app never ran the JavaScript needed to render the page (Sammy Straus's post, checked 18 Oct 2026). Browser analytics and server logs will therefore disagree, and both can be accurate. Second, decide your alert thresholds before launch. A number chosen during an incident tends to be chosen to justify the action you already want to take.
Protect the database and paid dependencies#
Check database and downstream-service constraints separately before launch. The provider-specific connection and retry behavior below is sourced; the protective moves in the table are ZeroRaised editorial advice.
| Dependency | What runs out first | Protective move | Source |
|---|---|---|---|
| Postgres from serverless functions | Connections, because each short-lived function can open its own | Use a transaction-mode pooler | Supabase connection guide, checked 18 Oct 2026 |
| Amazon RDS or Aurora | Connections during surges | Put RDS Proxy in front to pool and share connections | Amazon RDS Proxy, checked 18 Oct 2026 |
| Payment API | Duplicate operations on retry | Send idempotency keys on create requests | Stripe idempotent requests, checked 18 Oct 2026 |
| Any downstream service | Capacity under synchronized retries | Randomized exponential backoff and a retry limit | Google SRE, Addressing Cascading Failures, checked 18 Oct 2026 |
Supabase's connection guide recommends its shared pooler in transaction mode for serverless or edge functions because those environments open many short-lived connections (Supabase connection guide, checked 18 Oct 2026). AWS says RDS Proxy can help handle unpredictable surges in database traffic by keeping a connection pool and reusing its connections (Amazon RDS Proxy, checked 18 Oct 2026). Use the equivalent for your database host.
Retries deserve the same attention. Google's SRE book advises always using randomized exponential backoff when scheduling retries and limiting retries per request, because evenly timed retries can amplify a small disruption into a larger one (Google SRE, Addressing Cascading Failures, checked 18 Oct 2026). Check your own client code, background workers and any SDK defaults. A launch is a bad time to discover that a frontend retries a failing request in a tight loop.
ZeroRaised editorial advice: Consider moving optional work such as welcome emails, analytics enrichment, thumbnail generation and internal webhooks off the synchronous request path. If you use a queue, give it a bounded size, monitor its age and test its failure mode before launch.
Load test the launch path, not the homepage#
A spike test is the right shape for launch preparation. Grafana's k6 documentation defines a spike test as one that verifies whether a system survives and performs under sudden and massive rushes of utilization, and lists product launches among the events it suits (k6 spike testing, checked 18 Oct 2026). The same documentation describes other test types, such as smoke, average-load and stress tests, which are worth running first so a spike result is interpretable (k6 test types, checked 18 Oct 2026).
A practical sequence for a small team:
- Smoke test the core path with very low load to confirm the script and environment work.
- Script the real journey, including the cached homepage, the sign-up or first action, and any expensive request a curious visitor would trigger.
- Set thresholds for error rate and latency before running, so the test passes or fails on criteria you chose in advance. k6 supports pass-or-fail thresholds on request metrics (k6 thresholds, checked 18 Oct 2026).
- Run a spike that ramps quickly, holds briefly and drops, then watch saturation on every row of your dependency table.
- Fix the first bottleneck only, rerun, and record the result.
The steps are ZeroRaised editorial advice. Pick test levels from your own expectations and budget, not from another product's launch, because there is no reliable way to predict how much traffic a post will bring. Run the test against a staging environment that shares production's configuration, or against production in a quiet window if your providers' terms allow load testing and you have warned anyone who shares the infrastructure. Exclude test traffic from product analytics, and do not load test third-party APIs you do not own; stub them or use their test modes.
How to handle traffic spikes during the launch#
When traffic arrives, the job changes from building to operating. Google's SRE book describes separate incident roles: an incident commander who holds the overall state, an operations lead who changes the system, and a communication lead who issues updates (Google SRE, Managing Incidents, checked 18 Oct 2026). A solo maker cannot staff three roles, but can separate the modes: decide, then change, then communicate. If a co-founder or friend is available, give one person the system and the other the comments, inbox and status updates.
Use a symptom table so you are not inventing diagnosis on the spot:
| Symptom | Likely place to look | First reversible move |
|---|---|---|
| Pages slow, origin requests high, cache hits low | Cache configuration on public routes | Fix cache headers or rules for public pages; avoid caching personalized responses |
| Requests high, core-action completions flat | Automation, a single client, or a broken step in the funnel | Group requests by path and agent; test the core path yourself in a clean session |
| One agent or IP range dominates requests | A crawler, monitor or scraper | Narrow rate limit or block for that pattern, then confirm completions are unaffected |
| Database connection errors | Pool exhaustion from scaled-out functions | Route through a pooler, reduce per-instance connections, shed optional queries |
| Errors from a payment, email or AI provider | Provider limits or an outage | Pause the dependent optional feature; queue or show an honest retry message |
| Usage bill climbing faster than completions | Expensive work driven by machines | Rate limit the expensive route by identity; cap concurrency |
| Everything failing, nothing else works | Possible attack or total overload | Emergency challenge mode or static fallback page while you investigate |
The table is ZeroRaised editorial guidance. It follows the overload principle in Google's SRE book: when part of a system is overloaded, serving degraded responses that are cheaper to compute can be better than serving errors, and under extreme overload errors may be the only option left (Google SRE, Handling Overload, checked 18 Oct 2026). Your degradation order from earlier is that principle applied to one product.
Make one change at a time, write down the time and the reason, and watch the core-action signal for a few minutes before the next change. Stacking three fixes at once makes it impossible to know which one helped, and harder to undo the one that hurt.
Talk to the people who showed up#
ZeroRaised editorial advice: If you posted on a community site, use the thread to report the outage and answer people who mention it. A short, factual reply ("The sign-up step is failing under load; I have paused image uploads and am working on the database connection limit") is more useful than silence or a vague apology. Add a simple status line to the page if you can do so safely, and do not promise a recovery time you cannot meet. The Show HN writing guide and Product Hunt launch guide cover the maker's role in those threads in more detail.
After the spike: turn evidence into one tested change#
Once traffic settles, capture the evidence promptly. Export the request counts by route and agent, the error and latency graphs, the provider bills or usage pages, and your own timeline of interventions.
Google's SRE book frames the review as a blameless postmortem that focuses on contributing causes rather than on blaming a person, and recommends defining in advance what triggers one (Google SRE, Postmortem Culture, checked 18 Oct 2026). For a solo maker, "blameless" mostly means being honest with yourself: write what you knew at the time, not what you know now.
A short review template:
| Question | Example answer format |
|---|---|
| What did users experience, and for how long? | Which path failed, from when to when, by your own timestamps |
| What resource ran out first? | The first saturated row in your dependency table |
| What was traffic made of? | Share of requests by declared agent and route, from your logs, without guessing beyond them |
| Which intervention helped, and which did nothing? | Your timeline, with the signal you watched after each change |
| What one change prevents the same first failure? | A specific fix plus the test that proves it |
| What did the launch cost? | Provider usage for the period, compared with your money stop |
The template is ZeroRaised editorial advice. Resist a long list of improvements. Straus's own follow-up was a single priority, making the site faster, after the immediate blocks and limits were in place (Sammy Straus's post, checked 18 Oct 2026). One verified fix beats ten intentions.
Also check whether the traffic was worth keeping. If the post brought crawlers and scanners but few completions, the lesson may be about the launch venue or page, not infrastructure. The first 100 users guide covers direct channels that bring fewer visitors and more relevant ones.
A pre-launch checklist for a bootstrapped maker#
Use this the week before you post. Every row should be a "yes" or a deliberate "not needed."
| Area | Check |
|---|---|
| Request map | Core path and degradation order are written down |
| Cache | Public pages and assets return cache hits from a signed-out client; personalized pages do not |
| Bots | robots.txt reflects your crawler policy; you know how to rate limit or block a single agent at the edge |
| Limits | Expensive and abuse-prone routes have identity-based limits with defined responses |
| Database | Connections go through a pooler suited to your runtime |
| Writes | Payment and other create requests are idempotent and retries back off |
| Telemetry | You can see requests by route and agent, cache status, errors, saturation and core-action completions |
| Load test | A spike test of the real journey passed your thresholds after the last code change |
| Money stop | Usage alerts are set and you know which feature you would disable first |
| Recovery | A known-good deployment and an emergency challenge switch are one action away |
| People | Someone watches the system and someone answers the thread, even if it is the same person in turns |
The checklist is ZeroRaised editorial advice and complements the broader product launch checklist, which covers pages, messaging and launch-day support. If infrastructure spend is the worry, the maker perks page and the free startup perks guide list tools and credits available without a venture round.
Launch somewhere you can handle, then widen#
ZeroRaised editorial advice: Consider sequencing launches from smaller, relevant venues to larger ones so you can test caching, limits and dashboards at lower stakes before a sudden rush. Use what you observe to identify expensive routes and noisy crawlers before widening distribution.
ZeroRaised is one such step for makers who have never taken institutional VC or accelerator equity. Its submission page says submitting is free and asks for a public product URL, then the maker confirms the details (ZeroRaised submission page, checked 18 Oct 2026). Browse the home board to see how launches are presented, then submit your product for free when the checklist above is green. ZeroRaised makes no promise about how much traffic a listing will bring, which is exactly why the preparation in this guide is worth doing before every launch, not only the big one.
The short version of how to handle traffic spikes stays the same at any scale: cache what you can, limit what is expensive, know which visitors are machines, protect the core action, change one thing at a time, and turn what you learned into one tested fix.
Sources#
- Hacker News API record, checked 18 Oct 2026
- Hacker News discussion, checked 18 Oct 2026
- Sammy Straus's account, checked 18 Oct 2026
- Cloudflare default cache behavior, checked 18 Oct 2026
- Cloudflare cache control, checked 18 Oct 2026
- Cloudflare revalidation, checked 18 Oct 2026
- Cloudflare rate limiting, checked 18 Oct 2026
- Cloudflare bot analytics, checked 18 Oct 2026
- Cloudflare managed robots.txt, checked 18 Oct 2026
- Cloudflare AI Crawl Control, checked 18 Oct 2026
- Cloudflare bots, checked 18 Oct 2026
- Cloudflare Under Attack mode, checked 18 Oct 2026
- Google crawlers overview, checked 18 Oct 2026
- OpenAI crawlers, checked 18 Oct 2026
- AWS target tracking, checked 18 Oct 2026
- Amazon RDS Proxy, checked 18 Oct 2026
- NGINX logging, checked 18 Oct 2026
- Stripe idempotent requests, checked 18 Oct 2026
- k6 test types, checked 18 Oct 2026
- k6 spike testing, checked 18 Oct 2026
- k6 thresholds, checked 18 Oct 2026
- Google SRE, Addressing Cascading Failures, checked 18 Oct 2026
- Google SRE, Handling Overload, checked 18 Oct 2026
- Google SRE, Managing Incidents, checked 18 Oct 2026
- Google SRE, Monitoring Distributed Systems, checked 18 Oct 2026
- Google SRE, Postmortem Culture, checked 18 Oct 2026
- Supabase connection guide, checked 18 Oct 2026
- Vercel CDN cache, checked 18 Oct 2026
- Vercel concurrency scaling, checked 18 Oct 2026
- Vercel Attack Mode, checked 18 Oct 2026
- Vercel WAF rate limiting, checked 18 Oct 2026
- RFC 5861, checked 18 Oct 2026
- RFC 6585, checked 18 Oct 2026
- RFC 9110, checked 18 Oct 2026
- RFC 9309, checked 18 Oct 2026
Frequently asked questions
What is the fastest way to handle a sudden traffic spike?
Identify whether the pressure is at the edge, application, database or a third-party service; cache safe public responses; rate-limit expensive abuse patterns; and preserve the smallest useful user journey while you investigate.
Does autoscaling solve traffic spikes?
Autoscaling can add compute when its signal and limits fit the workload, but it does not make a database, payment provider or other constrained dependency scale with it.
Should I block all bots before a launch?
No. Decide which crawlers provide value, express preferences in robots.txt, and use enforceable edge controls for traffic that ignores those preferences or imposes unacceptable cost.
What should I monitor during a launch?
Watch request volume, cache status, error rate, latency, application concurrency, database pressure, third-party failures and completion of the product's core action, segmented by route and traffic source.
How should a bootstrapped maker prepare for launch traffic?
Set a spend boundary, test the production path, cache safe content, protect expensive endpoints, prepare a reduced-service mode, and assign one person to operations while another handles users when possible.