← Glossary / Time to First Byte (TTFB)

What is Time to First Byte (TTFB)?

Time to First Byte (TTFB) is the duration from the start of an HTTP request to the moment the client receives the first byte of the response payload. In scraping pipelines, it is the primary indicator of target server health, proxy latency, and anti-bot processing overhead. High TTFB doesn't just slow down extraction—it causes connection timeouts, inflates proxy billing, and cascades into pipeline failure when concurrency limits are exhausted waiting for idle sockets.

LatencyProxy OverheadNetwork LayerConcurrencyAnti-Bot Delay
// 02 — definitions

The waiting
game.

TTFB measures the dead air between asking for data and actually getting it. It dictates how many concurrent connections your pipeline needs to maintain throughput.

Ask a DataFlirt engineer →

TL;DR

TTFB is the sum of DNS resolution, TCP handshake, TLS negotiation, proxy routing, and server processing time. For scrapers, a sudden spike in TTFB is usually the first symptom of a shadow ban or an anti-bot challenge being computed on the edge. Monitoring TTFB per target is critical for tuning timeout thresholds and worker concurrency.

01Definition & structure
Time to First Byte (TTFB) is the fundamental latency metric of the web. It measures the time elapsed between the client sending an HTTP request and receiving the first byte of data from the server. It is composed of several distinct phases: DNS resolution, TCP connection setup, TLS negotiation, proxy routing delays, and the actual time the server spends processing the request (e.g., running database queries or anti-bot checks).
02How it impacts pipeline concurrency
TTFB dictates your infrastructure requirements. If your target requires 100 requests per second, and the TTFB is 100ms, you only need 10 concurrent connections. If the TTFB jumps to 2000ms due to proxy routing or slow database queries, you suddenly need 200 concurrent connections to maintain the same throughput. High TTFB forces you to scale horizontally just to wait for data.
03The anti-bot tarpit effect
Modern anti-bot vendors use TTFB as a weapon. When a request looks suspicious but doesn't cross the threshold for an outright block, the edge WAF may intentionally delay the response. This "tarpitting" ties up your scraper's sockets, exhausts your connection pool, and causes cascading timeout errors. Monitoring TTFB variance is often the fastest way to detect a shadow ban.
04How DataFlirt handles it
We treat TTFB as a primary health signal. Our telemetry tracks TTFB per target, per proxy ASN, and per endpoint. We use aggressive connection pooling and HTTP/2 multiplexing to eliminate TCP/TLS handshake overhead. When we detect sustained TTFB spikes on a specific target, our scheduler automatically adjusts worker concurrency and timeout thresholds to prevent socket exhaustion without dropping throughput.
05The residential proxy tax
Many engineers blame the target server for slow scraping, but the culprit is often the proxy. Residential proxies route your traffic through consumer devices (laptops, phones) on standard ISP connections. This extra hop can easily add 400–800ms to your TTFB. If speed is critical and the target's anti-bot posture allows it, switching to high-quality ISP or datacenter proxies is the fastest way to slash TTFB.
// 03 — latency math

Where the
milliseconds go.

TTFB is a composite metric. DataFlirt's telemetry breaks it down to isolate whether a slowdown is caused by our proxy routing, the target's origin server, or an edge WAF inspecting the request.

Total TTFB = tDNS + tTCP + tTLS + tProxy + tServer
The sum of all network and compute delays before the payload begins. Standard HTTP telemetry
Required Concurrency = (Target_RPS × TTFBms) / 1000
Little's Law applied to scraping workers. Higher TTFB requires more active threads. Queueing Theory
Proxy Overhead Tax = TTFBproxy − TTFBdirect
The latency penalty paid for routing through a residential or datacenter IP. DataFlirt routing metrics
// 04 — network trace

Anatomy of a
950ms TTFB.

A telemetry trace through a residential proxy hitting a Cloudflare-protected target. Notice how much time is spent in proxy negotiation and edge compute before the origin even responds.

residential proxyTLS 1.3Cloudflare edge
edge.dataflirt.io — live
CAPTURED
// connection setup
dns_lookup: 12ms
tcp_handshake: 45ms
tls_negotiation: 88ms
proxy_auth: 120ms

// request sent
request_time: 0.1ms
waiting_for_edge: 680ms // anti-bot compute

// response received
ttfb_total: 945.1ms
content_download: 42ms
total_time: 987.1ms

// analysis
bottleneck: "edge_compute"
status: 200 OK
// 05 — latency sources

What inflates
your TTFB.

The primary contributors to high Time to First Byte in production scraping pipelines, ranked by their average impact on total request duration.

SAMPLE SIZE ·  ·  ·  ·    1.2B requests
WINDOW ·  ·  ·  ·  ·  ·   7d trailing
UPDATED ·  ·  ·  ·  ·  ·  2026-05-19
01

Anti-bot edge compute

300–800ms · Cloudflare/DataDome JS challenges
02

Residential proxy routing

200–500ms · Multiple ISP hops and backconnects
03

Target origin DB queries

100–400ms · Uncached search or filter pages
04

TLS handshake overhead

50–150ms · Without session resumption
05

DNS resolution

10–50ms · Uncached lookups
// 06 — connection pooling

Don't pay the handshake tax,

reuse the socket.

High TTFB destroys pipeline economics because workers sit idle waiting for bytes. DataFlirt mitigates this by aggressively pooling connections and enforcing HTTP Keep-Alive at the proxy layer. By reusing established TCP/TLS sessions for subsequent requests to the same target, we eliminate the network setup overhead entirely, dropping TTFB by up to 400ms per request and drastically reducing the concurrency required to hit target RPS.

worker-04.telemetry

Live TTFB metrics for a pooled connection vs a cold start.

target.host api.target-ecommerce.com
connection.state warm · pooled
ttfb.cold_start 840ms
ttfb.pooled 112ms
tcp_reused true
tls_resumed true
worker.efficiency +86% throughput

Stay ahead of the pipeline

Data engineering
intel, weekly.

Anti-bot shifts, scraping infrastructure updates, dataset delivery patterns, and business outcomes from our pipelines. Short, technical, no fluff.

By submitting, you acknowledge our Privacy Notice.

// 07 — FAQ

Common
questions.

Common questions about measuring, debugging, and optimizing Time to First Byte in high-throughput scraping pipelines.

Ask us directly →
What is the difference between TTFB and page load time? +
TTFB only measures the time until the very first byte of the response is received. Page load time includes TTFB, plus the time to download the entire HTML payload, parse it, fetch sub-resources (images, CSS, JS), and render the DOM. In API scraping, TTFB is often 90% of the total request time. In browser scraping, it might only be 10%.
What is considered a 'good' TTFB for a scraping pipeline? +
It depends heavily on the proxy type and target. A direct datacenter request to a cached CDN edge should have a TTFB under 100ms. Routing through a residential proxy to an uncached search endpoint might legitimately take 800–1200ms. Anything consistently over 2000ms usually indicates severe proxy congestion or an anti-bot tarpit.
How do residential proxies affect TTFB? +
They inflate it significantly. A request through a residential proxy must travel from your server to the proxy provider's gateway, then to the residential peer (often on a slow home connection), then to the target, and back. This multi-hop routing typically adds 300–600ms of pure network latency to your TTFB.
Why does TTFB spike right before my scraper gets blocked? +
Anti-bot systems like Cloudflare or Akamai often route suspicious traffic to heavier compute nodes for deep inspection, or intentionally delay the response (tarpitting) to bleed your concurrency and discourage automated access. A sudden, sustained spike in TTFB is a leading indicator that your fingerprint or IP reputation has degraded.
How does DataFlirt handle targets with inherently slow TTFB? +
We decouple the fetch layer from the extraction layer using asynchronous I/O and aggressive horizontal scaling. If a target takes 3 seconds to respond, a synchronous worker would be blocked. Our async workers can hold thousands of open sockets simultaneously, ensuring that high TTFB doesn't bottleneck the overall pipeline throughput.
Does HTTP/2 multiplexing improve TTFB? +
Yes, dramatically. HTTP/2 allows multiple concurrent requests to be sent over a single TCP connection. This means you only pay the TCP and TLS handshake tax once. Subsequent requests on that multiplexed connection will have a much lower TTFB because the network tunnel is already established and warm.
$ dataflirt scope --new-project --target=time-to-first-byte-(ttfb) READY

Tell us what
to extract.
We do the rest.

20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or a continuous feed across millions of records — we scope, build, and operate the pipeline.

Start a pipeline → View pricing
hello@dataflirt.com  ·  Bengaluru  ·  IST  ·  typical reply < 4h
// 08 — keep reading

Related glossary terms

Scraping Performance

TCP Handshake Time

The initial network setup cost that inflates TTFB on cold connections.

Read →
IP Proxies

Proxy Latency

How routing through third-party networks adds unavoidable delay to your TTFB.

Read →
Network Layer

Connection Pooling

The primary technique for reducing TTFB by reusing established sockets.

Read →
Scraping Performance

Scraper Latency

The broader metric encompassing TTFB, download time, and extraction overhead.

Read →