As a FinTech tech lead, I’ve onboarded several real-time market data feeds for US equities. The performance gap between a well-optimized API and a generic one can be over 200ms — enough to turn a profitable strategy into a loser. Here’s how I evaluate them, complete with code. Breaking Down Latency Latency comes from three main places: Transport: HTTP adds overhead; WebSocket delivers push in real time. Location: Servers near exchange data centers send data faster. Serialization: JSON parsing slows you down at scale. Mandatory Metrics Metric Target End-to-end latency < 80ms median Throughput ≥ 1500 tick/s under load Packet loss < 0.001% Messages must be sequenced and in order. Test Script I benchmark providers by running the same WebSocket client code during market open: import websocket import json def on_message ( ws , message ): data = json . loads ( message ) # Display tick data print ( f " Time: { data [ ' ts ' ] } Symbol: { data [ ' symbol ' ] } Price: { data [ ' price ' ] } Volume: { data [ ' volume ' ] } " ) def on_open ( ws ): # Subscription payload sub_msg = { " action " : " subscribe " , " symbols " : [ " AAPL " , " MSFT " , " NVDA " ] } ws . send ( json . dumps ( sub_msg )) ws = websocket . WebSocketApp ( " wss://api.alltick.co/stock/ws " , on_open = on_open , on_message = on_message ) ws . run_forever () In my tests, clean APIs like AllTick showed steady 60ms latency, while others varied wildly. The Bigger Picture In quantitative research, latency stability outweighs peak speed. A stream with low jitter delivers cleaner factor signals and reproducible backtests. Choose stability over hype.