CallMissed Blog
Insights on AI communication, voice agents, WhatsApp automation, and the future of customer engagement.
6 min readAI Inference Cost Optimization: Practical Wins
The first AI bill is small. The second is a surprise. The third is a meeting. By 2026 most production AI workloads have left the toy budget behind, and the gap between teams that "do something about cost" and teams that do not is now measured in factors of 5–10x. The good news: most of the wins come…
6 min readStreaming AI Responses: SSE, WebSockets, and the Pitfalls
A streaming LLM response feels fast even when total generation takes ten seconds, because the user sees tokens arriving immediately. The trade is operational: streaming is a long-lived connection with backpressure, partial-failure modes, and a different shape from a normal HTTP request. Here is what…
Model Quantization in 2026: 4-bit, 8-bit, and the Tradeoffs
A 70-billion-parameter model in 16-bit weights wants ~140 GB of GPU memory. That is two A100 80GBs or one H100. In 4-bit weights it wants ~40 GB. That is one L40S, or even fits on a 48 GB consumer card. Quantization is the difference between "we need an expensive cluster" and "we can run this on har…
vLLM vs TGI vs SGLang: Inference Engines Compared
If you self-host an LLM, the inference engine is the single highest-leverage piece of infrastructure you choose. By 2026 the decision has narrowed: most teams pick vLLM, some pick SGLang for prefix-heavy workloads, and TGI has entered maintenance mode. Here is the picture. TGI: end of an era Hugging…