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750B tokens served dailyDay 0 support for frontier LLMs30× cheaper than legacy clouds750B tokens served dailyDay 0 support for frontier LLMs30× cheaper than legacy clouds750B tokens served dailyDay 0 support for frontier LLMs30× cheaper than legacy clouds750B tokens served dailyDay 0 support for frontier LLMs30× cheaper than legacy clouds750B tokens served dailyDay 0 support for frontier LLMs30× cheaper than legacy clouds750B tokens served dailyDay 0 support for frontier LLMs30× cheaper than legacy clouds

From the team Parasail blog

Product updates, engineering deep dives, and thought leadership from the Parasail team.

Guides

Kimi K3 vs. GPT-5.6 Sol: Performance, cost, and tradeoffs

Parasail August 21, 2026
Engineering

How Parasail built one AI agent for inference operations

August 13, 2026
Engineering

Building sub-second LLM inference for global AI traffic

August 10, 2026
Guides

Prefill vs. decode in LLM inference

August 7, 2026
Blog

Latest articles

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Kimi K3 vs. GPT-5.6 Sol: Performance, cost, and tradeoffs

Compare Kimi K3 and GPT-5.6 Sol on performance, pricing, context, weights, architecture, and deployment tradeoffs.

How Parasail built one AI agent for inference operations

The hard part wasn’t putting an AI agent in Slack. It was embedding trusted sources, reviewed calculations, and safe inference-operations workflows.

Building sub-second LLM inference for global AI traffic

A fast model isn’t a fast API. We worked backward from a 600ms p99 budget with Cloudflare Workers at the edge and WireGuard to the GPU.

Prefill vs. decode in LLM inference

Understand how prefill and decode shape time to first token, streaming performance, KV-cache pressure, and LLM serving decisions.

Designing multi-region LLM deployments with gateway architecture

Learn how multi-provider LLM gateways solve routing, failover, and latency across regions. Compare self-hosted vs. managed options for production inference.

Parasail vs. DeepInfra: Cost, latency, model selection, and engineering support

A side-by-side comparison of Parasail and DeepInfra on serverless and dedicated pricing, engineering support, model catalog, cold starts, and compliance.

Guide to optimize batching and throughput for offline LLM inference in 2026

Tune vLLM, TensorRT-LLM, and SGLang for offline LLM batch inference. Config knobs, KV cache math, prefix caching, and speculative decoding tradeoffs for 2026.

Parasail to Combine NVIDIA AI Infrastructure with d-Matrix Accelerators to Achieve 10x Faster Token Generation

Parasail to deliver faster, more cost-efficient tokens by pairing NVIDIA Hopper and Blackwell GPUs with d-Matrix Corsair accelerators

Beyond the frontier: How to build a defensible AI inference infrastructure

Closed model dependency is becoming structural liability. Here's the framework for building a reliable AI inference architecture.

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