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Trillions of Tokens served dailyDay 0 support for frontier LLMs30× cheaper than legacy cloudsTrillions of Tokens served dailyDay 0 support for frontier LLMs30× cheaper than legacy cloudsTrillions of Tokens served dailyDay 0 support for frontier LLMs30× cheaper than legacy cloudsTrillions of Tokens served dailyDay 0 support for frontier LLMs30× cheaper than legacy cloudsTrillions of Tokens served dailyDay 0 support for frontier LLMs30× cheaper than legacy cloudsTrillions of 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.

Engineering

Can an open model cut your Claude Code and Codex costs? Here’s how to test it on your own repo

Parasail October 29, 2026
Engineering

How we got MiniMax M3.1 Flash production-ready on day 0

October 26, 2026
Engineering

How to threat model an LLM application on hosted inference in 5 steps

October 21, 2026
Comparisons

GLM-5.3 vs. GLM-5.3 Flash: Which one to run in production

October 19, 2026
Blog

Latest articles

All AnnouncementsBenchmarksComparisonsEngineering

Can an open model cut your Claude Code and Codex costs? Here’s how to test it on your own repo

Run your current coding agent and an open model on Parasail against the same repository tasks, then compare what each costs for work your team would actually accept.

How we got MiniMax M3.1 Flash production-ready on day 0

MiniMax M3.1 Flash went live on Parasail on launch day. Here is the serving work behind it: what we optimized for long agent sessions, the bugs we found under concurrency, and how it benchmarks on NVIDIA B300.

How to threat model an LLM application on hosted inference in 5 steps

A threat model for an LLM application on hosted inference traces every copy of a request, marks each trust boundary, and separates what your provider enforces from what your application has to enforce. Five steps, worked through on a customer support application.

GLM-5.3 vs. GLM-5.3 Flash: Which one to run in production

GLM-5.3 and GLM-5.3 Flash share a 1M-token context window but sit 9x apart on token price. Compare benchmarks, cost per task, caching, and speed to pick the right model for each workload.

Alternatives to Together AI: Cost, latency, and reliability in production

Compare Together AI alternatives, including Parasail, Fireworks, Baseten, DeepInfra, Modal, Groq, and Cerebras, on latency controls, billing, SLA coverage, and engineering support.

Closed vs. open-weight models: When to migrate

Not every call to a closed API needs a frontier model. This guide covers when to move routine work to open-weight models, based on your costs, your task mix, and the control you need over the model.

How to increase LLM inference throughput without breaking your latency budget

Learn how to measure goodput, tune batching and KV cache in vLLM, and decide when another GPU will help, all without breaking your latency SLOs.

Reduce coding-agent costs with OpenCode and Parasail

Connect OpenCode to an open model on Parasail and run it against your frontier default on a real bug. Here's what the fixes cost and what it took to get one you'd ship.

How to reduce LLM latency in production: Diagnose before you tune

Diagnose LLM latency before you tune it. Split TTFT, TPOT and end-to-end latency, map them to eight stages, and find which one owns your slowdown.

10 best open-weight AI inference providers for startups

How to pick an open-weight inference provider before you can forecast your traffic: criteria to test, and how 10 providers compare on billing, commitments and risk.

How Reflow AI lowered inference costs for document ingestion and OCR by 70% with Parasail

Reflow AI moved document ingestion and OCR to Parasail's Elastic Endpoints, eliminating idle GPU cost and cutting inference spend by 70%.

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.

Faster autoscaling for vLLM: Restoring from snapshots instead of starting cold

Cold-start latency is one of the biggest bottlenecks when scaling inference. Parasail's model snapshotting saves and restores CPU and GPU process state to bring vLLM replicas online 3-5x faster than rebuilding from scratch.

Most inference commits are broken. Here's how we fixed ours.

Most inference commits lock you to hardware you'll outgrow and a model you'll want to swap. We structured Parasail's commit around dollars of inference, not a SKU, so it flexes as your usage and the frontier change.

The idle GPU tax: What it is, why it’s getting worse, and how you can fix it

Learn what the idle GPU tax is, what it costs, and how usage-based billing on dedicated endpoints helps you avoid it altogether.

Parasail and Neuralwatt: More Inference from Every Watt

Neuralwatt's energy intelligence is now running in Parasail's fleet, routing compute to the most efficient GPUs and pulling more inference out of every watt.

How to choose the right managed inference architecture: Serverless, dedicated, Elastic Endpoints, or batch

Use this decision framework to choose the right managed inference mode based on latency requirements, GPU breakeven utilization, and whether your workload needs a dedicated endpoint.

Serverless vs. Dedicated Inference: Why We Built Elastic Endpoints

With Elastic Endpoints you get dedicated hardware on per-token pricing, no idle-hour charges or long-term GPU commitment.

Parasail and Wafer AI: Faster models, lower costs

Parasail and Wafer AI are partnering to make frontier AI cheaper and more accessible.

Making an EAGLE fly: How We Got 2.6x Faster LLM Inference (Without Cheating)

We trained a custom EAGLE-3 speculative decoding head for OLMo-3.1-32B-Think and got 2.6x faster inference.

Making Cold Start Latencies go Brrrr: A Multi-pronged Approach (Part 1)

We walk through how we combined fastsafetensors, O_DIRECT, and io_uring to get fast cold-starts and fast warm-starts on the same stack.

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