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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.

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.

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.

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.

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.

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From the team Parasail blog

Latest articles

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

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

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

Closed vs. open-weight models: When to migrate

How to increase LLM inference throughput without breaking your latency budget

Reduce coding-agent costs with OpenCode and Parasail

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

10 best open-weight AI inference providers for startups

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

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

How Parasail built one AI agent for inference operations

Building sub-second LLM inference for global AI traffic

Prefill vs. decode in LLM inference

Designing multi-region LLM deployments with gateway architecture

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

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

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

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

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

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

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

Parasail and Neuralwatt: More Inference from Every Watt

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

Serverless vs. Dedicated Inference: Why We Built Elastic Endpoints

Parasail and Wafer AI: Faster models, lower costs

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

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

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