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Insights, product notes, and implementation guides for teams building on AI APIs.
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Private GPU Cloud: Architecture, Isolation, and Operating Model
Learn how to evaluate private GPU cloud architecture, isolation, scheduler ownership, security evidence, operating model, TCO, and acceptance tests.
vLLM Deployment Guide: Serve an OpenAI-Compatible LLM API on GPUs
Deploy vLLM as an OpenAI-compatible API on GPUs with Docker, RunPod Serverless, Ray Serve, metrics, rollout controls, and cost-per-accepted-output checks.
GPU Cloud vs Model API: When Self-Hosting Actually Saves Money
Compare GPU cloud vs model API economics with a break-even worksheet for accepted output cost, utilization, retries, idle capacity, and serving operations.
GPU Cluster TCO: Cloud Rental vs Colocation vs On-Premises
Compare GPU cluster TCO across cloud rental, colocation, and on-premises with a practical FinOps worksheet for AI infrastructure buyers.
NVIDIA B300 vs B200: Memory, Bandwidth, Training, and Inference
A practical B200 vs B300 comparison for ML infrastructure teams evaluating NVIDIA Blackwell GPU memory, bandwidth, training, inference, and procurement economics.
10 Best RunPod Alternatives in 2026: Prices, Pros, and Which to Pick
Compare 10 RunPod alternatives with October 2026 GPU prices: cheapest marketplaces, stable GPU clouds, serverless platforms, and model APIs, plus Vast.ai vs RunPod, reliability, and a migration checklist.
NVIDIA L40S vs H100 for AI Inference: Throughput per Dollar
A practical L40S vs H100 comparison for AI teams evaluating inference throughput, latency, memory fit, and cloud GPU cost per accepted output.
Best GPU for LLM Inference: Memory, Throughput, Latency, and Cost
A practical GPU selection guide for enterprise LLM inference teams, comparing H100, H200, B200, A100, L40S, local RTX-class GPUs, tokens per dollar, p95 latency, memory fit, and cost per accepted token.
Mistral API Pricing (2026): Every Model, Batch Discount, and Cost Example
Full Mistral API price list by model family, free tier and 50% batch rules, worked cost examples, a comparison with GPT, Claude and Qwen, and setup code.
Llama API (2026): Where You Can Still Call Llama, Prices, and Setup
What happened to the official Meta Llama API, which providers still serve Llama 3.3 and Llama 4 in October 2026, their prices, copy-paste OpenAI SDK code, and self-hosting trade-offs.
Grok API Pricing (2026): Every Model, Tool Fee, and Multiplier
Every current Grok API price: text, Imagine, and Voice models, plus the long-context, priority, US-region, batch, and tool multipliers, with a calculator and code.
vLLM vs SGLang: Throughput, Latency, Model Support, and Operations
Compare SGLang vs vLLM for cloud GPU inference: throughput, latency, model support, operations, benchmarks, and when Ollama fits local testing.
GPU Cluster Rental: A Buyer's Guide to Multi-Node AI Capacity
A buyer's guide for enterprise AI teams evaluating multi-node GPU cluster rental, cloud providers, reserved capacity, network fabric, storage, and TCO.
NVIDIA H100 vs A100: Training and Inference Cost-Performance
A practical H100 vs A100 comparison for AI teams deciding between Hopper performance and Ampere value across training, inference, and cloud GPU rental.
H200 vs H100 (2026): Specs, Real Performance, Price per Hour, and Which to Rent
NVIDIA H200 vs H100 in 2026: official specs, why 141GB matters for 70B inference, real hourly rental prices from Flatkey Compute, RunPod, and Lambda, Blackwell alternatives, and a workload-by-workload pick.
OpenAI API Cost in 2026: Pricing Table and Calculator
A current OpenAI API cost guide with GPT-6 pricing tables, a copyable calculator, worked examples, hidden fees, and optimization checks.
GPU Rental Prices Explained: On-Demand, Spot, Reserved, and Hidden Costs
A practical guide to comparing GPU rental prices by completed workload cost, not only hourly rates, with source checks for on-demand, spot, reserved, marketplace, and hidden-cost models.
LLM Context Window Explained: What It Means, Sizes for Every Major Model, and How to Use It (October 2026)
An LLM context window is the model's working memory per request. See what 128K, 200K and 1M tokens mean, published limits for major models in October 2026, long-context pricing, and code to count tokens.
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