Tag: Model Serving
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FastAPI vs Flask ML Serving: Beginner Speed Test in 50 Lines
Compare FastAPI vs Flask for ML model serving with a simple 50-line benchmark. The async performance gap may surprise youโsee which wins.
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SageMaker vs FastAPI Inference: $847/Month Breakeven Point
At 5M requests/month, self-hosted FastAPI beats SageMaker by $400. Below that? SageMaker wins when you factor in engineering time. Here's the real math.
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GPU vs CPU Inference: 5 Scenarios, Real Costs & Latency
GPU vs CPU inference across 5 traffic scenarios: real costs, latency benchmarks, and when each makes sense. BERT/ResNet/Whisper tested on AWS.
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TorchServe vs ONNX Runtime: First Inference in 5 Minutes
Compare TorchServe vs ONNX Runtime deployment speed, memory usage, and ease of setup. See which inference server wins in real-world performance tests.
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Feature Stores Are Overengineered: When SQL Is Enough
Most teams waste months on Feast or Tecton when Postgres + cron does the job. Here's when SQL beats feature stores โ with real latency numbers.
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FastAPI Model Serving: 5 Steps to 50ms Inference
Cut FastAPI model inference from 800ms to 47ms with 5 changes: lifespan handlers, warmup, ThreadPoolExecutor, Pydantic validation, and response models.
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MLOps Cheat Sheet: 12 Production Patterns You’ll Use
12 MLOps patterns from production systems: feature caching, traffic shadowing, schema validation. Real code, not theory.
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Triton vs TorchServe: How I Cut $800/Month in GPU Costs
Triton cut my inference costs 52% vs TorchServe. GPU utilization jumped from 23% to 81%. Here's what profiling actually revealed.