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  5. Serve AI models

Best AI Tools to Serve AI models

Deploy and serve AI models for applications, APIs, or production workloads.

Write codeReview codeBuild appsBuild AI appsAutomate software developmentTest websitesBuild RAG systemsCreate knowledge graphsAnalyze codebasesGenerate software documentationManage AI memoryConnect AI to appsEngineer promptsFine-tune AI modelsRun AI models locallyManage AI modelsMonitor infrastructureManage databasesRun code in sandboxesManage API gatewaysBuild search enginesOptimize AI contextRun distributed computing

Showing 2 of 2 tools

Ollama
Code
2023-06-26
Ollama logo
Ollama
177K

Platform for running large language models locally on a computer

Freemium • $0Open tool →
transformers
Code
2018-10-29
transformers logo
transformers
163K

🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.

FreeOpen tool →

Model serving tools deploy AI models behind APIs for applications and production workloads. They handle batching, scaling, GPU use and monitoring so models respond quickly and reliably under real traffic.

Choosing a serving stack

Compare supported model types and formats, throughput and latency on your hardware, OpenAI-compatible APIs, quantization, multi-GPU and autoscaling, monitoring and cost. Managed services reduce operations work; open-source servers give control and can lower costs at scale.

Running models in production

Benchmark with realistic prompts and traffic, set limits and timeouts, monitor errors and response times and plan capacity for peaks. Keep model versions tracked so you can roll back if quality drops.

FAQ

Should I self-host models or use an API?

APIs are simplest to start. Self-hosting makes sense for data control, steady high volume or custom models.

What is an OpenAI-compatible API?

An interface that mimics the OpenAI API format, so existing apps can switch to another model with few changes.

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