Ollama

Ollama is the most direct way to run open-weight language models on your own machine. It packages model download, quantization, and a local server behind a single CLI, so ollama run llama4 pulls the weights and starts an OpenAI-compatible API in one step. A desktop app and a hosted cloud tier now sit alongside the original command-line tool.

Where it fits

Ollama is the simplest way to go from zero to a running open model. One install, one ollama run command, and you have a local server exposing an OpenAI-compatible (and now Anthropic-compatible) API that your existing client code can point at by changing the base URL.

That makes it the natural fit for local development, offline and privacy-sensitive inference, and prototyping before you commit to a paid hosted provider. Teams use it to keep prompts and responses on hardware they control, to demo on a laptop without network access, and to iterate on agent loops without metering every token. The 2025 cloud tier extends the same workflow to datacenter GPUs when a model is too large for local VRAM.

Cost to adopt

Running locally is free and open source under the MIT license. The real cost is hardware: usable speed on mid-size models needs a recent GPU with enough VRAM, and quantized GGUF defaults exist precisely to fit larger models onto smaller cards at some quality cost. There is no per-token bill on the local path.

Ollama Cloud adds a hosted option for models too big to run locally. Free is $0 with one concurrent cloud model, Pro is $20/month (or $200 billed annually) with more usage and three concurrent models, and Max is $100/month for sustained heavy use and ten concurrent models. Cloud usage is metered by GPU time rather than tokens, so heavier models draw down quota faster. Local use stays unlimited on every tier.

How it compares

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  Replicate, a hosted API for running open models and custom checkpoints with no hardware to manage. Pick it when you want a managed endpoint and pay-per-use over owning the box.

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  Modal, serverless GPU compute for custom inference and batch jobs you script yourself. Pick it when you need programmable scale, not a one-command local runtime.

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  Together, a high-throughput hosted inference API for open models at production volume. Pick it when you have outgrown local hardware and want a managed, fast endpoint.