Claude vs a Local LLM
Claude is a hosted frontier model from Anthropic, generally stronger on complex reasoning, coding, and long agent tasks, billed per token or subscription. A local LLM is an open-weight model on your hardware that keeps data on your machine with no per-token bill, but is limited by memory and usually less capable.
What you are choosing between
Claude is a family of models from Anthropic, available through Anthropic's API, cloud platforms, and Claude's own applications, including Claude Code for software work. You send prompts over the network; Anthropic runs the model and returns results. You get frontier capability, large context windows, strong tool use, and no infrastructure to manage, subject to Anthropic's pricing, rate limits, and data policies.
A local LLM is an open-weight model you download and run with a runtime such as Ollama, LM Studio, or llama.cpp, on a laptop, workstation, or your own server. You control the model version, where data goes, and when it runs. Capability depends on which model you can fit and how fast your hardware runs it.
The real decision is rarely one or the other for everything. It is which tasks need frontier capability and which need privacy, predictable cost, offline operation, or full control.
Reading the differences
Capability. Frontier hosted models generally lead on difficult reasoning, large multi-file code changes, and long multi-step agent loops. Open-weight models have improved rapidly and handle many everyday tasks well, especially summarisation, extraction, classification, and question answering over supplied context.
Privacy and data control. Local inference keeps data on your hardware. Hosted use sends data to the provider under its terms; organisations with strict requirements may use enterprise agreements or cloud-hosted deployments with contractual controls.
Cost structure. Hosted models cost per token or via subscription, scaling with use. Local models require up-front hardware plus electricity and maintenance, with near-zero marginal cost per request. Heavy, steady usage favours local; bursty or occasional usage favours hosted.
Latency and availability. Hosted models depend on network and provider capacity and rate limits. Local models depend on your hardware and work offline.
Control. Local deployments let you pin a model version indefinitely. Hosted models are updated and eventually retired by the provider.
Operations. Hosted is managed for you. Local means handling downloads, updates, serving, monitoring, and hardware.
Routing between them
A practical pattern routes private or high-volume routine work to a local model and sends complex reasoning or large coding tasks to Claude. Put a retrieval layer in front of both, so each model receives relevant facts from indexed knowledge rather than regenerating them, which helps smaller local models most and reduces hosted token spend.
Security considerations
Local models remove one party from the data path, but local tools, plugins, and agents can still send data elsewhere. Hosted providers offer security certifications and contractual commitments that some organisations require. Evaluate the whole toolchain, not only where the model runs.
| Dimension | Claude | Local LLM |
|---|---|---|
| Where it runs | Anthropic or cloud platforms | Your hardware |
| Capability | Frontier, strongest on hard tasks | Varies by model and hardware |
| Data location | Sent to the provider under its terms | Stays on your machine |
| Cost structure | Per token or subscription | Hardware, power, maintenance |
| Offline use | No | Yes |
| Model version control | Provider updates and retires models | You pin versions |
| Operations | Managed | Self-managed |
Frequently asked questions
- Is Claude better than a local LLM?
- For complex reasoning, large coding tasks, and long agent workflows, Claude and other frontier hosted models are generally stronger. Local open-weight models handle many routine tasks well and win on data control, offline use, and predictable cost at high volume. The better choice depends on the task.
- Is it cheaper to run a local LLM than use Claude?
- It depends on usage. Local models cost hardware, electricity, and maintenance with little marginal cost per request, so heavy steady use can be cheaper. Occasional or bursty use usually costs less on hosted models, which require no up-front investment. Measure real token volume before deciding.
- Can I use a local LLM with Claude Code?
- Claude Code is designed around Anthropic's models. Some community setups route it to other endpoints, but behaviour and support vary. Many developers instead use Claude Code for complex work and separate tools configured with local models for private or routine tasks.
- Why use a local LLM?
- To keep data on your own hardware, work offline, avoid per-token costs at high volume, control exactly which model version runs, and experiment freely. The trade-offs are hardware limits, operational effort, and generally lower capability than frontier hosted models on hard tasks.