Continue is an IDE extension for AI-assisted coding that integrates with various LLMs and cloud services, while Ollama is a local LLM runtime that lets developers run open-source models offline on their own hardware. Continue focuses on in-editor AI assistance, whereas Ollama emphasizes local model execution without external dependencies.
Continue vs Ollama — Side-by-side comparison on A Versus B
Choose Continue if you want the best-in-class AI coding experience with access to state-of-the-art models (GPT-4, Claude 3) integrated seamlessly into your IDE with minimal setup. Choose Ollama if you prioritize data privacy, offline operation, cost savings (free models), and want to experiment with various open-source LLMs without relying on cloud services or API subscriptions.
Open-source AI code completion IDE extension with local and remote model support
Lightweight CLI tool for running open-source LLMs locally
Structured comparison data for Continue vs Ollama with 179 attributes.
Privacy Level (0=cloud-only, 100=fully local)
Code Generation Accuracy (HumanEval Benchmark)
Monthly Operating Cost (5,000 token average session)
Data Privacy (0=external servers, 1=local only)
Total Cost of Ownership (12 months, 1M daily tokens)
Inference Latency (7B model, first token)
Model Inference Speed (Llama 2 7B on RTX 4090)
Throughput (tokens/second, batch size 32)
Setup Time (from download to first inference)
Multimodal Capabilities (Vision, Image Gen)
Inference Throughput (RTX 4090, Llama 2 13B)
Typical Response Quality (Reasoning Tasks)
Typical Code Generation Quality (Subjective Rating)
Continue vs Ollama — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for Continue vs Ollama.
Yes, this is the recommended setup for privacy-focused developers. Configure Continue to use Ollama as the LLM backend instead of proprietary APIs. You get the IDE integration and chat interface of Continue with the offline privacy of Ollama.
What's the learning curve for each tool?
Continue is beginner-friendly: install extension, authenticate with an API key, start using. Ollama requires CLI familiarity but is straightforward for developers: download, run 'ollama pull llama2', then 'ollama serve'. Non-technical users may find Continue easier.
Continue itself requires internet for proprietary LLM APIs (Claude, GPT-4). However, you can configure it to use Ollama as a local backend, which enables offline operation. This requires running Ollama separately on your machine and pointing Continue to the local Ollama server via configuration.
No, Ollama is completely free and open-source. You can run as many open-source models as your hardware supports at no cost. There are no subscription fees, API charges, or usage limits. Your only costs are electricity and hardware.
Which is faster for code generation: Continue or Ollama?
Continue with cloud LLMs (Claude 3, GPT-4) is faster and produces higher-quality code (120-200 tokens/sec). Ollama runs locally at 30-60 tokens/sec on mid-range hardware but is sufficient for most coding tasks. Speed tradeoff: cloud = fast+premium, local = slower+private.