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
Open-source AI code completion IDE extension with local and remote model support
Lightweight CLI tool for running open-source LLMs locally
Primary Purpose. Setup Complexity. Offline Capability. Supported IDEs. Model Flexibility. Hardware Requirements. Privacy/Data Handling
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.
Continue: 10-20 (API key + config required) | Ollama: 2-3 minutes
Continue: Unlimited with local models | Ollama: N/A
Continue: 200-500ms average | Ollama: N/A
Continue: Up to 100,000+ tokens | Ollama: N/A
Continue: VS Code, JetBrains suite, Vim, Neovim (4 major platforms) | Ollama: N/A
Continue: Free (optional donations for commercial use) | Ollama: N/A
Continue: 50+ (with LLM-dependent support) | Ollama: N/A
Continue: $0 (self-hosted) | Ollama: N/A
Continue: Claude, GPT-4, Llama, Mistral, local | Ollama: N/A
Continue: Local (on-device) | Ollama: N/A
Continue: Unlimited (depends on API usage) | Ollama: N/A
Continue: Full JSON config (prompts, model params, shortcuts) | Ollama: N/A
Continue: Free (+ API costs) | Ollama: N/A
Continue: 5+ (Claude, GPT-4, Llama 2, custom, local) | Ollama: N/A
Continue: VS Code, IntelliJ, PyCharm, CLion, Rider, WebStorm | Ollama: CLI-based; requires manual IDE plugin integration
Continue: 6+ (Claude, GPT-4, Ollama, local) | Ollama: N/A
Continue: 5+ (VS Code, JetBrains, Vim) | Ollama: N/A
Continue: 8000-200000 (model-dependent) | Ollama: N/A
Continue: Yes (Ollama, LLaMA) | Ollama: N/A
Continue: Yes (full local support) | Ollama: N/A
Continue: ~0.05 million (2025 estimate) | Ollama: N/A
Continue: No (community-driven) | Ollama: N/A
Continue: Optional (local or cloud) | Ollama: N/A
Continue: Full open-source (Apache 2.0) | Ollama: N/A
Continue: 2-5 sec (Claude/GPT-4) | Ollama: 15-45 sec (CPU), 3-8 sec (GPU)
Continue: $50-150 for power users | Ollama: $0 after hardware
Continue: 10+ providers supported | Ollama: 15+ models
Continue: Required for cloud models | Ollama: Not required after setup
Continue: 2-4 GB (extension only) | Ollama: 8-16 GB (for quality inference)
Continue: Native VS Code extension | Ollama: Requires external plugins/API setup
Continue: Depends on provider, some cloud processing | Ollama: 100% local—zero external data transmission
Continue: 15-30 minutes (model download + config) | Ollama: N/A
Continue: 5-15 seconds (hardware-dependent) | Ollama: N/A
Continue: 4 major platforms (VS Code, JetBrains, Vim, Neovim) | Ollama: N/A
Continue: Unlimited (fully free and open-source) | Ollama: N/A
Continue: $0 (open-source) or hosted option available | Ollama: N/A
Continue: Local-only, zero cloud transmission | Ollama: N/A
Continue: Yes (GitHub public repository) | Ollama: N/A
Continue: Varies by provider | Ollama: N/A
Continue: 3 major editors (VS Code, JetBrains, Vim) | Ollama: None (CLI/API only)
Continue: 15+ providers including premium APIs | Ollama: 100+ open-source models (single source)
Continue: 2 minutes (IDE extension) | Ollama: 5-15 minutes (install + model download)
Continue: 300-500 MB | Ollama: 50-200 MB
Privacy Level (0=cloud-only, 100=fully local)
Continue: 80 (optional local via Ollama backend) | Ollama: 100 (always local)
Continue: Comprehensive with config examples | Ollama: Extensive REST API documentation
Continue: $0 (free + optional API costs) | Ollama: $0 (fully free)
Continue: Fully local option available | Ollama: N/A
Continue: 4+ (Claude, Llama, GPT-4, Mistral) | Ollama: N/A
Continue: High (manual LLM configuration required) | Ollama: N/A
Continue: Depends on LLM vendor (Claude, OpenAI, Google store data per ToS) | Ollama: 100% local, zero external transmission
Continue: 120-200 (with cloud LLMs like Claude) | Ollama: 30-60 (local on mid-range hardware)
Continue: GPT-4, Claude 3, Gemini, custom servers | Ollama: 100+ open-source: Llama 2, Mistral, Neural Chat, Orca, Zephyr
Continue: 15-30 (with model download) | Ollama: 15-30 minutes
Continue: $20 (Claude 3) | Ollama: $0 (free open models)
Continue: Full offline with local models | Ollama: Full offline operation
Continue: Free (open-source) | Ollama: N/A
Continue: Self-hosted (variable) | Ollama: N/A
Continue: 4 major (VS Code, JetBrains, Vim, Neovim) | Ollama: N/A
Continue: No data transmission with local setup | Ollama: N/A
Continue: 8+ (Claude, GPT-4, Llama, Mistral, local models) | Ollama: N/A
Continue: ~50-100 (estimated) | Ollama: N/A
Continue: N/A | Ollama: Full native support
Continue: N/A | Ollama: Command-line (CLI)
Continue: N/A | Ollama: Manual CLI required
Continue: N/A | Ollama: 3 (macOS, Linux, Windows)
Code Generation Accuracy (HumanEval Benchmark)
Continue: N/A | Ollama: 68% (Llama 2 70B)
Monthly Operating Cost (5,000 token average session)
Continue: N/A | Ollama: $0 (hardware only)
Continue: N/A | Ollama: 8GB (Llama 2 7B)
Continue: N/A | Ollama: 5-10s (CPU) / 2-4s (GPU)
Data Privacy (0=external servers, 1=local only)
Continue: N/A | Ollama: No (suggestions only)
Continue: N/A | Ollama: 4GB (minimum); 8GB recommended
Continue: N/A | Ollama: Unlimited (fully free)
Continue: N/A | Ollama: $0 (fully open-source)
Continue: N/A | Ollama: 100% local processing
Continue: N/A | Ollama: 10,000+ GitHub stars, active Discord
Total Cost of Ownership (12 months, 1M daily tokens)
Continue: N/A | Ollama: $0 (hardware amortized)
Inference Latency (7B model, first token)
Continue: N/A | Ollama: 8GB RAM + 4GB GPU (Llama 7B)
Continue: N/A | Ollama: 8-10 (including model download)
Continue: N/A | Ollama: 1-5 (limited by local hardware)
Model Inference Speed (Llama 2 7B on RTX 4090)
Continue: N/A | Ollama: Command-line interface
Continue: N/A | Ollama: 3-5 minutes (depends on internet)
Continue: N/A | Ollama: Yes (OpenAI-compatible /v1 endpoints)
Continue: N/A | Ollama: NVIDIA CUDA, AMD ROCm, Metal (Apple)
Continue: N/A | Ollama: Medium (CLI setup required)
Throughput (tokens/second, batch size 32)
Continue: N/A | Ollama: 4 GB (with offloading)
Setup Time (from download to first inference)
Continue: N/A | Ollama: 20,000+ (registry)
Continue: N/A | Ollama: Llama 2 70B: 82.3%
Continue: N/A | Ollama: 15-30 minutes (download, install, configure)
Continue: N/A | Ollama: 200+ open-source models
Continue: N/A | Ollama: Only for initial model download; runs offline after
Multimodal Capabilities (Vision, Image Gen)
Continue: N/A | Ollama: Limited; vision support emerging in some models
Continue: N/A | Ollama: Weekly updates (as of 2026)
Continue: N/A | Ollama: Full support with graceful degradation
Continue: N/A | Ollama: 200-5000ms (hardware dependent)
Continue: N/A | Ollama: 1-10 (single device)
Continue: N/A | Ollama: Community-only (none)
Continue: N/A | Ollama: Unlimited (local only)
Continue: N/A | Ollama: No external data transmission (100% offline)
Continue: N/A | Ollama: Model registry only, 0 community features
Continue: N/A | Ollama: Not applicable (CLI tool)
Inference Throughput (RTX 4090, Llama 2 13B)
Continue: N/A | Ollama: 4 (CPU, Metal, CUDA, Vulkan)
Continue: N/A | Ollama: No (sequential only)
Continue: N/A | Ollama: Custom REST endpoints
Continue: N/A | Ollama: 70B (Mixtral 8x22B)
Continue: N/A | Ollama: 32GB (for optimal performance)
Continue: N/A | Ollama: 5-30 seconds (varies by hardware/model)
Continue: N/A | Ollama: Limited—basic vision models available
Typical Response Quality (Reasoning Tasks)
Continue: N/A | Ollama: Good for general tasks; weaker on complex reasoning (88% MMLU benchmark score)
Continue: N/A | Ollama: OpenAI-compatible REST API
Continue: N/A | Ollama: 6+ (GGUF, GGML, etc.)
Continue: N/A | Ollama: NVIDIA, AMD, Intel (manual setup)
Continue: N/A | Ollama: 200,000 (Llama 2 70B via Ollama)
Continue: N/A | Ollama: Full (all models run offline)
Continue: N/A | Ollama: 110+ (Llama 2 405B)
Continue: N/A | Ollama: 100+ open-source models available
Typical Code Generation Quality (Subjective Rating)
Continue: N/A | Ollama: 7.1 (best open-source models)
How to choose between Continue and Ollama
Continue vs Ollama — Structured Comparison Data
Machine-readable comparison dataset for Continue vs Ollama. Contains 179 attribute comparisons, 5 FAQ pairs.
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.
How to Choose Between Continue and Ollama
Primary Purpose: Continue has IDE coding assistant with LLM integration, while Ollama has Local LLM runtime and model management.
Setup Complexity: Continue has Simple extension install (5 minutes), while Ollama has Download + model installation (10-30 minutes).
Offline Capability: Continue has Requires cloud LLM or local Ollama backend, while Ollama has Fully offline with local models.
Supported IDEs: Continue has VS Code, JetBrains IDEs (IntelliJ, PyCharm, etc.), while Ollama has CLI tool, works with any IDE via API.
Model Flexibility: Continue has Claude 3, GPT-4, Gemini, custom LM Studio servers, while Ollama has Llama 2, Mistral, Neural Chat, 100+ open models.
Hardware Requirements: Continue has Minimal (extension only, uses remote compute), while Ollama has 8GB+ RAM recommended; 16GB+ for optimal performance.
Privacy/Data Handling: Continue has Depends on LLM choice; proprietary models send data to vendors, while Ollama has 100% local processing, zero data transmission.