Continue vs Ollama
Continue
VS Code extension for AI-powered coding with multi-provider LLM support
Professional developers prioritizing speed and model quality, teams wanting standardized AI tooling, users comfortable with API costs
Ollama
Free, open-source platform for running large language models locally on personal computers.
Privacy-conscious users, organizations with data sensitivity, developers with sufficient local hardware, those building custom LLM applications
Short Answer
Continue is a VS Code extension that brings AI coding assistance directly into your editor with support for multiple LLM providers, while Ollama is a local LLM runtime that downloads and runs open-source models on your machine without cloud dependency. Continue requires an internet connection and API keys, whereas Ollama runs entirely offline after model download.
Our Verdict
AI-assistedChoose Continue if you want seamless IDE integration with minimal setup, access to cutting-edge models, and are comfortable with API costs for professional productivity. Choose Ollama if you prioritize privacy, offline capability, want complete control over models, have decent local hardware, and prefer zero recurring costs for personal or organizational use.
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Choose Continue if
Professional developers prioritizing speed and model quality, teams wanting standardized AI tooling, users comfortable with API costs
Choose Ollama if
Privacy-conscious users, organizations with data sensitivity, developers with sufficient local hardware, those building custom LLM applications
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Key Differences at a Glance
Key Facts & Figures
| Metric | Continue | Ollama | Diff |
|---|---|---|---|
| Initial Setup Time(minutes) | 10-20 (API key + config required) | 20-30 minutes | -40% |
| Autocomplete Latency(milliseconds) | 200-500ms average | — | — |
| Context Window Size(tokens) | Up to 100,000+ tokens | — | — |
| Supported IDEs Count(IDEs) | VS Code, JetBrains suite, Vim, Neovim (4 major platforms) | — | — |
| Paid Plan Monthly Cost(USD) | Free (optional donations for commercial use) | — | — |
| Programming Languages Supported(count) | 50+ (with LLM-dependent support) | — | — |
| Base Cost (Monthly)(USD) | $0 (self-hosted) | — | — |
| Supported IDE Count(IDEs) | 3 (VSCode, JetBrains, Cursor) | — | — |
| GitHub Stars (as of 2026)(stars) | 10,000+ | ~70,000 stars | -86% |
| Monthly Cost (Individual)(USD) | Free (+ API costs) | — | — |
| AI Model Options(count) | 5+ (Claude, GPT-4, Llama 2, custom, local) | — | — |
| IDE Support(count) | 4 major (VS Code, JetBrains, Vim, Web) | — | — |
| Base Monthly Cost(USD) | Free | — | — |
| Supported AI Models(count) | 6+ (Claude, GPT-4, Ollama, local) | — | — |
| IDE Compatibility(count) | 5+ (VS Code, JetBrains, Vim) | — | — |
| Code Context Window(tokens) | 8000-200000 (model-dependent) | — | — |
| Real-time Suggestion Speed(ms latency) | 400-800 | — | — |
| Estimated Active Users(thousands) | 150 | — | — |
| User Base Size(millions) | ~0.05 million (2025 estimate) | — | — |
| Base Pricing (Monthly)(USD) | $0 | — | — |
| Code Completion Latency(milliseconds) | 800-1200 | — | — |
| Number of Supported IDEs(count) | 4 | — | — |
| Time to First Response (Small Prompt)(seconds) | 2-5 sec (Claude/GPT-4) | 15-45 sec (CPU), 3-8 sec (GPU) | -86% |
| Monthly Cost at Heavy Usage(USD) | $50-150 for power users | $0 after hardware | — |
| Available Models(count) | 10+ providers supported | 2000+ | -100% |
| Minimum RAM Requirement(GB) | 4GB | 8 GB minimum | -50% |
| Code Generation Accuracy (HumanEval Benchmark)(%) | 68% (Llama 2 70B) | 68% (Llama 2 70B) | — |
| Monthly Operating Cost (5,000 token average session)(USD) | $0 (hardware only) | $0 (hardware only) | — |
| Minimum Hardware RAM Required(GB) | 8GB (Llama 2 7B) | 8GB (Llama 2 7B) | — |
| Average Response Latency(ms) | 5-10s (CPU) / 2-4s (GPU) | 5-10s (CPU) / 2-4s (GPU) | — |
| Supported Programming Languages(languages) | 50+ languages | 50+ languages | — |
| Data Privacy (0=external servers, 1=local only)(privacy score) | 1 (local) | 1 (local) | — |
| Minimum Hardware to Run(GB RAM) | 4GB (minimum); 8GB recommended | 4GB (minimum); 8GB recommended | — |
| Production API Cost(USD/month) | $0 (fully open-source) | $0 (fully open-source) | — |
| Community Contributors(count) | 10,000+ GitHub stars, active Discord | 10,000+ GitHub stars, active Discord | — |
| Inference Speed (Llama 2 7B)(tokens/sec) | 15-50 (GPU-dependent) | 15-50 (GPU-dependent) | — |
| Total Cost of Ownership (12 months, 1M daily tokens)(USD) | $0 (hardware amortized) | $0 (hardware amortized) | — |
| Inference Latency (7B model, first token)(milliseconds) | 800-1200ms | 800-1200ms | — |
| Throughput (7B model)(tokens/second) | 8-15 | 8-15 | — |
| Setup Time to First Inference(minutes) | 8-10 (including model download) | 8-10 (including model download) | — |
| Maximum Concurrent Requests(requests) | 1-5 (limited by local hardware) | 1-5 (limited by local hardware) | — |
| Supported Quantization Formats(count) | 1 (GGUF) | 1 (GGUF) | — |
| Model Inference Speed (Llama 2 7B on RTX 4090)(tokens/sec) | ~145 tokens/sec | ~145 tokens/sec | — |
| Idle Memory Usage(MB) | ~250 MB | ~250 MB | — |
| Model Download Time (7B model)(minutes) | 3-5 minutes (depends on internet) | 3-5 minutes (depends on internet) | — |
| GPU Acceleration Options(count) | NVIDIA CUDA, AMD ROCm, Metal (Apple) | NVIDIA CUDA, AMD ROCm, Metal (Apple) | — |
| Time to First Token (ms)(milliseconds) | 150-300 ms | 150-300 ms | — |
| Throughput (tokens/second, batch size 32)(tokens/sec) | ~80 tok/s | ~80 tok/s | — |
| Minimum RAM Required(GB) | 4 GB (with offloading) | 4 GB (with offloading) | — |
| GPU Memory for 7B Model(GB) | 6-8 GB (fp16) | 6-8 GB (fp16) | — |
| Setup Time (from download to first inference)(minutes) | 5 minutes | 5 minutes | — |
| Pre-packaged Models Available(count) | 20,000+ (registry) | 20,000+ (registry) | — |
| GitHub Stars | 100,000+ | 100,000+ | — |
| Cost (Monthly Usage Example)(USD) | $0 (free) | $0 (free) | — |
| Model Accuracy (MMLU Benchmark %)(%) | Llama 2 70B: 82.3% | Llama 2 70B: 82.3% | — |
| Setup Time (First Use)(minutes) | 15-30 minutes (download, install, configure) | 15-30 minutes (download, install, configure) | — |
| Number of Available Models(models) | 50+ open-source models | 50+ open-source models | — |
| Installation Size(MB) | ~150 MB | ~150 MB | — |
All figures sourced from publicly available data. Last updated Jun 2026.
Key Differences
Continue
Editor extension with cloud/local support
Ollama
Local-only runtime engine🏆
Continue
Required for most providers
Ollama
Not required after initial setup🏆
Continue
IDE-integrated coding assistance
Ollama
Standalone LLM inference engine
Continue
5-10 minutes with API configuration🏆
Ollama
10-30 minutes depending on model size
Continue
Limited to provider offerings
Ollama
Full control over downloaded models🏆
Continue
$20-100+ monthly depending on provider
Ollama
Free after hardware investment🏆
Continue
Minimal (internet connection only)🏆
Ollama
8GB+ RAM, GPU recommended for speed
Full Comparison
| Attribute | Continue |  Ollama |
|---|---|---|
| Setup Time(minutes) | 5-10 minutes | 2-3 (install binary, run command) |
| Initial Setup Time(minutes) | 10-20 (API key + config required) | 20-30 minutes |
| Free Tier Autocomplete Limit(completions per month) | Unlimited with local models | — |
| Paid Plan Monthly Cost(USD) | Free (optional donations for commercial use) | — |
| Base Cost (Monthly)(USD) | $0 (self-hosted) | — |
| Monthly Cost (Individual)(USD) | Free (+ API costs) | — |
| Base Monthly Cost(USD) | Free | — |
Show 1 more attributeCost (Monthly Usage Example)(USD) $0 (free) — | ||
| Autocomplete Latency(milliseconds) | 200-500ms average | — |
| Code Context Window(tokens) | 8000-200000 (model-dependent) | — |
| Real-time Suggestion Speed(ms latency) | 400-800 | — |
| Code Completion Latency(milliseconds) | 800-1200 | — |
| Time to First Response (Small Prompt)(seconds) | 2-5 sec (Claude/GPT-4) | 15-45 sec (CPU), 3-8 sec (GPU) |
Show 13 more attributesCode Generation Accuracy (HumanEval Benchmark)(%) 68% (Llama 2 70B) — Average Response Latency(ms) 5-10s (CPU) / 2-4s (GPU) — Inference Speed (Llama 2 7B)(tokens/sec) 15-50 (GPU-dependent) — Inference Latency (7B model, first token)(milliseconds) 800-1200ms — Throughput (7B model)(tokens/second) 8-15 — Model Inference Speed (Llama 2 7B on RTX 4090)(tokens/sec) ~145 tokens/sec — Idle Memory Usage(MB) ~250 MB — Model Download Time (7B model)(minutes) 3-5 minutes (depends on internet) — GPU Acceleration Options(count) NVIDIA CUDA, AMD ROCm, Metal (Apple) — Time to First Token (ms)(milliseconds) 150-300 ms — Throughput (tokens/second, batch size 32)(tokens/sec) ~80 tok/s — Model Accuracy (MMLU Benchmark %)(%) Llama 2 70B: 82.3% — Installation Size(MB) ~150 MB — | ||
| Context Window Size(tokens) | Up to 100,000+ tokens | — |
| Data Privacy Model | Self-hosted option available; optional cloud sync | — |
| Data Privacy Level | Depends on provider, some cloud processing | 100% local, zero external transmission |
| Data Privacy (0=external servers, 1=local only)(privacy score) | 1 (local) | — |
| Supported IDEs Count(IDEs) | VS Code, JetBrains suite, Vim, Neovim (4 major platforms) | — |
| Programming Languages Supported(count) | 50+ (with LLM-dependent support) | — |
| Supported IDE Count(IDEs) | 3 (VSCode, JetBrains, Cursor) | — |
| Number of Supported IDEs(count) | 4 | — |
| Supported Quantization Formats(count) | 1 (GGUF) | — |
| AI Model Choices(models) | Claude, GPT-4, Llama, Mistral, local | — |
| IDE Integration(text) | Native VS Code extension | Requires external plugins/API setup |
| Supported Programming Languages(languages) | 50+ languages | — |
| Autonomous Code File Editing(yes/no) | No (suggestions only) | — |
| REST API Support | Yes (native) | — |
Show 4 more attributesLoRA Fine-tuning Not supported — Model Merging Not supported — Number of Available Models(models) 50+ open-source models — Multimodal Capabilities (Vision, Image Gen) Limited; vision support emerging in some models — | ||
| Data Processing Location | Local-first or via chosen API provider | — |
| Local Model Support(boolean) | Yes (Ollama, LLaMA) | — |
| Local Execution Support(boolean) | Yes (full local support) | — |
| Data Privacy (Cloud Processing)(boolean) | Optional (local or cloud) | — |
| Local Processing Option(supported) | Yes (default) | — |
| GitHub Stars (as of 2026)(stars) | 10,000+ | ~70,000 stars |
| Estimated Active Users(thousands) | 150 | — |
| User Base Size(millions) | ~0.05 million (2025 estimate) | — |
| Community Contributors(count) | 10,000+ GitHub stars, active Discord | — |
| Free Tier Code Completions(completions/month) | Unlimited (depends on API usage) | — |
| Customization via Config | Full JSON config (prompts, model params, shortcuts) | — |
| Supported AI Models(count) | 6+ (Claude, GPT-4, Ollama, local) | — |
| AI Model Options(count) | 5+ (Claude, GPT-4, Llama 2, custom, local) | — |
| IDE Support(count) | 4 major (VS Code, JetBrains, Vim, Web) | — |
| IDE Compatibility(count) | 5+ (VS Code, JetBrains, Vim) | — |
| Native REST API Support | Yes (OpenAI-compatible /v1 endpoints) | — |
| Open Source(boolean) | Yes (Apache 2.0) | — |
| Enterprise SLA Support(boolean) | No (community-driven) | — |
| Setup Complexity(minutes) | 15–30 min (API key configuration) | — |
| Base Pricing (Monthly)(USD) | $0 | — |
| Monthly Cost at Heavy Usage(USD) | $50-150 for power users | $0 after hardware |
| Monthly Operating Cost (5,000 token average session)(USD) | $0 (hardware only) | — |
| Enterprise SSO Authentication(supported) | No | — |
| Open-Source Availability(status) | Full open-source (Apache 2.0) | — |
| Team Size Limit (Free Tier)(users) | Unlimited | — |
| Maximum Concurrent Requests(requests) | 1-5 (limited by local hardware) | — |
| Training Data Cutoff(year) | 2024 | — |
| Available Models(count) | 10+ providers supported | 2000+ |
| Internet Dependency(text) | Required for cloud models | Not required after setup |
| Minimum RAM Requirement(GB) | 4GB | 8 GB minimum |
| Minimum Hardware to Run(GB RAM) | 4GB (minimum); 8GB recommended | — |
| Minimum RAM Required(GB) | 4 GB (with offloading) | — |
| Minimum Hardware RAM Required(GB) | 8GB (Llama 2 7B) | — |
| Free Tier API Limit(GB/month) | Unlimited (fully free) | — |
| Production API Cost(USD/month) | $0 (fully open-source) | — |
| Privacy Level(null) | 100% local processing | — |
| Total Cost of Ownership (12 months, 1M daily tokens)(USD) | $0 (hardware amortized) | — |
| Minimum Hardware Requirements(GB RAM / GPU VRAM) | 8GB RAM + 4GB GPU (Llama 7B) | — |
| Setup Time to First Inference(minutes) | 8-10 (including model download) | — |
| User Interface | Command-line interface | — |
| Graphical User Interface | No (CLI only) | — |
| Setup Time (from download to first inference)(minutes) | 5 minutes | — |
| Setup Time (First Use)(minutes) | 15-30 minutes (download, install, configure) | — |
| Installation Complexity(minutes) | Medium (CLI setup required) | — |
| GPU Memory for 7B Model(GB) | 6-8 GB (fp16) | — |
| Pre-packaged Models Available(count) | 20,000+ (registry) | — |
| GitHub Stars | 100,000+ | — |
| Internet Connectivity Required | Only for initial model download; runs offline after | — |
| Latest Release Activity | Weekly updates (as of 2026) | — |
| CPU Fallback Support(capability) | Full support with graceful degradation | — |
OllamaShow 1 more attribute
Show 13 more attributes
Show 4 more attributes
Visual Comparison
Side-by-side comparison of numeric attributes
Pros & Cons
Continue
Pros
- Native VS Code integration with inline autocomplete and chat
- Supports 10+ LLM providers (OpenAI, Claude, Gemini, LLaMA, local models)
- Quick 5-minute setup with straightforward API key configuration
- Automatic context awareness for file selection and code understanding
- Built-in support for local model connections alongside cloud providers
Cons
- Requires API keys and internet connection for most premium models
- Recurring costs of $20-100+ monthly for Claude/GPT-4 usage at scale
- Limited debugging capabilities compared to full IDE native features
Ollama
Pros
- Runs entirely offline after model download with zero cloud dependency
- Free indefinitely with no API costs or usage limits
- Support for 50+ open-source models (Llama 2, Mistral, Neural Chat, CodeLlama)
- Privacy-focused with all processing on local machine, no data sent to servers
- Full model control including customization and fine-tuning capabilities
Cons
- Requires 8GB+ RAM minimum, GPU strongly recommended for practical inference speeds
- Slower responses than cloud models (30+ seconds for some queries on CPU-only)
- Requires separate IDE integration setup via plugins or API endpoints
Frequently Asked Questions
Yes, Continue supports Ollama as a local model provider. You can configure Continue to connect to your Ollama instance running locally, combining Continue's IDE integration with Ollama's offline capability. This requires Ollama to be running in the background and Continue to be pointed at localhost:11434.
Resources & Learn More
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Where to Buy
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