software
Continue vs Ollama 2026: AI Code Assistant Comparison
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.
C
Continue
Open-source AI code completion IDE extension with local and remote model support
Professional developers who want premium AI models integrated into their IDE, teams with API budgets, and users prioritizing code quality over privacy
Score63%
VS
Ollama
Lightweight CLI tool for running open-source LLMs locally
Privacy-conscious developers, teams with limited budgets, offline-first workflows, and users wanting to experiment with open-source models without vendor lock-in
Score63%
179 attributes7 differences16 pros/cons
Quick Answer
AI SummaryContinue 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.
Our Verdict
AI-assistedChoose 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.
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C
Continue
7/10
Ollama
8/10
C
Choose Continue if
Professional developers who want premium AI models integrated into their IDE, teams with API budgets, and users prioritizing code quality over privacy
Choose Ollama if
Best pickPrivacy-conscious developers, teams with limited budgets, offline-first workflows, and users wanting to experiment with open-source models without vendor lock-in
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Key Differences at a Glance
- Primary Purpose:IDE coding assistant with LLM integration vs Local LLM runtime and model management
- Setup Complexity:✓ Continue wins(Simple extension install (5 minutes) vs Download + model installation (10-30 minutes))
- Offline Capability:✓ Ollama wins(Fully offline with local models vs Requires cloud LLM or local Ollama backend)
Key Facts & Figures
113 numeric metrics compared
| Metric | Continue | Ollama | Ratio |
|---|---|---|---|
| Initial Setup Time(minutes) | 10-20 (API key + config required) | 2-3 minutes | |
| 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(languages) | 50+ (with LLM-dependent support) | — | — |
| Base Cost (Monthly)(USD) | $0 (self-hosted) | — | — |
| Supported IDE Count(IDEs) | 5 major IDEs | — | — |
| GitHub Stars (as of 2026)(thousands) | 13,000+ | ~18,000 | |
| Monthly Cost (Individual)(USD) | Free (+ API costs) | — | — |
| AI Model Options(count) | 5+ (Claude, GPT-4, Llama 2, custom, local) | — | — |
| 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 of developers) | ~0.05 million (2025 estimate) | — | — |
| 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) | |
| Monthly Cost at Heavy Usage(USD) | $50-150 for power users | $0 after hardware | |
| Available Models(count) | 10+ providers supported | 15+ models | |
| Minimum RAM Requirement(GB) | 2-4 GB (extension only) | 8-16 GB (for quality inference) | |
| Setup Time to First Use(minutes) | 15-30 minutes (model download + config) | — | — |
| Average Code Completion Time(seconds) | 5-15 seconds (hardware-dependent) | — | — |
| IDE Platform Support Count(platforms) | 4 major platforms (VS Code, JetBrains, Vim, Neovim) | — | — |
| Pro Plan Monthly Cost(USD) | $0 (open-source) or hosted option available | — | — |
| Avg Code Completion Speed(seconds) | 2 | — | — |
| Maximum Context Window(tokens) | 200,000 | — | — |
| Supported IDE Platforms(count) | 6+ | — | — |
| AI Provider Options(count) | 10+ | — | — |
| Annual Cost(USD) | $0 | — | — |
| Base Pricing (Monthly)(USD) | Free + $20 Pro | — | — |
| IDE Integration Support | 3 major editors (VS Code, JetBrains, Vim) | None (CLI/API only) | |
| LLM Provider Options | 15+ providers including premium APIs | 100+ open-source models (single source) | |
| Minimum Installation Time(minutes) | 2 minutes (IDE extension) | 5-15 minutes (install + model download) | |
| Runtime Memory Usage (Idle)(MB) | 300-500 MB | 50-200 MB | |
| Privacy Level (0=cloud-only, 100=fully local)(score) | 80 (optional local via Ollama backend) | 100 (always local) | |
| Monthly Subscription Cost(USD) | $0 | — | — |
| Annual Cost per Developer(USD) | $0 | — | — |
| Code Completion Acceptance Rate(%) | 75-82% | — | — |
| Supported LLM Backends(count) | 15+ models | — | — |
| GitHub Stars (Community Adoption)(count) | 19,000 stars | — | — |
| Project Launch Year(year) | 2024 | — | — |
| Subscription Cost (Monthly)(USD) | Free | — | — |
| IDE Support Count(IDEs) | 1 (VS Code) | — | — |
| LLM Model Flexibility(models supported) | 4+ (Claude, Llama, GPT-4, Mistral) | — | — |
| GitHub Stars(stars) | 500+ | 100,000+ | |
| Inference Speed (Llama 2 7B)(tokens/second) | 120-200 (with cloud LLMs like Claude) | 30-60 (local on mid-range hardware) | |
| Monthly Cost (Single LLM)(USD) | $20 (Claude 3) | $0 (free open models) | |
| Pricing (Base Tier)(USD/month) | Free (open-source) | — | — |
| IDE Integrations(count) | 4 major (VS Code, JetBrains, Vim, Neovim) | — | — |
| Model Selection Options(count) | 8+ (Claude, GPT-4, Llama, Mistral, local models) | — | — |
| Enterprise Customer Base(count) | ~50-100 (estimated) | — | — |
| Supported Models(count) | 100+ models | 100+ models | |
| Multi-Platform Support(platforms) | 3 (macOS, Linux, Windows) | 3 (macOS, Linux, Windows) | |
| Latest Release Year | 2024 | 2024 | |
| 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(seconds) | 5-10s (CPU) / 2-4s (GPU) | 5-10s (CPU) / 2-4s (GPU) | |
| Supported Programming Languages(count) | 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 | |
| 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) | |
| 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) | |
| 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) | 200+ open-source models | 200+ open-source models | |
| Installation Size(MB) | ~150 MB | ~150 MB | |
| Base Cost(USD/month (for typical usage)) | $0 (Free) | $0 (Free) | |
| Average Inference Latency(milliseconds) | 200-5000ms (hardware dependent) | 200-5000ms (hardware dependent) | |
| Maximum Throughput(events per second) | 1-10 (single device) | 1-10 (single device) | |
| Largest Available Model(parameters (billions)) | 70B (Llama 2) | 70B (Llama 2) | |
| Available Pre-trained Models(count) | 200+ | 200+ | |
| Minimum GPU Memory (7B LLM)(GB) | 4-6GB | 4-6GB | |
| Community Features | Model registry only, 0 community features | Model registry only, 0 community features | |
| Download Size(MB) | 450 MB | 450 MB | |
| Inference Throughput (RTX 4090, Llama 2 13B)(tokens/second) | ~175 tokens/sec | ~175 tokens/sec | |
| Memory Usage (Llama 2 7B, quantized)(GB) | 4-5 GB | 4-5 GB | |
| Installation Time (from zero)(minutes) | 3-5 minutes | 3-5 minutes | |
| Minimum VRAM for Llama 2 7B(GB) | 4 GB | 4 GB | |
| Number of Supported GPU Backends(count) | 4 (CPU, Metal, CUDA, Vulkan) | 4 (CPU, Metal, CUDA, Vulkan) | |
| Base Monthly Cost (100M tokens usage)(USD) | $0 (free) | $0 (free) | |
| Maximum Model Parameter Size(billion parameters) | 70B (Mixtral 8x22B) | 70B (Mixtral 8x22B) | |
| Minimum Recommended RAM(GB) | 32GB (for optimal performance) | 32GB (for optimal performance) | |
| Time to First Response (after setup)(seconds) | 5-30 seconds (varies by hardware/model) | 5-30 seconds (varies by hardware/model) | |
| Startup Time (7B Model)(seconds) | 3-5 seconds | 3-5 seconds | |
| Base Installation Size(MB) | 50-100 MB | 50-100 MB | |
| Available Models in Official Registry(models) | 80+ models | 80+ models | |
| Supported Quantization Formats(count) | 6+ (GGUF, GGML, etc.) | 6+ (GGUF, GGML, etc.) | |
| Monthly Operating Cost(USD) | $0 | $0 | |
| Token Context Window (Best Model)(tokens) | 200,000 (Llama 2 70B via Ollama) | 200,000 (Llama 2 70B via Ollama) | |
| Largest Local Model Size(GB) | 110+ (Llama 2 405B) | 110+ (Llama 2 405B) | |
| Supported Models Count(models) | 100+ open-source models available | 100+ open-source models available | |
| Typical Code Generation Quality (Subjective Rating)(1-10 scale) | 7.1 (best open-source models) | 7.1 (best open-source models) |
Sourced from publicly available data ·
Key Differences
7 attributes compared head-to-head
C
3ContinueEvenly matched1 tie
Continue
Attribute
Ollama
- IDE coding assistant with LLM integrationPrimary PurposeLocal LLM runtime and model management
- Simple extension install (5 minutes)(winner)Setup ComplexityDownload + model installation (10-30 minutes)
- Requires cloud LLM or local Ollama backendOffline CapabilityFully offline with local models(winner)
- VS Code, JetBrains IDEs (IntelliJ, PyCharm, etc.)(winner)Supported IDEsCLI tool, works with any IDE via API
- Claude 3, GPT-4, Gemini, custom LM Studio serversModel FlexibilityLlama 2, Mistral, Neural Chat, 100+ open models(winner)
- Minimal (extension only, uses remote compute)(winner)Hardware Requirements8GB+ RAM recommended; 16GB+ for optimal performance
- Depends on LLM choice; proprietary models send data to vendorsPrivacy/Data Handling100% local processing, zero data transmission(winner)
- Primary Purpose
Continue
IDE coding assistant with LLM integration
Ollama
Local LLM runtime and model management
- Setup Complexity
Continue
Simple extension install (5 minutes)(winner)
Ollama
Download + model installation (10-30 minutes)
- Offline Capability
Continue
Requires cloud LLM or local Ollama backend
Ollama
Fully offline with local models(winner)
- Supported IDEs
Continue
VS Code, JetBrains IDEs (IntelliJ, PyCharm, etc.)(winner)
Ollama
CLI tool, works with any IDE via API
- Model Flexibility
Continue
Claude 3, GPT-4, Gemini, custom LM Studio servers
Ollama
Llama 2, Mistral, Neural Chat, 100+ open models(winner)
- Hardware Requirements
Continue
Minimal (extension only, uses remote compute)(winner)
Ollama
8GB+ RAM recommended; 16GB+ for optimal performance
- Privacy/Data Handling
Continue
Depends on LLM choice; proprietary models send data to vendors
Ollama
100% local processing, zero data transmission(winner)
Full Comparison
| Attribute | Continue | Ollama |
|---|---|---|
| Initial Setup Time(minutes) | 10-20 (API key + config required) | 2-3 minutes(winner) |
| Setup Time to First Use(minutes) | 15-30 minutes (model download + config) | — |
| Free Tier Autocomplete Limit(requests/month) | Unlimited with local models | — |
| Autocomplete Latency(milliseconds) | 200-500ms average | — |
| Context Window Size(tokens) | Up to 100,000+ tokens | — |
| Code Context Window(tokens) | 8000-200000 (model-dependent) | — |
| Real-time Suggestion Speed(ms latency) | 400-800 | — |
| Code Completion Latency(milliseconds) | 800-1200 | — |
Show 25 more attributesTime to First Response (Small Prompt)(seconds) 2-5 sec (Claude/GPT-4) 15-45 sec (CPU), 3-8 sec (GPU) Average Code Completion Time(seconds) 5-15 seconds (hardware-dependent) — Avg Code Completion Speed(seconds) 2 — Runtime Memory Usage (Idle)(MB) 300-500 MB 50-200 MB Code Completion Acceptance Rate(%) 75-82% — Inference Speed (Llama 2 7B)(tokens/second) 120-200 (with cloud LLMs like Claude) 30-60 (local on mid-range hardware) Code Generation Accuracy (HumanEval Benchmark)(%) 68% (Llama 2 70B) — Average Response Latency(seconds) 5-10s (CPU) / 2-4s (GPU) — 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% — Average Inference Latency(milliseconds) 200-5000ms (hardware dependent) — Maximum Throughput(events per second) 1-10 (single device) — Inference Throughput (RTX 4090, Llama 2 13B)(tokens/second) ~175 tokens/sec — Time to First Response (after setup)(seconds) 5-30 seconds (varies by hardware/model) — Typical Response Quality (Reasoning Tasks)(null) Good for general tasks; weaker on complex reasoning (88% MMLU benchmark score) — Startup Time (7B Model)(seconds) 3-5 seconds — Token Context Window (Best Model)(tokens) 200,000 (Llama 2 70B via Ollama) — Typical Code Generation Quality (Subjective Rating)(1-10 scale) 7.1 (best open-source models) — | ||
| Supported IDEs Count(IDEs) | VS Code, JetBrains suite, Vim, Neovim (4 major platforms) | — |
| Supported IDE Count(IDEs) | 5 major IDEs | — |
| Number of Supported IDEs(count) | 4 | — |
| IDE Platform Support Count(platforms) | 4 major platforms (VS Code, JetBrains, Vim, Neovim) | — |
| Supported IDE Platforms(count) | 6+ | — |
Show 5 more attributesIDE Support Count(IDEs) 1 (VS Code) — IDE Integrations(count) 4 major (VS Code, JetBrains, Vim, Neovim) — Multi-Platform Support(platforms) 3 (macOS, Linux, Windows) — Supported Programming Languages(count) 50+ languages — Number of Supported GPU Backends(count) 4 (CPU, Metal, CUDA, Vulkan) — | ||
| Paid Plan Monthly Cost(USD) | Free (optional donations for commercial use) | — |
| Base Cost (Monthly)(USD) | $0 (self-hosted) | — |
| Free Tier Code Completions(requests/month) | Unlimited (depends on API usage) | — |
| Monthly Cost (Individual)(USD) | Free (+ API costs) | — |
| Base Monthly Cost(USD) | Free | — |
Show 13 more attributesFree Tier Monthly Limit(completions/month) Unlimited (fully free and open-source) — Pro Plan Monthly Cost(USD) $0 (open-source) or hosted option available — Monthly Cost(USD) $0 — Annual Cost(USD) $0 — Cost (Base Usage)(USD/month) $0 (free + optional API costs) $0 (fully free) Monthly Subscription Cost(USD) $0 — Annual Cost per Developer(USD) $0 — Subscription Cost (Monthly)(USD) Free — Monthly Cost (Single LLM)(USD) $20 (Claude 3) $0 (free open models) Cost (Monthly Usage Example)(USD) $0 (free) — Base Cost(USD/month (for typical usage)) $0 (Free) — Free Tier Request Limit(requests/month) Unlimited (local only) — Base Monthly Cost (100M tokens usage)(USD) $0 (free) — | ||
| Programming Languages Supported(languages) | 50+ (with LLM-dependent support) | — |
| AI Model Choices(models) | Claude, GPT-4, Llama, Mistral, local | — |
| Supported AI Models(count) | 6+ (Claude, GPT-4, Ollama, local) | — |
| IDE Integration | Native VS Code extension | Requires external plugins/API setup |
| LLM Provider Options | 15+ providers including premium APIs | 100+ open-source models (single source)(winner) |
Show 15 more attributesSupported LLM Backends(count) 15+ models — LLM Model Flexibility(models supported) 4+ (Claude, Llama, GPT-4, Mistral) — Offline Capability(text) Full offline with local models Full offline operation Supported Models(count) 100+ models — Model Auto-Download Manual CLI required — Autonomous Code File Editing(yes/no) No (suggestions only) — LoRA Fine-tuning Not supported — Model Merging Not supported — Multimodal Capabilities (Vision, Image Gen) Limited; vision support emerging in some models — Batch Processing Support(null) No (sequential only) — Multimodal Capabilities (Image/Audio)(null) Limited—basic vision models available — Supported Quantization Formats(count) 6+ (GGUF, GGML, etc.) — Built-in Chat History(null) Not included — Offline Functionality Full (all models run offline) — Git Integration(null) None — | ||
| Data Processing Location | Local (on-device) | — |
| 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) | — |
Show 1 more attributeData Transmission No external data transmission (100% offline) — | ||
| GitHub Stars (as of 2026)(thousands) | 13,000+ | ~18,000(winner) |
| Customization via Config | Full JSON config (prompts, model params, shortcuts) | — |
| AI Provider Options(count) | 10+ | — |
| Model Selection Options(count) | 8+ (Claude, GPT-4, Llama, Mistral, local models) | — |
| AI Model Options(count) | 5+ (Claude, GPT-4, Llama 2, custom, local) | — |
| IDE Support | VS Code, IntelliJ, PyCharm, CLion, Rider, WebStorm | CLI-based; requires manual IDE plugin integration |
| IDE Compatibility(count) | 5+ (VS Code, JetBrains, Vim) | — |
| IDE Integration Support | 3 major editors (VS Code, JetBrains, Vim)(winner) | None (CLI/API only) |
| REST API Support(yes/no) | Yes (native) | — |
| Native REST API Support | Yes (OpenAI-compatible /v1 endpoints) | — |
Show 2 more attributesAPI Standardization(null) Custom REST endpoints — API Compatibility OpenAI-compatible REST API — | ||
| Estimated Active Users(thousands) | 150 | — |
| GitHub Stars (Community Adoption)(count) | 19,000 stars | — |
| Community Contributors(count) | 10,000+ GitHub stars, active Discord | — |
| User Base Size(millions of developers) | ~0.05 million (2025 estimate) | — |
| Transformers Library Downloads (weekly)(downloads) | Not applicable (CLI tool) | — |
| Enterprise SLA Support(boolean) | No (community-driven) | — |
| Enterprise SSO Authentication(supported) | No | — |
| Enterprise SSO Support | No | — |
| Open Source Availability | 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(month/year) | 2024 | — |
| Monthly Cost at Heavy Usage(USD) | $50-150 for power users | $0 after hardware(winner) |
| Base Pricing (Monthly)(USD) | Free + $20 Pro | — |
| Pricing (Base Tier)(USD/month) | Free (open-source) | — |
| Enterprise Plan Cost(USD/month per user) | Self-hosted (variable) | — |
| Monthly Operating Cost (5,000 token average session)(USD) | $0 (hardware only) | — |
| Available Models(count) | 10+ providers supported | 15+ models(winner) |
| Internet Dependency(text) | Required for cloud models | Not required after setup |
| Internet Connectivity Required | Only for initial model download; runs offline after | — |
| Minimum RAM Requirement(GB) | 2-4 GB (extension only)(winner) | 8-16 GB (for quality inference) |
| Minimum Hardware RAM Required(GB) | 8GB (Llama 2 7B) | — |
| Minimum Recommended RAM(GB) | 32GB (for optimal performance) | — |
| GPU Support Types(null) | NVIDIA, AMD, Intel (manual setup) | — |
| Data Privacy Level(null) | Depends on provider, some cloud processing | 100% local—zero external data transmission |
| Data Privacy Model | Local-only, zero cloud transmission | — |
| Privacy Level (0=cloud-only, 100=fully local)(score) | 80 (optional local via Ollama backend) | 100 (always local)(winner) |
| Data Privacy (Local Execution)(text) | Fully local option available | — |
| Data Privacy Mode(text) | No data transmission with local setup | — |
Show 1 more attributeData Privacy (0=external servers, 1=local only)(privacy score) 1 (local) — | ||
| Open-Source | Yes (GitHub public repository) | — |
| Maximum Context Window(tokens) | 200,000 | — |
| Largest Available Model(parameters (billions)) | 70B (Llama 2) | — |
| Maximum Model Parameter Size(billion parameters) | 70B (Mixtral 8x22B) | — |
| Training Data Size(repositories) | Varies by provider | — |
| Minimum Installation Time(minutes) | 2 minutes (IDE extension)(winner) | 5-15 minutes (install + model download) |
| Installation Complexity(steps required) | Medium (CLI setup required) | — |
| API Documentation Quality | Comprehensive with config examples | Extensive REST API documentation |
| Setup Time(minutes) | 15-30 (with model download)(winner) | 15-30 minutes |
| OpenAI API Compatibility(boolean) | Full native support | — |
| Setup Time to First Inference(minutes) | 8-10 (including model download) | — |
| Project Launch Year(year) | 2024 | — |
| GitHub Stars(stars) | 500+ | 100,000+(winner) |
| Setup Complexity | High (manual LLM configuration required) | — |
| Setup Time (First Use)(minutes) | 15-30 minutes (download, install, configure) | — |
| Installation Time (from zero)(minutes) | 3-5 minutes | — |
| Minimum Setup Time(minutes) | 30-120 minutes | — |
| Data Privacy | Depends on LLM vendor (Claude, OpenAI, Google store data per ToS) | 100% local, zero external transmission |
| Available LLMs | GPT-4, Claude 3, Gemini, custom servers | 100+ open-source: Llama 2, Mistral, Neural Chat, Orca, Zephyr |
| Enterprise Customer Base(count) | ~50-100 (estimated) | — |
| User Interface Type | Command-line (CLI) | — |
| User Interface | Command-line interface | — |
| Graphical User Interface | No (CLI only) | — |
| Setup Time (from download to first inference)(minutes) | 5 minutes | — |
| Latest Release Year | 2024 | — |
| Latest Release Activity | Weekly updates (as of 2026) | — |
| Minimum Hardware to Run(GB RAM) | 4GB (minimum); 8GB recommended | — |
| Minimum RAM Required(GB) | 4 GB (with offloading) | — |
| 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) | — |
| Monthly Operating Cost(USD) | $0 | — |
| Minimum Hardware Requirements(GB RAM) | 8GB RAM + 4GB GPU (Llama 7B) | — |
| GPU Memory for 7B Model(GB) | 6-8 GB (fp16) | — |
| Minimum GPU Memory (7B LLM)(GB) | 4-6GB | — |
| Minimum VRAM for Llama 2 7B(GB) | 4 GB | — |
| Largest Local Model Size(GB) | 110+ (Llama 2 405B) | — |
| Pre-packaged Models Available(count) | 20,000+ (registry) | — |
| Number of Available Models(models) | 200+ open-source models | — |
| Supported Models Count(models) | 100+ open-source models available | — |
| Installation Size(MB) | ~150 MB | — |
| CPU Fallback Support(capability) | Full support with graceful degradation | — |
| Commercial Support SLA(availability %) | Community-only (none) | — |
| Available Pre-trained Models(count) | 200+ | — |
| Community Features | Model registry only, 0 community features | — |
| Download Size(MB) | 450 MB | — |
| Memory Usage (Llama 2 7B, quantized)(GB) | 4-5 GB | — |
| Base Installation Size(MB) | 50-100 MB | — |
| Available Models in Official Registry(models) | 80+ models | — |
CContinue
OllamaInitial Setup Time(minutes)
10-20 (API key + config required)
2-3 minutes
Setup Time to First Use(minutes)
15-30 minutes (model download + config)
—
Free Tier Autocomplete Limit(requests/month)
Unlimited with local models
—
Autocomplete Latency(milliseconds)
200-500ms average
—
Context Window Size(tokens)
Up to 100,000+ tokens
—
Code Context Window(tokens)
8000-200000 (model-dependent)
—
Real-time Suggestion Speed(ms latency)
400-800
—
Code Completion Latency(milliseconds)
800-1200
—
Show 25 more attributes
Time to First Response (Small Prompt)(seconds)
2-5 sec (Claude/GPT-4)
15-45 sec (CPU), 3-8 sec (GPU)
Average Code Completion Time(seconds)
5-15 seconds (hardware-dependent)
—
Avg Code Completion Speed(seconds)
2
—
Runtime Memory Usage (Idle)(MB)
300-500 MB
50-200 MB
Code Completion Acceptance Rate(%)
75-82%
—
Inference Speed (Llama 2 7B)(tokens/second)
120-200 (with cloud LLMs like Claude)
30-60 (local on mid-range hardware)
Code Generation Accuracy (HumanEval Benchmark)(%)
68% (Llama 2 70B)
—
Average Response Latency(seconds)
5-10s (CPU) / 2-4s (GPU)
—
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%
—
Average Inference Latency(milliseconds)
200-5000ms (hardware dependent)
—
Maximum Throughput(events per second)
1-10 (single device)
—
Inference Throughput (RTX 4090, Llama 2 13B)(tokens/second)
~175 tokens/sec
—
Time to First Response (after setup)(seconds)
5-30 seconds (varies by hardware/model)
—
Typical Response Quality (Reasoning Tasks)(null)
Good for general tasks; weaker on complex reasoning (88% MMLU benchmark score)
—
Startup Time (7B Model)(seconds)
3-5 seconds
—
Token Context Window (Best Model)(tokens)
200,000 (Llama 2 70B via Ollama)
—
Typical Code Generation Quality (Subjective Rating)(1-10 scale)
7.1 (best open-source models)
—
Supported IDEs Count(IDEs)
VS Code, JetBrains suite, Vim, Neovim (4 major platforms)
—
Supported IDE Count(IDEs)
5 major IDEs
—
Number of Supported IDEs(count)
4
—
IDE Platform Support Count(platforms)
4 major platforms (VS Code, JetBrains, Vim, Neovim)
—
Supported IDE Platforms(count)
6+
—
Show 5 more attributes
IDE Support Count(IDEs)
1 (VS Code)
—
IDE Integrations(count)
4 major (VS Code, JetBrains, Vim, Neovim)
—
Multi-Platform Support(platforms)
3 (macOS, Linux, Windows)
—
Supported Programming Languages(count)
50+ languages
—
Number of Supported GPU Backends(count)
4 (CPU, Metal, CUDA, Vulkan)
—
Paid Plan Monthly Cost(USD)
Free (optional donations for commercial use)
—
Base Cost (Monthly)(USD)
$0 (self-hosted)
—
Free Tier Code Completions(requests/month)
Unlimited (depends on API usage)
—
Monthly Cost (Individual)(USD)
Free (+ API costs)
—
Base Monthly Cost(USD)
Free
—
Show 13 more attributes
Free Tier Monthly Limit(completions/month)
Unlimited (fully free and open-source)
—
Pro Plan Monthly Cost(USD)
$0 (open-source) or hosted option available
—
Monthly Cost(USD)
$0
—
Annual Cost(USD)
$0
—
Cost (Base Usage)(USD/month)
$0 (free + optional API costs)
$0 (fully free)
Monthly Subscription Cost(USD)
$0
—
Annual Cost per Developer(USD)
$0
—
Subscription Cost (Monthly)(USD)
Free
—
Monthly Cost (Single LLM)(USD)
$20 (Claude 3)
$0 (free open models)
Cost (Monthly Usage Example)(USD)
$0 (free)
—
Base Cost(USD/month (for typical usage))
$0 (Free)
—
Free Tier Request Limit(requests/month)
Unlimited (local only)
—
Base Monthly Cost (100M tokens usage)(USD)
$0 (free)
—
Programming Languages Supported(languages)
50+ (with LLM-dependent support)
—
AI Model Choices(models)
Claude, GPT-4, Llama, Mistral, local
—
Supported AI Models(count)
6+ (Claude, GPT-4, Ollama, local)
—
IDE Integration
Native VS Code extension
Requires external plugins/API setup
LLM Provider Options
15+ providers including premium APIs
100+ open-source models (single source)
Show 15 more attributes
Supported LLM Backends(count)
15+ models
—
LLM Model Flexibility(models supported)
4+ (Claude, Llama, GPT-4, Mistral)
—
Offline Capability(text)
Full offline with local models
Full offline operation
Supported Models(count)
100+ models
—
Model Auto-Download
Manual CLI required
—
Autonomous Code File Editing(yes/no)
No (suggestions only)
—
LoRA Fine-tuning
Not supported
—
Model Merging
Not supported
—
Multimodal Capabilities (Vision, Image Gen)
Limited; vision support emerging in some models
—
Batch Processing Support(null)
No (sequential only)
—
Multimodal Capabilities (Image/Audio)(null)
Limited—basic vision models available
—
Supported Quantization Formats(count)
6+ (GGUF, GGML, etc.)
—
Built-in Chat History(null)
Not included
—
Offline Functionality
Full (all models run offline)
—
Git Integration(null)
None
—
Data Processing Location
Local (on-device)
—
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)
—
Show 1 more attribute
Data Transmission
No external data transmission (100% offline)
—
GitHub Stars (as of 2026)(thousands)
13,000+
~18,000
Customization via Config
Full JSON config (prompts, model params, shortcuts)
—
AI Provider Options(count)
10+
—
Model Selection Options(count)
8+ (Claude, GPT-4, Llama, Mistral, local models)
—
AI Model Options(count)
5+ (Claude, GPT-4, Llama 2, custom, local)
—
IDE Support
VS Code, IntelliJ, PyCharm, CLion, Rider, WebStorm
CLI-based; requires manual IDE plugin integration
IDE Compatibility(count)
5+ (VS Code, JetBrains, Vim)
—
IDE Integration Support
3 major editors (VS Code, JetBrains, Vim)
None (CLI/API only)
REST API Support(yes/no)
Yes (native)
—
Native REST API Support
Yes (OpenAI-compatible /v1 endpoints)
—
Show 2 more attributes
API Standardization(null)
Custom REST endpoints
—
API Compatibility
OpenAI-compatible REST API
—
Estimated Active Users(thousands)
150
—
GitHub Stars (Community Adoption)(count)
19,000 stars
—
Community Contributors(count)
10,000+ GitHub stars, active Discord
—
User Base Size(millions of developers)
~0.05 million (2025 estimate)
—
Transformers Library Downloads (weekly)(downloads)
Not applicable (CLI tool)
—
Enterprise SLA Support(boolean)
No (community-driven)
—
Enterprise SSO Authentication(supported)
No
—
Enterprise SSO Support
No
—
Open Source Availability
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(month/year)
2024
—
Monthly Cost at Heavy Usage(USD)
$50-150 for power users
$0 after hardware
Base Pricing (Monthly)(USD)
Free + $20 Pro
—
Pricing (Base Tier)(USD/month)
Free (open-source)
—
Enterprise Plan Cost(USD/month per user)
Self-hosted (variable)
—
Monthly Operating Cost (5,000 token average session)(USD)
$0 (hardware only)
—
Available Models(count)
10+ providers supported
15+ models
Internet Dependency(text)
Required for cloud models
Not required after setup
Internet Connectivity Required
Only for initial model download; runs offline after
—
Minimum RAM Requirement(GB)
2-4 GB (extension only)
8-16 GB (for quality inference)
Minimum Hardware RAM Required(GB)
8GB (Llama 2 7B)
—
Minimum Recommended RAM(GB)
32GB (for optimal performance)
—
GPU Support Types(null)
NVIDIA, AMD, Intel (manual setup)
—
Data Privacy Level(null)
Depends on provider, some cloud processing
100% local—zero external data transmission
Data Privacy Model
Local-only, zero cloud transmission
—
Privacy Level (0=cloud-only, 100=fully local)(score)
80 (optional local via Ollama backend)
100 (always local)
Data Privacy (Local Execution)(text)
Fully local option available
—
Data Privacy Mode(text)
No data transmission with local setup
—
Show 1 more attribute
Data Privacy (0=external servers, 1=local only)(privacy score)
1 (local)
—
Open-Source
Yes (GitHub public repository)
—
Maximum Context Window(tokens)
200,000
—
Largest Available Model(parameters (billions))
70B (Llama 2)
—
Maximum Model Parameter Size(billion parameters)
70B (Mixtral 8x22B)
—
Training Data Size(repositories)
Varies by provider
—
Minimum Installation Time(minutes)
2 minutes (IDE extension)
5-15 minutes (install + model download)
Installation Complexity(steps required)
Medium (CLI setup required)
—
API Documentation Quality
Comprehensive with config examples
Extensive REST API documentation
Setup Time(minutes)
15-30 (with model download)
15-30 minutes
OpenAI API Compatibility(boolean)
Full native support
—
Setup Time to First Inference(minutes)
8-10 (including model download)
—
Project Launch Year(year)
2024
—
GitHub Stars(stars)
500+
100,000+
Setup Complexity
High (manual LLM configuration required)
—
Setup Time (First Use)(minutes)
15-30 minutes (download, install, configure)
—
Installation Time (from zero)(minutes)
3-5 minutes
—
Minimum Setup Time(minutes)
30-120 minutes
—
Data Privacy
Depends on LLM vendor (Claude, OpenAI, Google store data per ToS)
100% local, zero external transmission
Available LLMs
GPT-4, Claude 3, Gemini, custom servers
100+ open-source: Llama 2, Mistral, Neural Chat, Orca, Zephyr
Enterprise Customer Base(count)
~50-100 (estimated)
—
User Interface Type
Command-line (CLI)
—
User Interface
Command-line interface
—
Graphical User Interface
No (CLI only)
—
Setup Time (from download to first inference)(minutes)
5 minutes
—
Latest Release Year
2024
—
Latest Release Activity
Weekly updates (as of 2026)
—
Minimum Hardware to Run(GB RAM)
4GB (minimum); 8GB recommended
—
Minimum RAM Required(GB)
4 GB (with offloading)
—
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)
—
Monthly Operating Cost(USD)
$0
—
Minimum Hardware Requirements(GB RAM)
8GB RAM + 4GB GPU (Llama 7B)
—
GPU Memory for 7B Model(GB)
6-8 GB (fp16)
—
Minimum GPU Memory (7B LLM)(GB)
4-6GB
—
Minimum VRAM for Llama 2 7B(GB)
4 GB
—
Largest Local Model Size(GB)
110+ (Llama 2 405B)
—
Pre-packaged Models Available(count)
20,000+ (registry)
—
Number of Available Models(models)
200+ open-source models
—
Supported Models Count(models)
100+ open-source models available
—
Installation Size(MB)
~150 MB
—
CPU Fallback Support(capability)
Full support with graceful degradation
—
Commercial Support SLA(availability %)
Community-only (none)
—
Available Pre-trained Models(count)
200+
—
Community Features
Model registry only, 0 community features
—
Download Size(MB)
450 MB
—
Memory Usage (Llama 2 7B, quantized)(GB)
4-5 GB
—
Base Installation Size(MB)
50-100 MB
—
Available Models in Official Registry(models)
80+ models
—
Pros & Cons
10 pros·6 cons across both
C
C
Continue
+5-3
Pros
- Seamless VS Code and JetBrains IDE integration with native UI
- Access to top proprietary models: GPT-4, Claude 3, Gemini with single auth
- Quick setup: install extension and authenticate (5 minutes)
- Chat, autocomplete, and code editing all in-editor
- Custom LM Studio and local server support for flexibility
Cons
- Requires API keys/subscriptions for proprietary LLMs (Claude 3: $20/mo, GPT-4: $20/mo)
- Data sent to external LLM providers unless configured with local backend
- Inferior performance on local Ollama models vs. cloud alternatives
Ollama
+5-3
Pros
- 100% offline and free: run Llama 2, Mistral, and 100+ models at zero cost
- Complete data privacy: all processing stays on user's machine
- Minimal dependencies: single 50MB+ download, simple CLI commands
- Low resource overhead: models run efficiently with quantization
- REST API allows integration with any IDE or application
Cons
- Requires 8-16GB RAM; slower inference than cloud LLMs (Llama 2 7B: 50-150 tokens/sec vs. GPT-4: 100-200 tokens/sec)
- No native IDE integration; requires manual setup with Continue or third-party clients
- Steeper learning curve for non-technical users: CLI-based operation
Frequently Asked Questions
5 questions
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.
Resources & Learn More
Curated sources to dive deeper
Where to Buy
As an affiliate, we may earn a commission from qualifying purchases at no extra cost to you. Learn more about our affiliate disclosure
Wikipedia
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