software
Continue vs Ollama 2026: IDE AI Coding vs Local LLM
Continue is an IDE extension for AI-assisted coding across multiple LLMs, while Ollama is a lightweight runtime for running open-source LLMs locally on personal devices. Continue requires an editor integration, whereas Ollama is a standalone service for local model execution.
C
Continue
Open-source IDE extension providing AI-assisted coding across multiple LLM providers.
Developers who want seamless AI coding assistance across multiple models and editors without leaving their IDE.
Score71%
VS
Ollama
Lightweight CLI tool for running open-source large language models locally on CPU or GPU.
Privacy-conscious developers, researchers, and users who want to run LLMs offline without cloud dependencies or IDE overhead.
Score63%
Quick Answer
AI SummaryContinue is an IDE extension for AI-assisted coding across multiple LLMs, while Ollama is a lightweight runtime for running open-source LLMs locally on personal devices. Continue requires an editor integration, whereas Ollama is a standalone service for local model execution.
Our Verdict
AI-assistedChoose Continue if you want a feature-rich, editor-integrated coding assistant with access to multiple premium and open-source LLMs through a single interface. Choose Ollama if you prioritize complete privacy, local-only execution, minimal resource usage, or need a lightweight API server for running open-source models in production or personal projects.
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C
Continue
6.8/10
Ollama
8.2/10
C
Choose Continue if
Developers who want seamless AI coding assistance across multiple models and editors without leaving their IDE.
Choose Ollama if
Best pickPrivacy-conscious developers, researchers, and users who want to run LLMs offline without cloud dependencies or IDE overhead.
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Key Differences at a Glance
- Primary Purpose:IDE-integrated coding copilot vs Local LLM runtime engine
- Supported Editors:✓ Continue wins(VS Code, JetBrains IDEs, Vim vs None (CLI/API only))
- Model Sources:✓ Continue wins(OpenAI, Claude, Gemini, Ollama, LocalAI vs Hugging Face models only)
Key Facts & Figures
80 numeric metrics compared
| Metric | Continue | Ollama | Ratio |
|---|---|---|---|
| Initial Setup Time(hours) | 10-20 (API key + config required) | 2-3 minutes | |
| Autocomplete Latency(milliseconds) | 200-500ms average | — | — |
| Context Window Size(K 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(integrations) | 3 (VSCode, JetBrains, Cursor) | — | — |
| GitHub Stars (as of 2026)(stars) | 13,000+ | 65,000+ | |
| Monthly Cost (Individual)(USD) | Free (+ API costs) | — | — |
| AI Model Options(models) | 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 | 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) | — | — |
| 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) | 8GB minimum (16GB recommended) | 8 GB minimum | |
| 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/month) | $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) | |
| 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(milliseconds) | 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 | |
| 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(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 | |
| 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(requests/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(count) | Model registry only, 0 community features | Model registry only, 0 community features | |
| Download Size(MB) | 450 MB | 450 MB |
Sourced from publicly available data ·
Key Differences
7 attributes compared head-to-head
C
3ContinueEvenly matched1 tie
Continue
Attribute
Ollama
- IDE-integrated coding copilotPrimary PurposeLocal LLM runtime engine
- VS Code, JetBrains IDEs, Vim(winner)Supported EditorsNone (CLI/API only)
- OpenAI, Claude, Gemini, Ollama, LocalAI(winner)Model SourcesHugging Face models only
- Optional via Ollama backendLocal ExecutionPrimary function(winner)
- 200-500 MB IDE extensionMemory Footprint (Typical Setup)50-200 MB runtime + model size(winner)
- Yes, when using Ollama backendPrivacy Mode (Zero Cloud Calls)Yes, all processing local(winner)
- Single extension install (2 minutes)(winner)Installation ComplexityCLI install + model download (5-15 minutes)
- Primary Purpose
Continue
IDE-integrated coding copilot
Ollama
Local LLM runtime engine
- Supported Editors
Continue
VS Code, JetBrains IDEs, Vim(winner)
Ollama
None (CLI/API only)
- Model Sources
Continue
OpenAI, Claude, Gemini, Ollama, LocalAI(winner)
Ollama
Hugging Face models only
- Local Execution
Continue
Optional via Ollama backend
Ollama
Primary function(winner)
- Memory Footprint (Typical Setup)
Continue
200-500 MB IDE extension
Ollama
50-200 MB runtime + model size(winner)
- Privacy Mode (Zero Cloud Calls)
Continue
Yes, when using Ollama backend
Ollama
Yes, all processing local(winner)
- Installation Complexity
Continue
Single extension install (2 minutes)(winner)
Ollama
CLI install + model download (5-15 minutes)
Full Comparison
| Attribute | Continue | Ollama |
|---|---|---|
| Setup Time(minutes) | 5-10 minutes(winner) | 15-30 (CLI, GPU setup) |
| Setup Time (First Use)(minutes) | 15-30 minutes (download, install, configure) | — |
| Initial Setup Time(hours) | 10-20 (API key + config required) | 2-3 minutes(winner) |
| 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 8 more attributesFree Tier Monthly Limit(completions/month) Unlimited (fully free and open-source) — Pro Plan Monthly Cost(USD/month) $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) 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) — | ||
| 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)(winner) | 15-45 sec (CPU), 3-8 sec (GPU) |
Show 16 more attributesMinimum RAM Requirement(GB) 8GB minimum (16GB recommended) 8 GB minimum 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 Generation Accuracy (HumanEval Benchmark)(%) 68% (Llama 2 70B) — 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 — 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 — Average Inference Latency(milliseconds) 200-5000ms (hardware dependent) — | ||
| Context Window Size(K tokens) | Up to 100,000+ tokens | — |
| Supported IDEs Count(IDEs) | VS Code, JetBrains suite, Vim, Neovim (4 major platforms) | — |
| Supported IDE Count(integrations) | 3 (VSCode, JetBrains, Cursor) | — |
| 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 1 more attributeSupported Quantization Formats(count) 1 (GGUF) — | ||
| Programming Languages Supported(count) | 50+ (with LLM-dependent support) | — |
| Commercial Support SLA(availability %) | Community-only (none) | — |
| AI Model Choices(models) | Claude, GPT-4, Llama, Mistral, local | — |
| Available Models(count) | 10+ providers supported | 15+ models(winner) |
| LLM Provider Options | 15+ providers including premium APIs | 100+ open-source models (single source)(winner) |
| Supported Programming Languages(count) | 50+ languages | — |
| Autonomous Code File Editing(yes/no) | No (suggestions only) | — |
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 + optional cloud | — |
| 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)(stars) | 13,000+ | 65,000+(winner) |
| 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 | 6+ (Claude, GPT-4, Ollama, local) | — |
| AI Provider Options(count) | 10+ | — |
| AI Model Options(models) | 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) | — |
| 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) | — |
| Enterprise SLA Support(boolean) | No (community-driven) | — |
| Setup Complexity(complexity score) | 15–30 min (API key configuration) | — |
| Enterprise SSO Authentication(supported) | 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) | — |
| Maximum Throughput(requests/second) | 1-10 (single device) | — |
| Training Data Cutoff(year) | 2024 | — |
| Monthly Cost at Heavy Usage(USD) | $50-150 for power users | $0 after hardware(winner) |
| Base Pricing (Monthly)(USD) | Free + $20 Pro | — |
| Monthly Operating Cost (5,000 token average session)(USD) | $0 (hardware only) | — |
| Internet Dependency(text) | Required for cloud models | Not required after setup |
| IDE Integration | Native VS Code extension | Requires external plugins/API setup |
| Data Privacy Level(percentage local) | Depends on provider, some cloud processing | 100% (on-device) |
| 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 (0=external servers, 1=local only)(privacy score) | 1 (local) | — |
| Setup Time to First Use(minutes) | 15-30 minutes (model download + config) | — |
| Open Source | Yes (GitHub public repository) | — |
| Maximum Context Window(tokens) | 200,000 | — |
| Training Data Size(repositories) | Varies by provider | — |
| Minimum Installation Time(minutes) | 2 minutes (IDE extension)(winner) | 5-15 minutes (install + model download) |
| Installation Complexity(required steps) | Medium (CLI setup required) | — |
| API Documentation Quality | Comprehensive with config examples | Extensive REST API documentation |
| Minimum Hardware RAM Required(GB) | 8GB (Llama 2 7B) | — |
| Average Response Latency(milliseconds) | 5-10s (CPU) / 2-4s (GPU) | — |
| 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) | — |
| 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 | — |
| Idle Memory Usage(MB) | ~250 MB | — |
| GPU Memory for 7B Model(GB) | 6-8 GB (fp16) | — |
| Minimum GPU Memory (7B LLM)(GB) | 4-6GB | — |
| Pre-packaged Models Available(count) | 20,000+ (registry) | — |
| GitHub Stars(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 | — |
| Largest Available Model(parameters (billions)) | 70B (Llama 2) | — |
| Available Pre-trained Models(count) | 200+ | — |
| Community Features(count) | Model registry only, 0 community features | — |
| Download Size(MB) | 450 MB | — |
| Transformers Library Downloads (weekly)(downloads) | Not applicable (CLI tool) | — |
CContinue
OllamaSetup Time(minutes)
5-10 minutes
15-30 (CLI, GPU setup)
Setup Time (First Use)(minutes)
15-30 minutes (download, install, configure)
—
Initial Setup Time(hours)
10-20 (API key + config required)
2-3 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 8 more attributes
Free Tier Monthly Limit(completions/month)
Unlimited (fully free and open-source)
—
Pro Plan Monthly Cost(USD/month)
$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)
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)
—
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 16 more attributes
Minimum RAM Requirement(GB)
8GB minimum (16GB recommended)
8 GB minimum
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 Generation Accuracy (HumanEval Benchmark)(%)
68% (Llama 2 70B)
—
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
—
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
—
Average Inference Latency(milliseconds)
200-5000ms (hardware dependent)
—
Context Window Size(K tokens)
Up to 100,000+ tokens
—
Supported IDEs Count(IDEs)
VS Code, JetBrains suite, Vim, Neovim (4 major platforms)
—
Supported IDE Count(integrations)
3 (VSCode, JetBrains, Cursor)
—
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 1 more attribute
Supported Quantization Formats(count)
1 (GGUF)
—
Programming Languages Supported(count)
50+ (with LLM-dependent support)
—
Commercial Support SLA(availability %)
Community-only (none)
—
AI Model Choices(models)
Claude, GPT-4, Llama, Mistral, local
—
Available Models(count)
10+ providers supported
15+ models
LLM Provider Options
15+ providers including premium APIs
100+ open-source models (single source)
Supported Programming Languages(count)
50+ languages
—
Autonomous Code File Editing(yes/no)
No (suggestions only)
—
Show 4 more attributes
LoRA 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 + optional cloud
—
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)(stars)
13,000+
65,000+
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
6+ (Claude, GPT-4, Ollama, local)
—
AI Provider Options(count)
10+
—
AI Model Options(models)
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)
—
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)
—
Enterprise SLA Support(boolean)
No (community-driven)
—
Setup Complexity(complexity score)
15–30 min (API key configuration)
—
Enterprise SSO Authentication(supported)
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)
—
Maximum Throughput(requests/second)
1-10 (single device)
—
Training Data Cutoff(year)
2024
—
Monthly Cost at Heavy Usage(USD)
$50-150 for power users
$0 after hardware
Base Pricing (Monthly)(USD)
Free + $20 Pro
—
Monthly Operating Cost (5,000 token average session)(USD)
$0 (hardware only)
—
Internet Dependency(text)
Required for cloud models
Not required after setup
IDE Integration
Native VS Code extension
Requires external plugins/API setup
Data Privacy Level(percentage local)
Depends on provider, some cloud processing
100% (on-device)
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 (0=external servers, 1=local only)(privacy score)
1 (local)
—
Setup Time to First Use(minutes)
15-30 minutes (model download + config)
—
Open Source
Yes (GitHub public repository)
—
Maximum Context Window(tokens)
200,000
—
Training Data Size(repositories)
Varies by provider
—
Minimum Installation Time(minutes)
2 minutes (IDE extension)
5-15 minutes (install + model download)
Installation Complexity(required steps)
Medium (CLI setup required)
—
API Documentation Quality
Comprehensive with config examples
Extensive REST API documentation
Minimum Hardware RAM Required(GB)
8GB (Llama 2 7B)
—
Average Response Latency(milliseconds)
5-10s (CPU) / 2-4s (GPU)
—
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)
—
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
—
Idle Memory Usage(MB)
~250 MB
—
GPU Memory for 7B Model(GB)
6-8 GB (fp16)
—
Minimum GPU Memory (7B LLM)(GB)
4-6GB
—
Pre-packaged Models Available(count)
20,000+ (registry)
—
GitHub Stars(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
—
Largest Available Model(parameters (billions))
70B (Llama 2)
—
Available Pre-trained Models(count)
200+
—
Community Features(count)
Model registry only, 0 community features
—
Download Size(MB)
450 MB
—
Transformers Library Downloads (weekly)(downloads)
Not applicable (CLI tool)
—
Pros & Cons
10 pros·5 cons across both
C
C
Continue
+5-2
Pros
- Integrates with VS Code, JetBrains, and Vim with full code context awareness
- Supports 15+ LLM providers (OpenAI, Claude, Gemini, Ollama, LocalAI, Mistral)
- Native code completion, chat, and refactoring within IDE workflow
- Can route requests to Ollama for completely private inference
- Extensive customization via .continuerc configuration file
Cons
- Requires internet connection for cloud-based model providers (unless using Ollama backend)
- Adds 200-500 MB to IDE overhead depending on editor and plugins
Ollama
+5-3
Pros
- Extremely lightweight (50-200 MB runtime) with minimal system requirements
- Complete privacy—all inference happens locally with zero external API calls
- Supports 100+ open-source models (Llama 2, Mistral, Neural Chat, Phi, Deepseek)
- Simple one-command setup: ollama run mistral (downloads and runs model)
- Native REST API for integrating into custom applications or external tools
Cons
- No built-in IDE integration—requires terminal use or API setup with external tools
- Performance depends entirely on local hardware; slower inference on CPU-only machines
- Limited to Hugging Face/open-source models; no access to OpenAI or Claude
Frequently Asked Questions
5 questions
Yes. Continue can use Ollama as a free backend, allowing you to run open-source models like Mistral or Llama 2 entirely locally. You can also configure Continue to use other free options like LocalAI. However, if you want access to GPT-4 or Claude, you'll need paid API keys.
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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