| 1 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | — | $10/mo |
| Capabilities | 9 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Analyzes codebases across 8+ languages (TypeScript, Python, C#, Java, PHP, Go, Rust, C++) using a Rust-based core engine that performs AST parsing and structural analysis to identify recurring patterns, naming conventions, architectural styles, and anti-patterns. Returns pattern matches with statistical confidence scores derived from frequency analysis across the codebase, enabling AI assistants to understand project-specific conventions with quantified certainty rather than guessing.
Unique: Uses a hybrid Rust + TypeScript architecture where the Rust core engine performs performance-critical AST parsing and pattern matching across 8+ languages, while TypeScript interfaces expose results via MCP and CLI. This hybrid approach achieves both speed (Rust's memory efficiency for large codebases) and accessibility (Node.js ecosystem for distribution), unlike pure-JavaScript tools that struggle with large-scale analysis.
vs alternatives: Faster and more accurate than regex-based pattern detection because it uses proper AST parsing for structural awareness, and more accessible than language-specific linters because it works across 8+ languages with unified pattern detection logic.
Maintains a file-system-backed decision store (stored in .drift/ directory) that records architectural decisions, design choices, and conventions made across coding sessions. The memory system allows developers and AI assistants to query previous decisions via MCP, enabling context to persist across IDE restarts and multiple AI interactions without requiring manual re-explanation of project decisions.
Unique: Implements a persistent decision memory system that survives IDE restarts and multiple AI sessions by storing decisions in a local .drift/ directory, then exposes them via MCP tools that AI assistants can query. This is distinct from context-window-only solutions (like raw Claude conversations) because decisions are permanently stored and queryable, not ephemeral.
vs alternatives: Provides true session persistence unlike context-window-based approaches that lose decisions when conversations end, and requires no external infrastructure unlike cloud-based decision tracking systems.
Exposes Drift's pattern detection and decision memory capabilities as an MCP (Model Context Protocol) server that integrates directly into IDEs like VS Code and Cursor. The MCP server implements standard tool-calling interfaces allowing AI assistants running in the IDE to query codebase patterns and decisions without leaving the editor, with results automatically injected into the AI's context window for code generation.
Unique: Implements a native MCP server that exposes codebase intelligence as queryable tools, allowing AI assistants to call pattern detection and decision memory functions directly from the IDE. This is architecturally distinct from plugins that require custom IDE extensions because it uses the standardized MCP protocol, making it compatible with any MCP-supporting IDE and any AI model that supports tool calling.
vs alternatives: More seamless than manual context injection because queries happen automatically via MCP tool calling, and more portable than IDE-specific plugins because it uses the standardized MCP protocol that works across VS Code, Cursor, and future MCP clients.
Provides a command-line interface (drift init, drift scan, drift import, drift memory) that performs batch analysis of codebases without requiring IDE integration or cloud connectivity. The CLI invokes the Rust core engine to parse and analyze code, stores results in the local .drift/ directory, and outputs human-readable reports or JSON data for integration into CI/CD pipelines and automation workflows.
Unique: Provides a standalone CLI that doesn't require IDE integration or network connectivity, making it suitable for CI/CD pipelines and server environments. The CLI directly invokes the Rust core engine via native bindings, achieving performance comparable to the MCP server while remaining completely offline and scriptable.
vs alternatives: More suitable for CI/CD automation than IDE-only solutions because it's scriptable and offline, and faster than pure-JavaScript CLI tools because it uses Rust for performance-critical parsing operations.
Analyzes code structure using Abstract Syntax Trees (ASTs) for each supported language, enabling detection of language-specific conventions like naming patterns (camelCase vs snake_case), architectural styles (MVC, layered, modular), and language idioms. The Rust core engine maintains separate parsers for each language, allowing it to understand semantic structure beyond simple text matching and detect violations of language-specific best practices.
Unique: Uses proper AST parsing via language-specific parsers in the Rust core engine rather than regex or heuristic-based pattern matching, enabling structural awareness of code semantics. This allows detection of patterns that require understanding scope, type information, and control flow — not just text patterns.
vs alternatives: More accurate than regex-based pattern detection because it understands code structure, and more unified than running separate linters for each language because it provides consistent pattern detection across 8+ languages with a single tool.
Provides a drift import command that allows developers to import existing architectural decisions, patterns, and conventions from legacy documentation, previous analysis tools, or manual records into Drift's persistent memory system. This enables teams to bootstrap Drift with existing knowledge rather than starting from scratch, and facilitates migration from other codebase intelligence tools.
Unique: Provides a dedicated import mechanism that allows bootstrapping Drift's decision memory from external sources, enabling teams to preserve existing architectural knowledge when adopting Drift. This is distinct from tools that only detect patterns from scratch because it acknowledges that teams often have pre-existing documented decisions.
vs alternatives: Enables faster adoption than starting from scratch because teams can import existing decisions, and more flexible than tools that only auto-detect patterns because it allows manual decision curation and import.
Supports project-level configuration (via .driftrc or similar config files) that allows developers to customize which files/directories are analyzed, which patterns to detect, which languages to prioritize, and how to weight different pattern types. The configuration system integrates with .gitignore for automatic exclusion of ignored files, reducing noise and focusing analysis on relevant code.
Unique: Integrates with .gitignore for automatic file exclusion and supports project-level configuration files that allow fine-grained control over analysis scope and pattern detection priorities. This is distinct from tools with fixed analysis behavior because it allows teams to customize Drift for their specific architectural concerns.
vs alternatives: More flexible than tools with fixed analysis scope because configuration allows customization, and more convenient than manual file exclusion because .gitignore integration is automatic.
Implements a three-tier architecture where performance-critical operations (AST parsing, pattern matching, statistical analysis) run in Rust for speed and memory efficiency, while user-facing interfaces (CLI, MCP server, configuration handling) are implemented in TypeScript for rapid development and Node.js ecosystem access. Native bindings bridge the Rust core and TypeScript interfaces, enabling both performance and accessibility without sacrificing either.
Unique: Uses a deliberate hybrid architecture where Rust handles performance-critical parsing and analysis while TypeScript provides user-facing interfaces and MCP integration. This is architecturally distinct from pure-JavaScript tools (slower) and pure-Rust tools (less accessible) because it optimizes for both performance and developer experience.
vs alternatives: Faster than pure-JavaScript tools for large codebase analysis because Rust core handles parsing, and more accessible than pure-Rust tools because TypeScript interfaces integrate with Node.js ecosystem and MCP protocol.
+1 more capabilities
Generates single-line and multi-line code suggestions in real-time as developers type, using semantic indexing of the entire codebase to retrieve relevant type definitions, function signatures, and contextual patterns. The system maintains a 1M token context window (Pro/Team tiers) that enables suggestions informed by distant code definitions and cross-file dependencies, constructed via local codebase semantic search rather than simple token-based recency. Suggestions adapt to detected coding style on Pro/Team tiers through implicit pattern learning from recent edits.
Unique: 1M token context window with codebase-wide semantic indexing enables suggestions informed by distant code definitions and cross-file patterns, versus competitors (Copilot, Tabnine) that typically use fixed context windows (4K-32K tokens) or file-local context. Claimed 250ms latency suggests optimized retrieval pipeline, though indexing mechanism and performance at scale remain undisclosed.
vs alternatives: Larger context window than GitHub Copilot (8K-32K tokens) and faster latency than unnamed competitors (250ms vs 783ms claimed), enabling suggestions on large codebases with minimal typing delay; trade-off is cloud dependency and undisclosed free tier limitations.
Provides a separate chat interface supporting multiple LLM backends (GPT-4o, Claude 3.5 Sonnet, GPT-4, others) for conversational code assistance. Users attach files, reference recent edits, and trigger compiler diagnostic uploads; the system generates diffs and applies code changes directly to the editor. Model selection is per-conversation, and $5/month in credits (included in Pro/Team) covers external model API costs; overage pricing is undisclosed. Hotkey-driven workflow enables rapid context switching between inline completion and chat.
Unique: Multi-model chat interface with per-conversation model selection and integrated diff application, combined with compiler diagnostic auto-upload. Unlike Copilot Chat (single model per tier) or standalone ChatGPT, Supermaven Chat unifies multiple LLM backends in a single hotkey-driven workflow with direct editor integration for change application.
Supermaven scores higher at 71/100 vs drift at 34/100. drift leads on ecosystem, while Supermaven is stronger on adoption and quality.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
vs alternatives: Supports multiple LLM backends (GPT-4o, Claude 3.5 Sonnet) in one interface with included credits, whereas GitHub Copilot Chat is single-model per tier and requires separate ChatGPT subscription for model switching; trade-off is credit limits and unknown overage pricing.
Supermaven Chat can automatically upload compiler diagnostic messages (errors, warnings) alongside code context to provide error-aware suggestions and fixes. The mechanism is described as 'automatically uploading your code together with compiler diagnostic messages,' but specific language/compiler support and the upload trigger mechanism are undisclosed. This feature is Chat-only and not available in inline completion.
Unique: Automatic compiler diagnostic upload in Chat for error-aware suggestions, versus competitors (Copilot, Tabnine) that require manual error context or have limited diagnostic integration. Supermaven's approach reduces friction but with undisclosed language/compiler support.
vs alternatives: Automatic diagnostic upload reduces manual context-gathering compared to manual copy-paste; trade-off is undisclosed language support and unclear upload trigger mechanism.
Supermaven offers a 30-day free trial of the Pro tier ($10/month), providing full access to 1M token context window, largest model, style adaptation, and $5/month chat credits. No credit card is required to start the trial (implied), and trial conversion to paid is automatic after 30 days unless cancelled. Trial terms and auto-renewal policy are not explicitly detailed.
Unique: 30-day free trial of Pro tier with full feature access (1M context, largest model, chat credits), versus competitors (Copilot 2-month free trial, Tabnine free tier only) with different trial lengths and feature access. Supermaven's approach is generous but with undisclosed auto-renewal terms.
vs alternatives: Full Pro feature access during trial compared to limited free tier; trade-off is undisclosed auto-renewal policy and potential unexpected charges if not cancelled.
Supermaven requires internet connectivity and server-side inference; no offline mode or local inference capability is mentioned or available. All code completion requests are sent to Supermaven's backend servers for processing, and responses are returned over the network. This creates a hard dependency on network connectivity and Supermaven's service availability; if the service is down or network is unavailable, code completion is not available.
Unique: Supermaven has no offline mode or local inference capability; all processing is server-side. GitHub Copilot also requires server-side inference, but Tabnine offers local inference options for some use cases. Supermaven's lack of offline capability is a significant limitation for developers with connectivity constraints.
vs alternatives: Supermaven's server-side-only approach is comparable to GitHub Copilot; Tabnine offers local inference options, making Tabnine more suitable for offline work. Supermaven's lack of offline capability is a weakness vs. Tabnine.
Analyzes recent code edits and inferred coding patterns to adapt inline suggestions to match team conventions, naming patterns, and structural preferences. The mechanism is implicit (not explicit fine-tuning) and operates only on Pro/Team tiers, suggesting pattern learning from editor activity rather than explicit configuration. Free tier uses a single base model without personalization.
Unique: Implicit style adaptation via editor activity analysis without explicit configuration, versus competitors (Copilot, Tabnine) that require manual style guides or explicit fine-tuning. Supermaven's approach is transparent to the user but also non-configurable and undisclosed in mechanism.
vs alternatives: Requires no manual style configuration compared to tools requiring explicit style guides; trade-off is lack of transparency and inability to control or export learned styles.
Delivers code suggestions to the editor inline as the developer types, with a claimed baseline latency of 250ms from keystroke to suggestion display. The system uses a cloud inference backend and local editor plugin to minimize round-trip time. Latency claim is positioned against an unnamed competitor (783ms), but methodology is undisclosed and no independent verification is provided.
Unique: Claimed 250ms latency via optimized cloud inference pipeline and editor plugin architecture, versus competitors with higher latency (783ms unnamed baseline). Actual differentiation is undisclosed; mechanism may involve request batching, model quantization, or edge caching, but specifics are not public.
vs alternatives: Faster than unnamed competitor (250ms vs 783ms claimed); trade-off is cloud dependency and unverified latency claim with no SLA or performance guarantee.
Provides native editor extensions for VS Code, JetBrains IDEs (IntelliJ IDEA, PyCharm, WebStorm, etc.), and Neovim, enabling inline suggestion rendering, hotkey-driven chat access, and compiler diagnostic integration directly within the editor. Each plugin variant is maintained separately and integrates with the editor's native autocomplete UI, keybinding system, and file context APIs.
Unique: Native plugins for three major editor ecosystems (VS Code, JetBrains, Neovim) with integrated chat and diff application, versus competitors (Copilot, Tabnine) that support broader editor ecosystems but with less deep integration in some cases. Supermaven's approach prioritizes depth over breadth.
vs alternatives: Deep integration with VS Code and JetBrains (native autocomplete UI, hotkey system) compared to web-based tools or lighter integrations; trade-off is limited editor support (no Sublime, Vim, Emacs) and undisclosed Neovim support details.
+5 more capabilities