| 0 |
| 1 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | — | $10/mo |
| Capabilities | 14 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Defines workflow tasks using a TypeScript-first SDK that compiles task definitions into a schema-aware registry, enabling static type checking across task inputs/outputs and automatic serialization of complex types. The Task Definition API creates tasks as first-class objects with built-in support for retries, timeouts, and concurrency limits, stored in a workerCatalog that the run engine references during execution.
Unique: Uses a monorepo-based build system (Turborepo) with task schema compilation that generates a workerCatalog at build time, enabling the run engine to validate task invocations against pre-compiled schemas rather than runtime reflection or JSON schema validation
vs alternatives: Stronger type safety than Temporal or Airflow because task contracts are validated at TypeScript compile time, not runtime, catching integration bugs before deployment
Executes tasks across distributed workers using a state machine-driven run engine that persists execution checkpoints to enable resumption after failures or long-running operations. The checkpoint system captures execution state at defined points (waitpoints), allowing tasks to pause, wait for external events, and resume without re-executing completed work. Implemented via the Run Engine Architecture with dedicated checkpointSystem and waitpointSystem components that manage state transitions.
Unique: Implements a dual-system checkpoint architecture: executionSnapshotSystem captures full execution state at arbitrary points, while checkpointSystem and waitpointSystem provide explicit pause/resume semantics with distributed locking via Redis to prevent concurrent execution conflicts
vs alternatives: More granular than AWS Step Functions because checkpoints can be placed at any task step, not just between state transitions, enabling true mid-function resumption for long-running operations
Implements distributed locking via Redis to prevent concurrent execution of the same task or conflicting state transitions. Uses Redis EVAL scripts for atomic lock acquisition and release, ensuring exactly-once semantics across multiple coordinator instances. Concurrency management system enforces per-task concurrency limits (e.g., max 5 concurrent executions), with queuing of excess requests. Prevents race conditions in checkpoint updates and dequeue operations.
Unique: Uses Redis EVAL scripts for atomic lock operations, avoiding race conditions that could occur with separate GET/SET commands. Integrates with concurrency management system to enforce per-task limits without requiring separate rate-limiting service.
vs alternatives: More efficient than database-based locking because Redis operations are in-memory and sub-millisecond, whereas database locks require disk I/O and transaction overhead
Provides lifecycle hooks (onStart, onSuccess, onFailure, onRetry) that execute custom code before task execution, after success, after failure, or before retry attempts. Hooks are defined in task configuration and executed by the run engine as part of the run state machine. Enables cross-cutting concerns like metrics emission, notification sending, or resource cleanup without modifying task code. Hooks have access to task context and execution metadata.
Unique: Hooks are integrated into the run state machine, executing at specific state transitions rather than as separate event handlers. Provides access to full task context and execution metadata, enabling rich customization without external event systems.
vs alternatives: More integrated than webhook-based approaches because hooks execute in-process with full context access, whereas webhooks require serialization and network round-trips
Allows developers to define custom build extensions that transform task code during compilation, enabling code generation, instrumentation, or optimization. Build extensions hook into the Turborepo build system and can modify task definitions before they're registered in the workerCatalog. Enables use cases like automatic OpenTelemetry instrumentation, code splitting, or custom serialization logic without manual implementation.
Unique: Integrates with Turborepo build system to allow compile-time task transformation, enabling code generation and instrumentation without runtime overhead. Extensions have access to full TypeScript AST, enabling sophisticated code analysis and generation.
vs alternatives: More powerful than decorator-based approaches because extensions can perform arbitrary code transformation, whereas decorators are limited to metadata attachment
Automatically expires and cleans up old task runs based on configurable TTL (time-to-live) policies, freeing database storage and improving query performance. The TTL system (ttlSystem component) periodically scans for expired runs and marks them for deletion. Supports per-environment TTL configuration (e.g., dev runs expire after 7 days, prod runs after 90 days). Deleted runs are archived to cold storage before permanent deletion.
Unique: Implements TTL as a dedicated system component (ttlSystem) that runs periodically, rather than relying on database-level TTL features. Supports per-environment configuration and integrates with execution snapshot system to archive data before deletion.
vs alternatives: More flexible than database-level TTL because per-environment policies can be configured without database schema changes, and archived data can be queried separately
Routes task execution across multiple compute providers (Docker, Kubernetes, serverless) using a provider abstraction layer that abstracts provider-specific deployment details. The dequeue system polls task queues managed by Redis, applies concurrency limits and rate limiting per task, and dispatches work to available workers based on provider capacity and task affinity. Queue management uses distributed locking to ensure exactly-once dequeue semantics across multiple coordinator instances.
Unique: Uses a pluggable provider architecture (Docker, Kubernetes providers as separate apps) with a coordinator service that abstracts provider-specific logic, enabling new providers to be added without modifying core scheduling logic. Dequeue system implements distributed locking via Redis EVAL scripts to guarantee exactly-once semantics.
vs alternatives: More flexible than Celery because provider abstraction allows seamless switching between Docker/K8s/serverless without code changes, whereas Celery requires separate broker/worker configurations per backend
Manages task execution lifecycle through a deterministic state machine (defined in runEngine.server.ts and statuses.ts) that transitions runs through states: PENDING → QUEUED → EXECUTING → COMPLETED/FAILED/RETRYING. Implements automatic retry logic with exponential backoff, configurable retry limits per task, and error categorization to distinguish transient vs permanent failures. Failed runs trigger the retryAttemptSystem which re-enqueues work based on retry policies.
Unique: Implements a centralized run state machine in the run engine that all coordinator instances reference, with state transitions persisted to database and validated via distributed locking, ensuring no concurrent state conflicts. Retry logic is decoupled from task code via runAttemptSystem, allowing retry policies to be updated without redeploying tasks.
vs alternatives: More deterministic than Temporal because state transitions are explicitly modeled in a single state machine rather than distributed across workflow code, making failure modes easier to reason about
+6 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 trigger.dev at 38/100. trigger.dev leads on ecosystem, while Supermaven is stronger on adoption and quality.
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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