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| Pricing | Free | Free |
| Capabilities | 8 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Greip processes incoming transaction requests through a multi-signal scoring engine that combines IP geolocation, device fingerprinting, and behavioral heuristics to assign a fraud risk score in under 100ms. The system evaluates transaction metadata (IP, device ID, user behavior patterns) against historical fraud patterns and returns a numerical risk score that integrates directly into payment authorization flows without blocking legitimate transactions.
Unique: Achieves sub-100ms latency through edge-cached IP geolocation databases and pre-computed device fingerprint hashes rather than real-time ML inference, enabling synchronous integration into payment authorization flows without async callbacks
vs alternatives: Faster than Stripe Radar for simple fraud signals (IP + device) because it avoids heavyweight ML inference, but less sophisticated than AWS Fraud Detector which uses ensemble models and requires more integration effort
Greip maintains a continuously-updated IP address database that maps IP ranges to geographic locations, ISP information, and flags suspicious IP characteristics (datacenter IPs, known proxy services, VPN exit nodes). When a transaction IP is queried, the system performs a lookup against this database and returns geolocation coordinates, country/city, ISP name, and risk flags indicating whether the IP belongs to a proxy, VPN, or datacenter network commonly used for fraud.
Unique: Combines IP geolocation with proxy/VPN detection in a single lookup rather than requiring separate API calls to different providers, reducing latency and simplifying integration for developers who need both signals
vs alternatives: Simpler integration than MaxMind (single API call vs. multiple databases) but less comprehensive than Maxmind's GeoIP2 which includes additional signals like mobile carrier detection and threat intelligence
Greip provides a client-side JavaScript SDK that collects device characteristics (user agent, screen resolution, installed fonts, canvas fingerprint, WebGL renderer, timezone, language settings) and generates a stable device fingerprint hash. This fingerprint is sent with transactions to enable device-level fraud detection, allowing the system to identify when multiple accounts are being accessed from the same device or when a device's behavior pattern suddenly changes.
Unique: Combines multiple fingerprinting signals (canvas, WebGL, font enumeration, user agent) into a single hash rather than relying on a single signal, improving stability and reducing false positives from minor browser changes
vs alternatives: Lighter-weight than FingerprintJS Pro (no server-side ML model) but less stable; better for real-time fraud scoring than historical device tracking
Greip analyzes transaction patterns for each user account (transaction frequency, amount distribution, time-of-day patterns, geographic velocity) and flags deviations from the user's historical baseline as behavioral anomalies. The system learns normal behavior from the first 10-20 transactions and then scores subsequent transactions based on how much they deviate from established patterns (e.g., a user who normally spends $50/transaction suddenly spending $5000 triggers a high anomaly score).
Unique: Uses statistical deviation from user-specific baselines rather than global fraud patterns, enabling personalized fraud detection that adapts to individual spending habits without requiring labeled fraud training data
vs alternatives: More personalized than Stripe Radar's global rules but requires more historical data; faster to implement than building custom ML models but less sophisticated than ensemble approaches that combine behavioral, network, and device signals
Greip exposes a REST API endpoint that accepts transaction details (IP, device fingerprint, user ID, amount, merchant category) and returns a fraud risk assessment synchronously or asynchronously via webhook. The API supports both real-time blocking (synchronous response) and async scoring (webhook callback) to accommodate different integration patterns. Developers can call the API at transaction time, post-transaction for batch scoring, or set up webhooks to receive risk updates as new signals become available.
Unique: Supports both synchronous and asynchronous scoring modes in a single API, allowing developers to choose between real-time blocking (sync) and background risk updates (async webhooks) based on their authorization flow requirements
vs alternatives: More flexible than Stripe Radar which is tightly coupled to Stripe's payment flow; simpler than building custom fraud detection but less integrated than native payment processor solutions
Greip offers a free tier that provides limited API access (typically 100-1000 requests/month) with full feature parity to paid tiers, enabling developers to test fraud detection against real transaction patterns before committing budget. The free tier includes all core capabilities (IP geolocation, device fingerprinting, behavioral analysis) but with strict rate limits enforced at the API key level. Developers can upgrade to paid tiers (typically $99-999/month) for higher rate limits and priority support.
Unique: Offers full feature parity between free and paid tiers (unlike competitors who cripple free tiers with reduced accuracy or missing signals), allowing developers to validate fraud detection effectiveness before paying
vs alternatives: More generous than Stripe Radar's free tier (which requires active Stripe account) and MaxMind's free tier (which has significantly reduced accuracy); better for early-stage validation than AWS Fraud Detector which requires AWS account setup
Greip provides a web-based dashboard that displays real-time fraud alerts, historical transaction risk scores, and aggregated fraud metrics (fraud rate, high-risk transaction volume, geographic distribution of fraud). The dashboard allows developers to review flagged transactions, adjust risk thresholds, and export transaction history for analysis. Alerts are surfaced with risk scores, signal breakdowns, and recommended actions (block, challenge, allow).
Unique: Provides unified dashboard for all fraud signals (IP, device, behavioral) rather than requiring separate dashboards for each signal type, simplifying fraud investigation workflows
vs alternatives: More user-friendly than Stripe Radar's dashboard for non-technical users; less comprehensive than enterprise fraud management platforms (Kount, Sift) which offer advanced case management and investigation tools
Greip sends webhook notifications to a developer-specified HTTPS endpoint whenever a transaction exceeds a configurable fraud risk threshold. Webhooks are sent in real-time (within seconds of transaction scoring) and include full transaction details, risk score, signal breakdown, and recommended action. Developers can configure separate thresholds for different actions (alert, block, challenge) and customize webhook payload format.
Unique: Sends webhooks with full signal breakdown (IP risk, device risk, behavioral risk) rather than just a binary fraud/not-fraud decision, enabling developers to implement nuanced fraud response logic based on specific risk signals
vs alternatives: More flexible than Stripe Radar's webhook system which only sends alerts for high-risk transactions; simpler than building custom fraud detection but requires webhook infrastructure on client side
Executes SQL queries against Supabase PostgreSQL instances through the Model Context Protocol, translating natural language or structured query requests into parameterized SQL statements. Uses MCP's tool-calling interface to expose database operations as callable functions with schema validation, enabling LLM agents to perform CRUD operations, joins, and aggregations with automatic connection pooling and credential management through Supabase client SDK.
Unique: Exposes Supabase PostgreSQL as MCP tools with automatic credential injection from Supabase client SDK, eliminating manual connection string management and enabling seamless LLM-to-database queries within Claude or compatible agents
vs alternatives: Tighter integration than generic SQL MCP servers because it leverages Supabase's built-in authentication and connection pooling rather than requiring separate database credential configuration
Exposes Supabase Auth session state and user metadata through MCP tools, allowing agents to inspect current authentication context, retrieve user profiles, and trigger auth-related operations. Integrates with Supabase's JWT-based auth system to validate sessions and access user claims without re-authenticating, using the Supabase client's built-in session management.
Unique: Integrates Supabase's JWT-based auth system directly into MCP tool interface, allowing agents to inspect and act on auth state without managing separate credential stores or re-authentication flows
vs alternatives: More seamless than generic auth MCP servers because it leverages Supabase's built-in session management and avoids redundant credential passing between agent and auth system
Invokes Supabase Edge Functions (serverless TypeScript/JavaScript functions) through MCP tools, passing parameters and receiving results with optional streaming support. Uses Supabase's edge function HTTP API to trigger functions with automatic authentication headers and response parsing, enabling agents to execute custom business logic without embedding it in the agent itself.
Supabase scores higher at 42/100 vs Greip at 41/100. Greip leads on adoption and quality, while Supabase is stronger on ecosystem.
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Unique: Exposes Supabase Edge Functions as MCP tools with automatic authentication and response parsing, allowing agents to invoke custom serverless logic without managing HTTP clients or credential injection
vs alternatives: More integrated than generic HTTP MCP tools because it handles Supabase-specific authentication, error handling, and response formatting automatically
Subscribes to real-time changes on Supabase tables through MCP's event streaming interface, using Supabase's PostgreSQL LISTEN/NOTIFY mechanism to push INSERT, UPDATE, and DELETE events to agents. Maintains persistent WebSocket connections and filters events by table and row-level policies, enabling agents to react to database changes without polling.
Unique: Bridges Supabase's PostgreSQL LISTEN/NOTIFY real-time system with MCP's tool interface, enabling agents to subscribe to database changes without managing WebSocket connections or event serialization
vs alternatives: More efficient than polling-based approaches because it uses Supabase's native real-time infrastructure rather than repeated database queries
Manages files in Supabase Storage buckets through MCP tools, supporting upload, download, list, and delete operations with automatic authentication and path-based access control. Uses Supabase's S3-compatible storage API with built-in support for public/private buckets and signed URLs for temporary access, enabling agents to handle file I/O without managing cloud storage credentials.
Unique: Exposes Supabase Storage's S3-compatible API as MCP tools with automatic authentication and signed URL generation, eliminating the need for agents to manage cloud storage credentials or generate temporary access tokens
vs alternatives: More integrated than generic S3 MCP tools because it leverages Supabase's built-in bucket policies and authentication rather than requiring separate AWS credentials
Performs semantic similarity searches on vector embeddings stored in Supabase PostgreSQL using pgvector extension, translating natural language queries into embedding vectors and executing cosine/L2 distance searches. Integrates with embedding providers (OpenAI, Cohere) or uses pre-computed embeddings, enabling agents to retrieve semantically similar documents or records without full-text search limitations.
Unique: Integrates pgvector directly into MCP tools with automatic embedding generation and distance calculation, enabling agents to perform semantic search without managing separate vector database infrastructure
vs alternatives: More efficient than external vector databases (Pinecone, Weaviate) for Supabase users because it colocates embeddings with relational data, reducing network latency and simplifying data synchronization
Exposes Supabase database schema information through MCP tools, allowing agents to discover table structures, column types, constraints, and relationships without manual schema documentation. Queries PostgreSQL information_schema and Supabase metadata tables to dynamically generate schema descriptions, enabling agents to construct valid queries and understand data relationships.
Unique: Queries Supabase's PostgreSQL information_schema directly through MCP tools, enabling agents to dynamically discover and adapt to database schemas without pre-configured schema definitions
vs alternatives: More flexible than static schema definitions because it reflects live database state, including recent migrations or schema changes
Enforces Supabase Row-Level Security policies within agent queries, ensuring that agents can only access rows permitted by RLS rules defined in the database. Evaluates policies based on authenticated user context (JWT claims, user ID) and applies WHERE clause filters automatically, preventing unauthorized data access at the database layer rather than application layer.
Unique: Delegates authorization enforcement to PostgreSQL RLS policies rather than implementing authorization in agent code, ensuring that data access rules are centralized and cannot be bypassed by agent logic
vs alternatives: More secure than application-level authorization because RLS is enforced at the database layer, preventing accidental data leaks even if agent code has bugs
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