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| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | $0.10/M tokens | — |
| Capabilities | 13 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Provides on-demand REST API access to 100+ pre-hosted open-source LLM models (Llama, Qwen, DeepSeek, Gemma, etc.) without requiring infrastructure provisioning. Models are deployed across NVIDIA GPU clusters with automatic request routing and load balancing. Token-based pricing charges separately for input and output tokens, with optional prompt caching for reduced costs on repeated contexts. Developers call a single endpoint and receive streamed or batch responses without managing model weights, VRAM allocation, or GPU scheduling.
Unique: Aggregates 100+ open-source models under a single unified REST API with token-based pricing and optional prompt caching, eliminating the need to manage separate endpoints or model deployments. Uses FlashAttention-4 custom kernels and distribution-aware speculative decoding (proprietary optimization) to achieve industry-leading throughput and latency compared to self-hosted or single-model inference services.
vs alternatives: Faster and cheaper than self-hosting open-source models on cloud VMs (no infrastructure overhead), and more flexible than single-model APIs like OpenAI (supports 100+ models with unified pricing) while maintaining lower costs than proprietary model APIs through open-source model selection.
Asynchronous batch processing API that accepts large volumes of inference requests (up to 30 billion tokens per model per batch) and processes them at lower cost (50% reduction vs real-time API) by optimizing GPU utilization and request scheduling. Requests are queued, batched by model, and processed during off-peak or scheduled windows. Results are stored and retrieved via polling or webhook callbacks. Designed for non-latency-sensitive workloads like data labeling, content generation, or periodic model evaluation.
Unique: Offers 50% cost reduction for batch workloads by decoupling inference from real-time latency requirements and optimizing GPU utilization through request batching and scheduling. Scales to 30 billion tokens per batch, enabling single-job processing of enterprise-scale datasets without manual job splitting or orchestration.
vs alternatives: Cheaper than real-time API for bulk workloads (50% cost reduction) and simpler than self-managed batch infrastructure (no Kubernetes, job queues, or GPU cluster management required), but slower than real-time APIs and less flexible than custom batch pipelines.
Support for function calling (tool use) across text, vision, and audio models via schema-based function definitions. Developers define functions as JSON schemas, and models return structured function call arguments. Supports parallel function calling (multiple tools in one response) and tool result feedback loops. Integrated into the same REST API as inference, enabling agentic workflows without separate tool orchestration infrastructure.
Unique: Provides function calling across all model types (text, vision, audio) via a unified schema-based interface, enabling multi-modal agentic workflows without separate tool orchestration services. Supports parallel function calling and tool result feedback loops for complex agent behaviors.
vs alternatives: More integrated than point solutions (separate function calling APIs) and simpler than custom agent frameworks (LangChain, AutoGen) which require manual orchestration, but less feature-rich than specialized agent platforms (Anthropic Agents, OpenAI Assistants) which include built-in memory and tool management.
Automatic caching of prompt prefixes (system prompts, context, documents) to reduce token costs on repeated requests. When the same prefix is used multiple times, subsequent requests pay reduced rates for cached tokens (exact reduction not specified per model). Implemented at the API level; developers specify cache control headers or parameters. Designed for applications with static context (e.g., RAG with the same documents, multi-turn conversations with system prompts) that repeat across requests.
Unique: Implements automatic prompt caching at the API level, reducing token costs for repeated context without requiring developers to manually manage cache keys or invalidation. Particularly effective for RAG and multi-turn applications where context is static across requests.
vs alternatives: Simpler than manual caching (no cache key management or invalidation logic required) and more cost-effective than paying full token rates for repeated context, but less transparent than explicit caching (no visibility into cache hit rates or savings) and cache reduction rates are not publicly specified.
Proprietary inference optimizations developed through published research and implemented as custom CUDA kernels (FlashAttention-4, distribution-aware speculative decoding, ATLAS runtime-learning accelerators). These optimizations are transparently applied to all inference requests without developer configuration. Reduces latency and increases throughput compared to standard inference implementations. Backed by peer-reviewed research papers published by Together AI team.
Unique: Implements custom CUDA kernels (FlashAttention-4, distribution-aware speculative decoding, ATLAS) developed through published research, providing transparent performance improvements without requiring developer configuration or code changes. Differentiates through research-backed optimizations rather than hardware advantages.
vs alternatives: More performant than standard inference implementations (vLLM, TensorRT) due to custom kernel optimizations, and more transparent than proprietary inference services (OpenAI, Anthropic) which don't disclose optimization techniques. However, performance gains are not quantified and optimizations are not open-source.
Serverless inference for vision models including image generation (FLUX, Stable Diffusion, Qwen Image), image analysis, and visual understanding. Image generation is priced per image or per megapixel depending on model, with configurable step counts (e.g., FLUX.1 schnell at 4 steps). Vision models accept image inputs (format not specified) and return generated or analyzed outputs. Integrated into the same REST API as text models, allowing multi-modal workflows without separate endpoints.
Unique: Integrates image generation (FLUX, Stable Diffusion) and vision models into the same unified REST API as text models, enabling multi-modal workflows without separate endpoints or authentication. Offers per-image and per-megapixel pricing options, allowing cost optimization for different image dimensions and quality requirements.
vs alternatives: Simpler than managing separate image generation services (Replicate, Stability AI) and cheaper than proprietary image APIs (DALL-E, Midjourney) for bulk generation, but less feature-rich than specialized image platforms (no style transfer, inpainting, or advanced editing documented).
Serverless inference for audio generation, audio transcription, and video generation models. Audio models handle text-to-speech and audio synthesis; transcription models convert audio files to text. Video generation models create videos from text prompts or images. All models are accessed via the same REST API as text and image models. Pricing structure for audio/video not fully specified in public documentation (contact sales for details).
Unique: Bundles audio generation, transcription, and video generation into the same unified REST API as text and image models, enabling end-to-end multi-modal workflows without switching between services. Leverages dedicated container inference infrastructure optimized for generative media workloads.
vs alternatives: More integrated than point solutions (separate TTS, transcription, and video APIs) and simpler than self-hosted audio/video pipelines, but less specialized than dedicated audio platforms (Eleven Labs for TTS, AssemblyAI for transcription) and pricing opacity makes cost comparison difficult.
Serverless inference for embedding models that convert text into high-dimensional vectors for semantic search, similarity matching, and RAG (Retrieval-Augmented Generation) applications. Embeddings are generated via REST API and can be stored in external vector databases (Pinecone, Weaviate, Milvus, etc.) or Together AI's Managed Storage. Supports batch embedding generation for large document corpora. Pricing is per-token (same as text models), making it cost-effective for embedding large datasets.
Unique: Integrates embedding generation into the same token-based pricing model as text inference, and offers optional Managed Storage with zero egress fees for vector persistence. Enables end-to-end RAG pipelines (embedding generation → storage → retrieval) without switching between services or paying egress costs.
vs alternatives: Cheaper than dedicated embedding APIs (OpenAI Embeddings) due to open-source model selection and token-based pricing, and simpler than self-hosted embedding pipelines (no model management or vector database setup required), but less integrated than full-stack RAG platforms (Pinecone, Weaviate) which include search and indexing.
+5 more capabilities
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.
Together AI Platform scores higher at 57/100 vs Supabase at 42/100. Together AI Platform 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
+1 more capabilities