{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"together-ai-platform","slug":"together-ai-platform","name":"Together AI Platform","type":"platform","url":"https://www.together.ai","page_url":"https://unfragile.ai/together-ai-platform","categories":["deployment-infra"],"tags":[],"pricing":{"model":"usage-based","free":true,"starting_price":"$0.10/M tokens"},"status":"active","verified":false},"capabilities":[{"id":"together-ai-platform__cap_0","uri":"capability://tool.use.integration.serverless.inference.for.100.plus.open.source.models","name":"serverless-inference-for-100-plus-open-source-models","description":"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.","intents":["I want to run inference on Llama 3.3 70B without buying GPUs or managing containers","I need to compare outputs across multiple open-source models (Qwen, DeepSeek, Gemma) with a single API","I want to reduce inference costs by caching prompts that are reused across requests","I need to scale from 10 to 10,000 concurrent inference requests without provisioning infrastructure"],"best_for":["startups and solo developers building LLM applications without ML infrastructure expertise","teams prototyping with open-source models before committing to fine-tuning or custom deployment","enterprises needing multi-model inference without maintaining separate GPU clusters"],"limitations":["Models are served exclusively through Together AI infrastructure — no option to export or self-host models after testing","Cold-start latency and per-request overhead not publicly specified; may be unsuitable for sub-100ms latency requirements","No control over model versions or update timing; Together AI updates models unilaterally","Rate limiting and concurrent request caps not documented; scaling to 'thousands of GPUs' requires contact with sales","Context length limits per request not specified in public documentation"],"requires":["API key from Together AI account","HTTP client library (curl, requests, axios, etc.)","Network connectivity to Together AI endpoints (geographic regions not specified)"],"input_types":["text (chat messages, prompts)","structured JSON (function calling schemas, tool definitions)"],"output_types":["text (streamed or buffered LLM responses)","structured JSON (function call arguments, tool invocations)"],"categories":["tool-use-integration","inference-as-a-service"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"together-ai-platform__cap_1","uri":"capability://automation.workflow.batch.inference.api.with.50.percent.cost.reduction","name":"batch-inference-api-with-50-percent-cost-reduction","description":"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.","intents":["I need to generate embeddings for 1 million documents but don't need results in real-time","I want to run inference on a large dataset at 50% of the per-token cost of real-time API","I need to process 30 billion tokens of inference without managing queue infrastructure myself","I want to schedule batch jobs to run during off-peak hours to minimize costs"],"best_for":["data teams processing large corpora (embeddings, classification, summarization)","content platforms generating bulk content (product descriptions, social media posts)","research teams evaluating models on benchmark datasets without latency constraints"],"limitations":["No real-time response — results available only after batch completion (latency not specified)","Maximum 30 billion tokens per batch per model; larger workloads require multiple batch submissions","No streaming responses; entire batch must complete before results are available","Webhook callback mechanism not documented; polling may require custom retry logic","No built-in result persistence or export to data warehouses; results must be manually retrieved and stored"],"requires":["API key from Together AI account","Batch job submission endpoint (format and schema not publicly documented)","Storage for batch results (S3, GCS, or local filesystem)"],"input_types":["JSONL (JSON Lines) format with prompt/input per line","CSV or Parquet (inferred from 'large dataset' language, not confirmed)"],"output_types":["JSONL with model outputs per line","Structured results with token counts and metadata"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"together-ai-platform__cap_10","uri":"capability://tool.use.integration.multi.modal.function.calling.with.tool.use","name":"multi-modal-function-calling-with-tool-use","description":"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.","intents":["I want to build an AI agent that can call APIs, databases, or custom functions based on user requests","I need to extract structured data from text or images using function calling instead of prompt engineering","I want to create a multi-step workflow where an LLM decides which tools to call and in what order","I need to integrate LLM outputs with external systems (CRM, ERP, databases) via function calling"],"best_for":["teams building AI agents and agentic workflows","applications requiring structured data extraction or API integration","enterprises automating business processes with LLM-driven tool orchestration"],"limitations":["Function calling schema format not specified; unclear if OpenAI, Anthropic, or custom format is used","Parallel function calling support not confirmed; unclear if models can return multiple tool calls in one response","Tool result feedback mechanism not documented; unclear how to provide tool outputs back to the model","Error handling and retry logic not specified; unclear how to handle tool failures or invalid function calls","No built-in tool registry or discovery; developers must manually define all functions"],"requires":["API key from Together AI account","Function definitions as JSON schemas","Tool implementation (custom code, API endpoints, etc.)"],"input_types":["text prompts with tool definitions","images or audio (for vision/audio models)"],"output_types":["structured JSON with function call arguments","tool invocation results"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"together-ai-platform__cap_11","uri":"capability://data.processing.analysis.prompt.caching.for.cost.reduction.on.repeated.contexts","name":"prompt-caching-for-cost-reduction-on-repeated-contexts","description":"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.","intents":["I want to reduce costs for RAG applications that use the same document context across multiple queries","I need to cache system prompts and instructions to avoid re-paying for them on every request","I want to optimize costs for multi-turn conversations where the system prompt and conversation history are reused","I need to cache large context windows (e.g., 100K tokens) and only pay for new tokens in each request"],"best_for":["RAG applications with static document context","multi-turn conversation applications with fixed system prompts","applications with large context windows that are reused across requests"],"limitations":["Cache reduction rates not publicly specified; unclear how much savings prompt caching provides","Cache invalidation and TTL not documented; unclear how long cached prompts are retained","Cache key generation not specified; unclear how Together AI determines if prompts are identical","No cache statistics or monitoring; developers cannot see cache hit rates or savings","Cache size limits not documented; unclear if there are quotas on cached prompt size"],"requires":["API key from Together AI account","Support for cache control headers or parameters in API client"],"input_types":["text prompts with cacheable prefixes"],"output_types":["LLM responses with cache usage metadata (if provided)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"together-ai-platform__cap_12","uri":"capability://automation.workflow.research.backed.inference.optimization.via.custom.kernels","name":"research-backed-inference-optimization-via-custom-kernels","description":"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.","intents":["I want to run inference faster and cheaper without changing my code or model","I need to reduce latency for real-time inference applications (chatbots, search, recommendations)","I want to maximize GPU utilization and throughput for batch inference workloads","I need to understand the technical foundations of the inference optimizations I'm using"],"best_for":["latency-sensitive applications requiring sub-100ms inference","high-throughput inference workloads (batch processing, serving 1000s of concurrent requests)","teams wanting to leverage cutting-edge inference research without implementing custom kernels"],"limitations":["Optimization techniques are proprietary; exact implementation details not publicly available","Performance gains not quantified in public documentation; unclear how much latency/throughput improvement is achieved","Optimizations may not apply to all models or use cases; unclear which models benefit most","No opt-out mechanism; developers cannot disable optimizations if they cause issues","Research papers are published but may not be accessible to all developers; technical details require reading papers"],"requires":["API key from Together AI account","No additional configuration or code changes required"],"input_types":["standard inference requests (text, images, etc.)"],"output_types":["optimized inference results with reduced latency/increased throughput"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"together-ai-platform__cap_2","uri":"capability://image.visual.vision.and.image.generation.inference","name":"vision-and-image-generation-inference","description":"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.","intents":["I want to generate product images for an e-commerce catalog using FLUX without running Stable Diffusion locally","I need to analyze images (OCR, object detection, scene understanding) via API without building a vision model pipeline","I want to compare outputs from FLUX, Stable Diffusion, and Qwen Image models with a single API","I need to generate images at scale (1000+ per day) with transparent per-image pricing"],"best_for":["e-commerce and content platforms generating product images and marketing assets","applications requiring image analysis or visual understanding without ML infrastructure","teams prototyping multi-modal AI workflows (text-to-image, image-to-text)"],"limitations":["Image input/output formats not specified (JPEG, PNG, WebP support unknown)","Step count configuration available but default values vary by model; no guidance on step count vs quality tradeoff","Per-megapixel pricing (e.g., FLUX.2 max at $0.070/mp) requires calculating image dimensions; no cost calculator provided","No batch image generation API documented; large-scale image generation may require sequential API calls","Image safety/content moderation filters not documented; NSFW or policy-violating outputs may be rejected without explanation"],"requires":["API key from Together AI account","Image input in supported format (format not specified)","Understanding of model-specific parameters (step counts, guidance scales, etc.)"],"input_types":["text prompts (for image generation)","images in unspecified format (for image analysis)"],"output_types":["images in unspecified format","structured JSON with analysis results (for vision models)"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"together-ai-platform__cap_3","uri":"capability://image.visual.audio.and.video.generation.inference","name":"audio-and-video-generation-inference","description":"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).","intents":["I want to generate speech from text for accessibility or voiceover applications without managing TTS infrastructure","I need to transcribe audio files (podcasts, meetings, user-generated content) at scale via API","I want to generate short videos from text prompts for social media content creation","I need to integrate audio/video generation into a multi-modal AI application with a single API"],"best_for":["content creators and platforms generating audio/video assets at scale","accessibility-focused applications requiring text-to-speech synthesis","media companies automating video production workflows"],"limitations":["Pricing for audio and video models not publicly specified; requires contacting sales for cost estimates","Audio input/output formats not documented (WAV, MP3, AAC support unknown)","Video generation capabilities and output formats not detailed (resolution, duration, codec unknown)","No batch audio/video processing API documented; large-scale generation may require sequential calls","Audio/video quality, latency, and SLA not specified in public documentation"],"requires":["API key from Together AI account","Audio files in unspecified format (for transcription)","Text prompts or images (for generation)"],"input_types":["text prompts (for audio/video generation)","audio files in unspecified format (for transcription)"],"output_types":["audio files in unspecified format","video files in unspecified format","text transcriptions"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"together-ai-platform__cap_4","uri":"capability://memory.knowledge.embedding.and.vector.generation.for.rag","name":"embedding-and-vector-generation-for-rag","description":"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.","intents":["I want to embed 1 million documents for semantic search without managing embedding infrastructure","I need to generate embeddings for a RAG pipeline that retrieves context for LLM prompts","I want to find semantically similar documents using embeddings without building a vector database","I need to store embeddings in Together AI's managed storage with zero egress fees"],"best_for":["teams building RAG systems and semantic search applications","data teams embedding large document corpora for similarity matching","applications requiring real-time embedding generation without vector database management"],"limitations":["Embedding models available not specified in public documentation; unclear which models (e.g., BGE, E5, Nomic) are supported","Vector dimension and similarity metrics (cosine, L2, dot product) not documented","No built-in vector search or similarity matching; embeddings must be stored in external vector databases or Managed Storage","Managed Storage pricing not specified; requires contacting sales","No automatic embedding updates; if source documents change, embeddings must be regenerated manually"],"requires":["API key from Together AI account","Text input (documents, queries, or sentences to embed)","External vector database (Pinecone, Weaviate, Milvus) or Together AI Managed Storage for persistence"],"input_types":["text (documents, queries, sentences)"],"output_types":["vectors (floating-point arrays of unspecified dimension)","structured JSON with embedding metadata"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"together-ai-platform__cap_5","uri":"capability://search.retrieval.reranking.models.for.search.relevance","name":"reranking-models-for-search-relevance","description":"Serverless inference for reranking models that score and reorder search results based on relevance to a query. Rerankers accept a query and a list of candidate documents/passages and return ranked scores. Used in RAG pipelines to improve retrieval quality by reordering results from semantic search or keyword search. Integrated into the same REST API as other models, with token-based pricing.","intents":["I want to improve RAG retrieval quality by reranking semantic search results with a cross-encoder model","I need to score and rank documents by relevance to a user query without building a reranking model","I want to combine keyword search and semantic search by reranking results from both methods","I need to reduce hallucinations in LLM responses by ensuring only top-ranked documents are used as context"],"best_for":["RAG systems requiring high-quality document retrieval","search applications combining multiple retrieval methods (keyword + semantic)","teams optimizing LLM accuracy by filtering low-relevance context"],"limitations":["Reranking models available not specified; unclear which cross-encoder models (e.g., mxbai-rerank, bge-reranker) are supported","Batch reranking API not documented; reranking large result sets may require sequential API calls","Scoring scale and interpretation not specified (0-1, 0-100, unbounded)","No built-in result filtering or threshold configuration; developers must implement cutoff logic","Latency for reranking large candidate lists not specified; may be unsuitable for real-time search"],"requires":["API key from Together AI account","Query text and list of candidate documents/passages","Understanding of reranking model inputs and output format"],"input_types":["text (query and candidate documents)"],"output_types":["structured JSON with relevance scores per document","ranked list of documents"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"together-ai-platform__cap_6","uri":"capability://safety.moderation.content.moderation.and.safety.filtering","name":"content-moderation-and-safety-filtering","description":"Serverless inference for content moderation models that classify text for policy violations (hate speech, violence, sexual content, etc.). Models return classification scores or labels indicating content safety. Integrated into the same REST API as other models. Can be used to filter user-generated content, moderate chat applications, or audit training data for harmful content.","intents":["I want to filter user-generated content for hate speech and violence without building a moderation model","I need to audit my training dataset for harmful content before fine-tuning a model","I want to moderate chat messages in real-time to prevent policy violations","I need to classify content by safety category (sexual, violent, hateful, etc.) for compliance reporting"],"best_for":["platforms with user-generated content requiring real-time moderation","teams auditing datasets for harmful content before model training","applications requiring compliance with content policies (GDPR, COPPA, etc.)"],"limitations":["Moderation models available not specified; unclear which models (e.g., Perspective API, OpenAI Moderation) are supported","Classification categories and scoring thresholds not documented; unclear how to interpret scores","No appeal or human review workflow; moderation decisions are final","False positive/negative rates not specified; may require manual review for sensitive decisions","No context awareness; models may struggle with sarcasm, cultural references, or domain-specific language"],"requires":["API key from Together AI account","Text input to moderate"],"input_types":["text (user messages, content, etc.)"],"output_types":["structured JSON with moderation scores or labels","classification categories (hate speech, violence, sexual, etc.)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"together-ai-platform__cap_7","uri":"capability://code.generation.editing.custom.model.fine.tuning.and.deployment","name":"custom-model-fine-tuning-and-deployment","description":"Platform service for fine-tuning open-source models on custom datasets and deploying fine-tuned models as serverless inference endpoints. Developers upload training data (format not specified), configure hyperparameters, and Together AI manages the fine-tuning job on dedicated GPUs. Fine-tuned models are stored on the platform and can be invoked via the same REST API as pre-hosted models. Pricing for fine-tuning not publicly specified (contact sales).","intents":["I want to fine-tune Llama 3.3 70B on my proprietary dataset without managing GPU infrastructure","I need to create domain-specific models (legal, medical, financial) by fine-tuning open-source models","I want to deploy fine-tuned models as serverless endpoints without managing containers or Kubernetes","I need to version and manage multiple fine-tuned models for A/B testing or gradual rollout"],"best_for":["teams with proprietary data wanting to build custom models without ML infrastructure expertise","enterprises requiring domain-specific models (legal, medical, financial) with controlled data","startups iterating on model performance with multiple fine-tuning experiments"],"limitations":["Fine-tuning pricing not publicly specified; requires contacting sales for cost estimates","Training data format and size limits not documented; unclear if JSONL, CSV, or other formats are supported","Hyperparameter configuration options not specified; unclear if learning rate, batch size, epochs are configurable","Fine-tuned models cannot be exported or downloaded; locked into Together AI infrastructure","Fine-tuning job duration and SLA not specified; unclear how long training takes or if there are guarantees","No built-in experiment tracking or hyperparameter optimization; manual tuning required"],"requires":["API key from Together AI account","Training dataset in unspecified format","Base model selection (Llama, Qwen, DeepSeek, etc.)","Understanding of fine-tuning hyperparameters"],"input_types":["training data in unspecified format","hyperparameter configuration"],"output_types":["fine-tuned model deployed as serverless endpoint","model metadata and versioning information"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"together-ai-platform__cap_8","uri":"capability://automation.workflow.dedicated.gpu.cluster.provisioning.for.custom.workloads","name":"dedicated-gpu-cluster-provisioning-for-custom-workloads","description":"Self-service provisioning of dedicated NVIDIA GPU clusters for custom inference, fine-tuning, or other ML workloads. Developers select GPU type and quantity, and Together AI provisions a cluster accessible via SSH or containerized inference endpoints. Clusters can run custom code, custom models, or proprietary inference engines. Pricing is per-GPU-hour (exact rates not specified; contact sales). Designed for teams needing full control over infrastructure and workloads not supported by serverless APIs.","intents":["I want to run custom inference code (not supported by serverless API) on GPUs without managing cloud infrastructure","I need to deploy proprietary models or inference engines on dedicated GPUs","I want to fine-tune models with custom training loops or distributed training frameworks","I need to scale from a few GPUs to thousands of GPUs for large-scale training or inference"],"best_for":["teams with custom ML workloads not supported by serverless APIs","enterprises deploying proprietary models or inference engines","research teams running large-scale training or evaluation experiments"],"limitations":["GPU cluster pricing not publicly specified; requires contacting sales for cost estimates","Specific GPU SKUs available not documented; unclear if H100, A100, L40S, or other models are offered","Cluster provisioning time not specified; unclear how long it takes to allocate GPUs","No built-in monitoring, logging, or observability; developers must implement custom monitoring","No automatic scaling or load balancing; manual cluster management required","Data persistence and backup mechanisms not documented; unclear how to persist training checkpoints or results"],"requires":["API key from Together AI account","GPU cluster provisioning request (format not specified)","SSH access or container runtime for workload deployment","Custom code or models to run on cluster"],"input_types":["custom code (Python, C++, CUDA, etc.)","custom models or model weights","training data or inference inputs"],"output_types":["training checkpoints or model weights","inference results or logs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"together-ai-platform__cap_9","uri":"capability://memory.knowledge.managed.storage.for.model.artifacts.and.data","name":"managed-storage-for-model-artifacts-and-data","description":"Persistent storage service for model weights, training data, fine-tuned models, and inference results. Integrated with fine-tuning and inference services; fine-tuned models are automatically stored and versioned. Offers zero egress fees (data can be downloaded without additional charges). Storage pricing not publicly specified (contact sales). Designed to reduce data transfer costs and simplify artifact management for ML workflows.","intents":["I want to store fine-tuned models and training checkpoints without paying egress fees","I need to version and manage multiple model artifacts (checkpoints, weights, metadata)","I want to download inference results or training data without incurring egress charges","I need persistent storage for embeddings or other inference outputs"],"best_for":["teams fine-tuning models and needing to store and version artifacts","data-intensive ML workflows requiring persistent storage without egress costs","enterprises managing multiple model versions for A/B testing or gradual rollout"],"limitations":["Storage pricing not publicly specified; requires contacting sales for cost estimates","Storage capacity limits not documented; unclear if there are quotas per account or model","No built-in versioning or rollback mechanisms; version management must be manual","No access control or sharing mechanisms documented; unclear if storage can be shared across team members","Data retention policies not specified; unclear how long data is retained after deletion or account closure","No built-in backup or disaster recovery; unclear if data is replicated or backed up"],"requires":["API key from Together AI account","Integration with fine-tuning or inference services"],"input_types":["model weights and artifacts","training data","inference results"],"output_types":["stored artifacts accessible via API or download","versioned model metadata"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"together-ai-platform__headline","uri":"capability://deployment.infra.serverless.ai.model.deployment.platform","name":"serverless ai model deployment platform","description":"A cloud platform for deploying and fine-tuning over 100 open-source AI models, offering serverless inference and optimized performance for large-scale production environments.","intents":["best serverless AI deployment platform","AI model hosting for production","top platforms for fine-tuning open-source models","cloud solutions for scalable AI inference","best GPU clusters for AI model deployment"],"best_for":["high throughput AI applications","large-scale model deployment"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["deployment-infra"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":56,"verified":false,"data_access_risk":"high","permissions":["API key from Together AI account","HTTP client library (curl, requests, axios, etc.)","Network connectivity to Together AI endpoints (geographic regions not specified)","Batch job submission endpoint (format and schema not publicly documented)","Storage for batch results (S3, GCS, or local filesystem)","Function definitions as JSON schemas","Tool implementation (custom code, API endpoints, etc.)","Support for cache control headers or parameters in API client","No additional configuration or code changes required","Image input in supported format (format not specified)"],"failure_modes":["Models are served exclusively through Together AI infrastructure — no option to export or self-host models after testing","Cold-start latency and per-request overhead not publicly specified; may be unsuitable for sub-100ms latency requirements","No control over model versions or update timing; Together AI updates models unilaterally","Rate limiting and concurrent request caps not documented; scaling to 'thousands of GPUs' requires contact with sales","Context length limits per request not specified in public documentation","No real-time response — results available only after batch completion (latency not specified)","Maximum 30 billion tokens per batch per model; larger workloads require multiple batch submissions","No streaming responses; entire batch must complete before results are available","Webhook callback mechanism not documented; polling may require custom retry logic","No built-in result persistence or export to data warehouses; results must be manually retrieved and stored","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.25,"ecosystem":0.15,"match_graph":0.25,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:28.696Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=together-ai-platform","compare_url":"https://unfragile.ai/compare?artifact=together-ai-platform"}},"signature":"W4v4GofFadygEUg1wWubOx5eIJwDTIkV6bKU6LQgC8FMxbDFMm4ZhNxVE8T8HMjO5AGyC4/QFIA5eIPHoWAKCw==","signedAt":"2026-06-20T18:49:25.045Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/together-ai-platform","artifact":"https://unfragile.ai/together-ai-platform","verify":"https://unfragile.ai/api/v1/verify?slug=together-ai-platform","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}