{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"lepton-ai","slug":"lepton-ai","name":"Lepton AI","type":"platform","url":"https://lepton.ai","page_url":"https://unfragile.ai/lepton-ai","categories":["deployment-infra"],"tags":[],"pricing":{"model":"usage","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"lepton-ai__cap_0","uri":"capability://automation.workflow.serverless.llm.api.deployment.with.automatic.gpu.provisioning","name":"serverless llm api deployment with automatic gpu provisioning","description":"Deploy large language models as production-ready HTTP endpoints without managing infrastructure. Lepton automatically allocates GPU resources based on model size and request volume, handling scaling, load balancing, and resource cleanup. Models are containerized and deployed across distributed GPU clusters with transparent resource management.","intents":["I want to expose an LLM as an API without setting up Kubernetes or managing GPU instances","I need to scale LLM inference from 0 to thousands of requests per second automatically","I want to switch between different LLM models without redeploying infrastructure"],"best_for":["startups and solo developers building LLM applications without DevOps expertise","teams needing rapid model iteration without infrastructure overhead","companies wanting to avoid long-term GPU commitments and pay per inference"],"limitations":["Cold start latency for GPU allocation can be 30-60 seconds on first request after idle period","Limited control over exact GPU hardware selection — platform chooses based on model requirements","No guaranteed latency SLAs for burst traffic — queuing occurs during resource contention","Regional availability limited to Lepton's data center footprint"],"requires":["Lepton AI account with API credentials","Model weights accessible via HuggingFace, local file, or URL","Network connectivity to Lepton's API endpoints","Python 3.8+ or language SDK for client integration"],"input_types":["model identifiers (HuggingFace model IDs)","custom model code (Python with PyTorch/TensorFlow)","model weights and configuration files"],"output_types":["HTTP REST API endpoints","JSON responses with model outputs","streaming responses for token-by-token generation"],"categories":["automation-workflow","deployment-infrastructure"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lepton-ai__cap_1","uri":"capability://tool.use.integration.openai.compatible.api.endpoint.generation","name":"openai-compatible api endpoint generation","description":"Automatically wraps deployed models with OpenAI API-compatible interfaces (chat completions, embeddings, image generation endpoints). Clients can use standard OpenAI SDKs and libraries without modification, with request/response schemas matching OpenAI's specification exactly. Supports streaming, function calling, and vision capabilities where applicable.","intents":["I want to use open-source models with my existing OpenAI client code without refactoring","I need to switch between OpenAI and self-hosted models by changing one API endpoint","I want to run local models through the same interface as cloud-based LLMs"],"best_for":["developers with existing OpenAI integrations wanting to reduce vendor lock-in","teams evaluating cost savings by switching to open-source models mid-project","enterprises needing on-premise or private cloud model hosting with standard interfaces"],"limitations":["Some OpenAI-specific features (e.g., fine-tuning API, batch processing) are not available","Response latency may differ from OpenAI due to model inference time — not a drop-in replacement for latency-sensitive applications","Advanced features like vision models require explicit model selection; not all models support all OpenAI capabilities","Rate limiting and quota management differ from OpenAI's system"],"requires":["Lepton AI account with deployed model","OpenAI Python SDK (openai>=1.0) or compatible HTTP client","API key from Lepton (substituted for OpenAI key)","Model must support the requested capability (chat, embeddings, etc.)"],"input_types":["chat messages (role/content format)","text prompts for embeddings","images for vision models"],"output_types":["chat completion responses (JSON with choices array)","embedding vectors (float arrays)","streaming token responses"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lepton-ai__cap_10","uri":"capability://data.processing.analysis.cost.tracking.and.usage.based.billing.with.per.model.pricing","name":"cost tracking and usage-based billing with per-model pricing","description":"Tracks inference costs by model, user, and time period with granular billing based on actual resource consumption (GPU time, tokens generated, images processed). Provides cost forecasting and budget alerts. Supports cost attribution to different projects or departments. Integrates with accounting systems via API.","intents":["I want to understand which models are most expensive and optimize spending","I need to allocate costs to different teams or projects for chargeback","I want to set budget limits and receive alerts when approaching thresholds"],"best_for":["organizations with multiple teams sharing AI infrastructure","cost-conscious startups optimizing inference spending","enterprises needing cost attribution for billing and budgeting"],"limitations":["Billing is based on actual GPU time, not request count — difficult to predict costs without usage patterns","Cost attribution is manual; no automatic cost allocation to projects without tagging","Budget alerts are email-based; no integration with automated spending controls","Historical cost data is limited to 12 months; longer retention requires export"],"requires":["Lepton AI account with billing enabled","Payment method on file (credit card or invoice)","Optional: tagging strategy for cost attribution"],"input_types":["inference requests (automatically tracked)","cost allocation tags (project, team, user)"],"output_types":["cost reports (CSV, JSON) by model, user, time period","cost forecasts based on historical usage","budget alerts via email or webhook"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lepton-ai__cap_11","uri":"capability://text.generation.language.model.inference.with.streaming.token.responses","name":"model inference with streaming token responses","description":"Streams model outputs token-by-token in real-time using HTTP Server-Sent Events (SSE) or WebSocket connections. Reduces perceived latency by showing first token within 100-500ms. Supports cancellation of in-flight requests. Includes token counting and cost estimation during streaming.","intents":["I want to show model outputs to users in real-time as they're generated","I need to reduce perceived latency by streaming tokens instead of waiting for full response","I want to allow users to cancel long-running inference requests"],"best_for":["interactive applications (chatbots, content generation tools, code assistants)","user-facing products where perceived latency matters","applications needing to show partial results before completion"],"limitations":["Streaming adds complexity to client code (must handle SSE or WebSocket)","Token-by-token streaming is slower overall than batch inference (higher overhead per token)","Browser compatibility issues with older clients (IE11 and older don't support SSE)","Streaming responses cannot be cached; each request must be fully processed"],"requires":["Lepton AI account with streaming enabled","Client library supporting SSE or WebSocket (most modern frameworks support this)","Network connection stable enough for streaming (mobile networks may have issues)"],"input_types":["standard LLM inputs (prompts, messages)","streaming parameters (token timeout, max tokens)"],"output_types":["SSE stream with JSON chunks (one per token)","WebSocket messages with token data","final response metadata (total tokens, finish reason)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lepton-ai__cap_2","uri":"capability://automation.workflow.multi.model.inference.with.dynamic.model.selection","name":"multi-model inference with dynamic model selection","description":"Deploy multiple LLMs, vision models, and custom models simultaneously on shared GPU infrastructure with request-time model selection. Routes requests to appropriate model based on task requirements, with built-in model versioning and A/B testing support. Models share GPU memory pools efficiently through dynamic allocation.","intents":["I want to run multiple models (e.g., fast small model + accurate large model) and choose at request time","I need to A/B test different model versions or architectures without separate deployments","I want to optimize cost by routing simple requests to small models and complex requests to large models"],"best_for":["teams building multi-model AI systems with intelligent routing logic","product teams running continuous A/B tests on model performance","cost-conscious builders wanting to minimize GPU utilization while maintaining quality"],"limitations":["GPU memory must accommodate all deployed models simultaneously — total VRAM is shared pool","Model switching adds 10-50ms latency per request due to context loading","No automatic model selection — routing logic must be implemented by user","Models compete for GPU resources; high concurrency on one model can starve others"],"requires":["Lepton AI account with multi-model deployment capability","Client-side routing logic to select model per request","Sufficient GPU memory to load all models (e.g., 2x 40GB A100s for 70B + 13B models)","Monitoring infrastructure to track per-model performance metrics"],"input_types":["model identifier parameter in API request","standard LLM inputs (prompts, messages, images)"],"output_types":["model-specific outputs with metadata indicating which model was used","performance metrics (latency, tokens generated)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lepton-ai__cap_3","uri":"capability://safety.moderation.built.in.model.observability.and.performance.monitoring","name":"built-in model observability and performance monitoring","description":"Automatically collects and visualizes inference metrics including latency, throughput, token counts, error rates, and GPU utilization without additional instrumentation. Provides dashboards showing per-model performance, cost tracking, and request tracing. Integrates with standard monitoring tools via Prometheus-compatible metrics endpoints.","intents":["I want to understand which models are slow and why without adding logging code","I need to track inference costs per model and optimize spending","I want to debug production issues by seeing request traces and GPU utilization patterns"],"best_for":["production teams running multiple models needing visibility into system health","cost-conscious organizations tracking inference spending by model and user","developers debugging latency issues in multi-model deployments"],"limitations":["Metrics retention is limited to 30 days by default; longer retention requires paid tier","Custom metrics require manual instrumentation — only standard inference metrics are automatic","Dashboards are read-only in free tier; custom dashboards require premium","No built-in alerting — must integrate with external monitoring systems for alerts"],"requires":["Lepton AI account with observability features enabled","Access to Lepton dashboard or Prometheus-compatible monitoring system","Optional: Grafana or similar for custom dashboard creation"],"input_types":["inference requests (automatically captured)","model configuration and metadata"],"output_types":["JSON metrics via Prometheus endpoint","dashboard visualizations (latency histograms, throughput graphs)","cost reports (per-model, per-user, per-time-period)","request traces with timing breakdowns"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lepton-ai__cap_4","uri":"capability://text.generation.language.interactive.model.playground.with.parameter.tuning","name":"interactive model playground with parameter tuning","description":"Web-based interface for testing deployed models with real-time parameter adjustment (temperature, top-p, max-tokens, etc.) and response comparison. Supports batch testing with CSV inputs and exports results. Includes prompt engineering tools like variable substitution and few-shot example management. No code required.","intents":["I want to test a model's behavior before integrating it into my application","I need to find optimal hyperparameters (temperature, top-p) for my use case","I want to compare outputs from different models side-by-side for the same prompt"],"best_for":["non-technical stakeholders evaluating model quality","prompt engineers iterating on prompts without writing code","product managers comparing model outputs for feature decisions"],"limitations":["Playground is browser-based; large batch tests (>10k rows) may timeout","No persistent prompt library — prompts are session-based unless manually exported","Parameter tuning is manual; no automated hyperparameter optimization","Batch testing limited to CSV format; no support for complex nested JSON inputs"],"requires":["Lepton AI account with deployed model","Web browser with JavaScript enabled","Model must be in 'ready' state (fully deployed)"],"input_types":["text prompts (free-form or templated)","CSV files for batch testing","model parameters (temperature, top-p, max-tokens, etc.)"],"output_types":["model responses (text, streamed in real-time)","comparison matrices (side-by-side outputs from multiple models)","CSV exports of batch results with metadata"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lepton-ai__cap_5","uri":"capability://code.generation.editing.custom.model.deployment.with.python.code.support","name":"custom model deployment with python code support","description":"Deploy custom inference logic written in Python (PyTorch, TensorFlow, ONNX, or custom code) as managed endpoints. Lepton handles containerization, GPU allocation, and scaling automatically. Supports model loading from local files, HuggingFace, or custom URLs. Includes dependency management and environment variable injection.","intents":["I want to deploy my fine-tuned model or custom inference pipeline as an API","I need to run preprocessing or postprocessing logic alongside model inference","I want to combine multiple models in a single endpoint (e.g., embedding + retrieval + generation)"],"best_for":["ML engineers with custom models or inference pipelines","teams needing specialized preprocessing (image resizing, text normalization) before inference","researchers deploying experimental models without DevOps infrastructure"],"limitations":["Python code must be stateless or use Lepton's provided state management — no persistent local files","Dependency installation happens at deployment time; large dependency trees (>1GB) may timeout","No built-in GPU memory management within custom code — out-of-memory errors crash the endpoint","Debugging is limited to logs; no interactive debugging or profiling tools"],"requires":["Python 3.8+ with PyTorch, TensorFlow, or compatible ML framework","Model weights accessible via URL or included in deployment package","Lepton Python SDK (leptonai package)","Docker knowledge optional but helpful for understanding containerization"],"input_types":["Python code defining inference function","model weights (safetensors, .pt, .pth, .onnx files)","environment variables for configuration"],"output_types":["HTTP API endpoints accepting JSON requests","JSON responses with custom schema defined by user code","streaming responses if implemented in custom code"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lepton-ai__cap_6","uri":"capability://image.visual.image.generation.and.vision.model.deployment","name":"image generation and vision model deployment","description":"Deploy and serve image generation models (Stable Diffusion, DALL-E compatible) and vision models (image classification, object detection, visual QA) as APIs. Handles image encoding/decoding, batch processing, and GPU memory optimization for vision workloads. Supports both synchronous and asynchronous image generation.","intents":["I want to generate images from text prompts via API without managing Stable Diffusion infrastructure","I need to analyze images (classification, detection, captioning) at scale","I want to combine text and vision models in a single application"],"best_for":["applications requiring image generation (marketing content, design tools, creative platforms)","computer vision teams needing scalable inference for classification or detection","multimodal AI applications combining text and image understanding"],"limitations":["Image generation is slow (5-30 seconds per image depending on model) — not suitable for real-time applications","Large batch image processing (>100 images) requires asynchronous handling; synchronous requests timeout","Vision model accuracy varies significantly by model; no automatic model selection for task type","Generated images are stored temporarily; long-term storage requires external integration"],"requires":["Lepton AI account with image model deployment enabled","For image generation: 24GB+ VRAM (e.g., RTX 4090 or A100)","For vision models: 8GB+ VRAM depending on model size","Client code to handle image encoding (base64 or multipart) and decoding"],"input_types":["text prompts for image generation","image files (JPEG, PNG, WebP) for vision models","generation parameters (steps, guidance scale, negative prompts)"],"output_types":["generated images (PNG, JPEG, base64-encoded)","classification scores or detection bounding boxes (JSON)","image captions or visual QA responses (text)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lepton-ai__cap_7","uri":"capability://data.processing.analysis.embedding.model.deployment.with.vector.search.integration","name":"embedding model deployment with vector search integration","description":"Deploy embedding models (text, image, multimodal) that convert inputs to dense vector representations. Integrates with vector databases (Pinecone, Weaviate, Milvus) for semantic search and RAG applications. Supports batch embedding generation and automatic vector normalization. Handles tokenization and context window management.","intents":["I want to generate embeddings for semantic search without managing embedding infrastructure","I need to build a RAG system with embeddings from my custom models","I want to find similar documents or images using embeddings at scale"],"best_for":["teams building semantic search or recommendation systems","RAG applications needing custom or domain-specific embeddings","multimodal search applications (text + image embeddings)"],"limitations":["Embedding generation is sequential; batch processing of 10k+ vectors requires multiple API calls or async handling","Vector database integration is manual — Lepton provides embeddings but doesn't manage vector storage","Embedding model switching requires recomputing all vectors — no automatic model migration","Context window limitations apply; long documents must be chunked before embedding"],"requires":["Lepton AI account with embedding model deployed","Vector database account (Pinecone, Weaviate, etc.) for storage and search","Client code to handle batch embedding requests and vector storage","Chunking strategy for documents exceeding model context window"],"input_types":["text strings for text embeddings","images for image embeddings","mixed text + image for multimodal embeddings"],"output_types":["dense vectors (float arrays, typically 384-1536 dimensions)","vector metadata (token count, model version)","similarity scores when comparing embeddings"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lepton-ai__cap_8","uri":"capability://automation.workflow.request.batching.and.async.inference.for.high.throughput.workloads","name":"request batching and async inference for high-throughput workloads","description":"Automatically batches multiple inference requests together to maximize GPU utilization and throughput. Supports asynchronous request submission with webhook callbacks or polling for results. Implements request queuing with configurable timeout and priority levels. Optimizes for latency-insensitive batch processing (e.g., embedding generation, image processing).","intents":["I want to process thousands of inference requests efficiently without overwhelming the API","I need to submit batch jobs and retrieve results asynchronously without blocking","I want to maximize GPU utilization by batching requests together"],"best_for":["batch processing pipelines (ETL, data enrichment, bulk inference)","applications with variable load patterns needing cost optimization","teams processing large datasets through ML models"],"limitations":["Batching adds latency (100-500ms) compared to single-request inference — not suitable for real-time applications","Batch size is automatic; no user control over batching strategy","Webhook callbacks require publicly accessible endpoint; polling adds complexity","Maximum batch size limited by GPU memory; very large requests may not batch effectively"],"requires":["Lepton AI account with async inference enabled","For webhooks: publicly accessible HTTP endpoint to receive callbacks","Client code to handle async request submission and result retrieval","Idempotency handling for retried requests"],"input_types":["multiple inference requests (same or different models)","batch job specifications with priority levels"],"output_types":["job IDs for tracking async requests","batch results with per-request status","webhook notifications when batch completes"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lepton-ai__cap_9","uri":"capability://automation.workflow.model.versioning.and.canary.deployment","name":"model versioning and canary deployment","description":"Deploy multiple versions of the same model simultaneously with traffic splitting for gradual rollouts. Supports A/B testing by routing a percentage of requests to new model versions. Includes automatic rollback on error rate thresholds. Maintains version history with easy rollback to previous versions.","intents":["I want to test a new model version with 10% of traffic before full rollout","I need to quickly rollback to a previous model version if quality degrades","I want to run A/B tests comparing model versions with statistical significance"],"best_for":["teams running continuous model improvements with risk mitigation","product teams needing data-driven model selection decisions","organizations with strict uptime requirements and rollback needs"],"limitations":["Traffic splitting is percentage-based; no user-level or request-property-based routing","Automatic rollback requires manual configuration of error rate thresholds","Version history is limited to last 10 versions; older versions must be manually archived","Canary deployments require monitoring integration to track error rates"],"requires":["Lepton AI account with versioning enabled","Multiple model versions deployed and ready","Monitoring system to track error rates for automatic rollback","Client code to handle version-specific responses if needed"],"input_types":["model version identifiers","traffic split percentages (e.g., 90% stable, 10% canary)"],"output_types":["responses from either version (transparent to client)","version metadata in response headers for tracking","deployment status and traffic split metrics"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lepton-ai__headline","uri":"capability://deployment.infra.ai.model.deployment.platform","name":"ai model deployment platform","description":"Lepton AI is a platform that enables developers to deploy LLMs, image models, and custom AI models as APIs with minimal coding, featuring automatic GPU management and built-in observability.","intents":["best AI model deployment platform","AI model deployment for minimal coding","AI application platform for GPU management","deploy LLMs as APIs","AI model hosting solutions"],"best_for":["developers looking for easy deployment of AI models"],"limitations":[],"requires":[],"input_types":["AI models"],"output_types":["APIs"],"categories":["deployment-infra"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":56,"verified":false,"data_access_risk":"high","permissions":["Lepton AI account with API credentials","Model weights accessible via HuggingFace, local file, or URL","Network connectivity to Lepton's API endpoints","Python 3.8+ or language SDK for client integration","Lepton AI account with deployed model","OpenAI Python SDK (openai>=1.0) or compatible HTTP client","API key from Lepton (substituted for OpenAI key)","Model must support the requested capability (chat, embeddings, etc.)","Lepton AI account with billing enabled","Payment method on file (credit card or invoice)"],"failure_modes":["Cold start latency for GPU allocation can be 30-60 seconds on first request after idle period","Limited control over exact GPU hardware selection — platform chooses based on model requirements","No guaranteed latency SLAs for burst traffic — queuing occurs during resource contention","Regional availability limited to Lepton's data center footprint","Some OpenAI-specific features (e.g., fine-tuning API, batch processing) are not available","Response latency may differ from OpenAI due to model inference time — not a drop-in replacement for latency-sensitive applications","Advanced features like vision models require explicit model selection; not all models support all OpenAI capabilities","Rate limiting and quota management differ from OpenAI's system","Billing is based on actual GPU time, not request count — difficult to predict costs without usage patterns","Cost attribution is manual; no automatic cost allocation to projects without tagging","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":"2026-05-24T12:16:23.327Z","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=lepton-ai","compare_url":"https://unfragile.ai/compare?artifact=lepton-ai"}},"signature":"albvC2O8q1eEVskGXWE/5GjREFEJtgPRhM+DqEy7UyG2p5/JR06VBUEKVK5k9bUXBapeTLsFKAQUdkGTalhRCQ==","signedAt":"2026-06-21T07:45:42.885Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/lepton-ai","artifact":"https://unfragile.ai/lepton-ai","verify":"https://unfragile.ai/api/v1/verify?slug=lepton-ai","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"}}