{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"replicate","slug":"replicate","name":"Replicate","type":"platform","url":"https://replicate.com","page_url":"https://unfragile.ai/replicate","categories":["deployment-infra"],"tags":[],"pricing":{"model":"usage","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"replicate__cap_0","uri":"capability://automation.workflow.pay.per.second.gpu.compute.with.automatic.hardware.selection","name":"pay-per-second gpu compute with automatic hardware selection","description":"Replicate abstracts GPU provisioning by billing per second of actual compute time across multiple hardware tiers (A100 80GB, H100, CPU variants). The platform automatically allocates the appropriate hardware based on model requirements and user selection, scaling up/down based on demand. Unlike fixed-cost cloud instances, users pay only for active inference time, with pricing ranging from $0.000025/sec for CPU-small to $0.0028/sec for dual A100 configurations.","intents":["I want to run GPU-intensive models without committing to reserved instances or managing infrastructure","I need cost-predictable inference pricing that scales with actual usage rather than idle time","I want to compare costs across different hardware tiers before running a model"],"best_for":["startups and indie developers avoiding upfront infrastructure costs","teams with variable inference workloads that don't justify reserved capacity","builders prototyping multiple models across different hardware requirements"],"limitations":["Private model deployments bill for idle time (except fast-booting fine-tunes), making sustained low-traffic deployments expensive","No reserved capacity or commitment discounts documented for predictable high-volume workloads","Cold start latency not documented; potential delays on first inference after idle period","Multi-region deployment not available; all compute appears to be in single region"],"requires":["Replicate API token (generated from account dashboard)","Model selection that specifies hardware tier (e.g., gpu-a100-large)","Sufficient account balance or payment method on file"],"input_types":["model identifier","input parameters (varies by model)"],"output_types":["inference results","compute duration in seconds","cost estimate"],"categories":["automation-workflow","deployment-infra"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"replicate__cap_1","uri":"capability://search.retrieval.model.marketplace.discovery.and.public.api.access","name":"model marketplace discovery and public api access","description":"Replicate hosts thousands of community-contributed and official models (from OpenAI, Google, Black Forest Labs, ByteDance, etc.) accessible via a unified API without authentication for public models. Models are discoverable by category (image generation, LLMs, video, audio, speech), display run counts and metadata, and can be invoked via simple API calls with standardized input/output contracts. The marketplace separates official models from community contributions, enabling users to find and compare alternatives.","intents":["I want to discover and try multiple image generation or LLM models without building integrations for each","I need to compare model performance and cost across different providers in one place","I want to use community-contributed models without managing model weights or infrastructure"],"best_for":["developers building multi-model applications (e.g., comparing Flux vs. Ideogram vs. Recraft)","non-technical founders prototyping AI features without ML infrastructure knowledge","researchers evaluating model performance across a curated set of alternatives"],"limitations":["No built-in model comparison tools (e.g., side-by-side output comparison, benchmark results)","Community models lack standardized quality guarantees; vetting responsibility on user","Model discoverability limited to category browsing; no advanced search or filtering by performance metrics","No community engagement features (likes, discussions, reviews) documented; discovery relies on run counts only","Model availability depends on community maintenance; deprecated models may remain listed"],"requires":["Replicate account (free tier available for public model access)","API token for programmatic access","Model identifier in format 'username/model-name' or 'organization/model-name'"],"input_types":["model identifier string","input parameters (image prompt, text, etc.)"],"output_types":["model output (image URL, text, audio file, etc.)","metadata (model version, hardware used)"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"replicate__cap_10","uri":"capability://safety.moderation.safety.checking.and.content.moderation","name":"safety checking and content moderation","description":"Replicate provides safety checking capabilities for predictions, enabling content moderation and filtering of unsafe outputs. The platform can flag or block predictions based on content policies, reducing the risk of generating harmful content. Safety checking is documented as a capability but implementation details are not provided; it likely integrates with model-specific safety mechanisms or external moderation APIs.","intents":["I want to prevent my application from generating harmful or inappropriate content","I need to comply with content policies when deploying models to production","I want to filter predictions before displaying them to users"],"best_for":["applications with user-generated content or public-facing predictions","organizations with strict content policies or regulatory requirements","platforms hosting community-generated models with safety concerns"],"limitations":["Safety checking implementation not documented; unclear which models support it","No configuration options documented; unclear if safety levels can be adjusted","No transparency on false positive/negative rates or moderation accuracy","Safety checking may add latency; no performance impact documented","Unclear if safety checking is automatic or opt-in per model"],"requires":["Model that supports safety checking (not specified)","Replicate API token"],"input_types":["prediction input (varies by model)"],"output_types":["prediction result with safety flag or filtered output"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"replicate__cap_11","uri":"capability://memory.knowledge.data.retention.and.prediction.lifecycle.management","name":"data retention and prediction lifecycle management","description":"Replicate manages prediction lifecycle and data retention, storing prediction results and metadata for a documented period. The platform provides visibility into prediction status (queued, processing, completed, failed) and allows users to retrieve historical predictions. Data retention policies are documented but specific retention periods and deletion mechanisms are not detailed in available documentation.","intents":["I want to retrieve results from past predictions without re-running models","I need to monitor prediction status and debug failures","I want to understand how long Replicate retains my prediction data"],"best_for":["applications with audit or compliance requirements for prediction history","debugging and monitoring workflows where historical data is valuable","cost optimization where re-running predictions is expensive"],"limitations":["Data retention period not specified; unclear if retention is indefinite or time-limited","Deletion mechanisms not documented; unclear if users can manually delete predictions","No data export API documented; unclear if predictions can be bulk exported","Privacy implications not documented; unclear if data is encrypted at rest or in transit","Prediction history API not documented; unclear if there are query or pagination limits"],"requires":["Replicate API token","Prediction ID (returned from initial prediction call)"],"input_types":["prediction ID"],"output_types":["prediction metadata (status, input, output, cost, timestamp)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"replicate__cap_12","uri":"capability://automation.workflow.rate.limiting.and.quota.management","name":"rate limiting and quota management","description":"Replicate enforces rate limits on API requests to prevent abuse and ensure fair resource allocation. Rate limits are documented as a capability but specific limits (requests per second, concurrent predictions, etc.) are not detailed. Users can monitor their usage and quota consumption through the dashboard or API.","intents":["I want to understand API rate limits before building high-volume applications","I need to implement backoff and retry logic for rate-limited requests","I want to monitor my API usage and quota consumption"],"best_for":["high-volume applications requiring rate limit awareness","teams building resilient clients with retry logic","organizations with usage monitoring and cost control requirements"],"limitations":["Rate limit specifics not documented; unclear if limits vary by plan or model","No documented rate limit headers or status codes; unclear how limits are communicated","No quota increase mechanism documented; unclear if users can request higher limits","No rate limit dashboard or usage analytics documented","Unclear if rate limits apply per API token, per account, or per IP address"],"requires":["Replicate API token","HTTP client capable of handling 429 (Too Many Requests) responses"],"input_types":["API request"],"output_types":["HTTP response with rate limit headers (if documented)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"replicate__cap_13","uri":"capability://automation.workflow.gpu.provisioning.and.infrastructure.monitoring","name":"gpu provisioning and infrastructure monitoring","description":"Replicate provides monitoring capabilities for deployed models, enabling users to track resource utilization, prediction latency, and infrastructure health. The platform abstracts GPU provisioning details but provides visibility into deployment status, scaling events, and performance metrics. Monitoring is accessible through the dashboard with documented sections for 'Monitor a deployment' and 'View deployments'.","intents":["I want to monitor my deployed model's performance and resource utilization","I need to understand scaling behavior and predict infrastructure costs","I want to debug performance issues and identify bottlenecks"],"best_for":["teams running production models requiring observability","cost-conscious users optimizing infrastructure spending","developers debugging performance issues and latency"],"limitations":["Monitoring capabilities not detailed; unclear which metrics are available (latency, throughput, GPU utilization, etc.)","No alerting mechanism documented; unclear if users can set up alerts for performance degradation","No log aggregation or detailed error tracking documented","Monitoring API not documented; unclear if metrics can be queried programmatically","No historical data retention for metrics documented; unclear if trends can be analyzed"],"requires":["Replicate account with deployed model","Access to dashboard or monitoring API"],"input_types":["deployment ID"],"output_types":["performance metrics (varies by capability)","deployment status"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"replicate__cap_14","uri":"capability://automation.workflow.image.caching.and.cdn.integration.with.cloudflare","name":"image caching and cdn integration with cloudflare","description":"Replicate integrates with Cloudflare to enable image caching and CDN distribution of prediction outputs. Users can cache image generation results at the edge, reducing bandwidth costs and improving delivery latency for frequently-accessed images. The integration is documented as a guide ('Cache images with Cloudflare') but specific caching strategies and configuration details are not provided.","intents":["I want to reduce bandwidth costs by caching generated images at the edge","I need to improve image delivery latency for global users","I want to serve cached images without re-running expensive generation models"],"best_for":["image generation applications with high traffic and global users","cost-sensitive applications where bandwidth is a significant expense","applications with deterministic outputs (same prompt → same image) that benefit from caching"],"limitations":["Caching strategy not documented; unclear if caching is automatic or requires configuration","Cache invalidation mechanism not documented; unclear how stale images are handled","Cloudflare-specific integration; no support for other CDNs documented","Caching only applies to image outputs; unclear if other model types (text, video) are supported","Cache hit rate and cost savings not quantified; unclear if caching is cost-effective for all workloads"],"requires":["Cloudflare account and domain","Replicate API token","Image generation model"],"input_types":["image generation request"],"output_types":["cached or freshly generated image URL"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"replicate__cap_15","uri":"capability://safety.moderation.rate.limiting.and.quota.management","name":"rate limiting and quota management","description":"Enforce per-user and per-organization rate limits to prevent abuse and manage resource consumption. Developers can configure request limits (e.g., 100 requests/minute), burst allowances, and quota thresholds. Rate limit headers in API responses indicate remaining capacity, enabling clients to implement backoff strategies. Exceeding limits returns HTTP 429 (Too Many Requests) with retry-after guidance.","intents":["Prevent abuse and runaway costs from compromised API keys","Manage shared resource pools fairly across team members","Implement client-side backoff and retry logic","Monitor and alert on quota usage"],"best_for":["Teams sharing API keys across multiple applications","Public APIs requiring abuse prevention","Organizations with strict cost controls"],"limitations":["Rate limiting configuration details not documented; unclear how to set custom limits","No per-model or per-hardware rate limiting; limits apply globally","No quota alerts or notifications; manual monitoring required","Rate limit headers not documented; unclear which headers are returned"],"requires":["Replicate API token","HTTP client capable of reading response headers"],"input_types":["API request with authentication"],"output_types":["HTTP 429 response if rate limit exceeded","Rate limit headers (X-RateLimit-Limit, X-RateLimit-Remaining, X-RateLimit-Reset)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"replicate__cap_2","uri":"capability://automation.workflow.streaming.output.for.long.running.inference","name":"streaming output for long-running inference","description":"Replicate supports streaming output for models that generate results incrementally (e.g., text generation, image generation with progressive refinement). The API streams results back to the client as they become available, reducing perceived latency and enabling real-time UI updates. Streaming is implemented via HTTP streaming or WebSocket-like patterns, allowing clients to consume output chunks without waiting for full completion.","intents":["I want to show users real-time progress as a model generates output (e.g., text appearing word-by-word)","I need to reduce perceived latency by streaming partial results instead of waiting for full completion","I want to build interactive applications where users see model output as it's generated"],"best_for":["web applications with real-time UI requirements (chat interfaces, progressive image generation)","mobile apps where bandwidth is constrained and progressive loading improves UX","streaming-first architectures (e.g., Next.js server components, WebSocket-based dashboards)"],"limitations":["Streaming support varies by model; not all models support incremental output","Streaming adds complexity to error handling (partial results may be delivered before failure)","No documented backpressure mechanism; client must handle streaming rate","Streaming latency not documented; unclear if streaming adds overhead vs. batch output"],"requires":["Model that supports streaming output (documented per model)","HTTP client capable of handling streaming responses (e.g., fetch with ReadableStream, axios with responseType: 'stream')","Replicate API token"],"input_types":["model identifier","input parameters"],"output_types":["streamed output chunks (text, image data, etc.)","completion signal"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"replicate__cap_3","uri":"capability://automation.workflow.webhook.based.asynchronous.prediction.delivery","name":"webhook-based asynchronous prediction delivery","description":"Replicate supports webhooks for long-running predictions, enabling asynchronous workflows where results are delivered to a user-specified URL instead of blocking on API calls. When a prediction completes, Replicate sends an HTTP POST to the webhook URL with the result payload. Webhooks include HMAC signatures for verification, allowing secure integration with external systems (Discord bots, Slack notifications, database updates, etc.).","intents":["I want to process predictions asynchronously without blocking my application on long-running model inference","I need to integrate Replicate predictions into a workflow (e.g., save results to database, notify users via email)","I want to build serverless applications that respond to prediction completion events"],"best_for":["batch processing pipelines where latency is not critical","serverless architectures (AWS Lambda, Cloudflare Workers, Val Town) that can't maintain long-lived connections","integrations with external services (Discord, Slack, email) that require event-driven triggers","applications with unpredictable prediction duration where blocking is infeasible"],"limitations":["Webhook delivery is not guaranteed; no built-in retry mechanism documented","No webhook event filtering; all prediction events sent to the same URL","Webhook payload size not documented; large results may exceed HTTP limits","No webhook replay or manual retry mechanism documented","Requires publicly accessible webhook endpoint; not suitable for private networks without tunneling"],"requires":["Publicly accessible HTTP endpoint (HTTPS recommended for security)","Ability to verify HMAC signatures (Replicate provides signature in X-Replicate-Content-SHA256 header)","Replicate API token","Webhook URL registered in Replicate dashboard or passed per-prediction"],"input_types":["webhook URL","prediction parameters"],"output_types":["HTTP POST to webhook with prediction result","status code 200 to acknowledge receipt"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"replicate__cap_4","uri":"capability://code.generation.editing.custom.model.deployment.via.cog.containerization","name":"custom model deployment via cog containerization","description":"Replicate enables users to package custom ML models using Cog, an open-source tool that standardizes model packaging into a container format with defined inputs/outputs. Users define a Cog YAML configuration specifying model weights, dependencies, and a Python Predict class, then deploy to Replicate. The platform handles containerization, versioning, and scaling. Models are billed on dedicated hardware with auto-scaling based on traffic, though idle time is charged (except for fast-booting fine-tunes).","intents":["I want to deploy my custom fine-tuned model or proprietary model without managing Kubernetes or Docker infrastructure","I need to version and iterate on my model while maintaining a stable API endpoint","I want to monetize my model by hosting it on Replicate's marketplace"],"best_for":["ML researchers and practitioners with custom models (fine-tuned LLMs, custom diffusion models)","teams building proprietary models that need versioning and scaling without DevOps overhead","model creators monetizing their work through Replicate's marketplace"],"limitations":["Cog is Python-only; no support for models in other languages (Go, Rust, etc.) without custom Docker","Idle time billing for private models makes low-traffic deployments expensive; only fast-booting fine-tunes avoid idle charges","Model export from Replicate not documented; potential vendor lock-in to Cog format","No built-in A/B testing or canary deployment for model versions","Cold start latency not documented; unclear if custom models have different startup characteristics than official models","Secrets management required for model weights; no built-in encryption at rest documented"],"requires":["Cog CLI installed (Python package)","Python 3.8+ environment","Model weights and inference code","Replicate account with deployment permissions","Docker installed (Cog uses Docker for containerization)"],"input_types":["Cog YAML configuration","Python Predict class","model weights (file paths or URLs)"],"output_types":["deployed model endpoint","versioned API","model page on Replicate marketplace"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"replicate__cap_5","uri":"capability://automation.workflow.token.based.and.output.based.pricing.for.llms.and.image.models","name":"token-based and output-based pricing for llms and image models","description":"Replicate offers alternative billing models for certain model categories: LLMs are billed per input/output tokens (e.g., Claude: $3.00/million input tokens, $0.015/thousand output tokens), while image models are billed per output image (e.g., Flux Pro: $0.04/image, Flux Schnell: $3.00/thousand images). Video models use per-second output billing. This pricing model provides predictability for high-volume applications where token/output count is known in advance, contrasting with per-second GPU billing for other models.","intents":["I want predictable pricing for LLM inference based on token count rather than GPU time","I need to estimate costs for image generation at scale without worrying about GPU utilization variance","I want to compare token pricing across LLM providers (Claude, GPT, DeepSeek) on a single platform"],"best_for":["applications with high-volume LLM inference where token count is predictable","image generation services where per-image pricing aligns with business model","cost-sensitive applications where token-based pricing is cheaper than per-second GPU billing"],"limitations":["Token pricing varies significantly by model; no unified pricing across LLM providers","Output token pricing is higher than input pricing, incentivizing shorter responses","No bulk discounts or volume pricing documented for high-volume applications","Image model pricing varies by quality tier (Flux Pro vs. Schnell); users must choose between cost and quality","Video pricing per output second may be expensive for long-form content generation","No cost estimation API; users must calculate costs manually or check model page"],"requires":["Replicate API token","Model that supports token/output-based pricing (documented per model)","Ability to estimate token count or output volume for cost planning"],"input_types":["text prompt (for LLMs)","image prompt or image (for image models)","video parameters (for video models)"],"output_types":["LLM text output with token count","image URL with output count","video file with duration"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"replicate__cap_6","uri":"capability://automation.workflow.model.versioning.and.fine.tuning.infrastructure","name":"model versioning and fine-tuning infrastructure","description":"Replicate supports model versioning, allowing users to deploy multiple versions of the same model and route traffic between them. Fine-tuning infrastructure is available for image models (documented guide: 'Fine-tune an image model'), enabling users to create custom variants of base models. Fine-tuned models are billed differently (fast-booting fine-tunes avoid idle charges), reducing deployment costs for frequently-accessed custom variants.","intents":["I want to deploy a new model version without breaking existing API consumers","I need to fine-tune a base model on custom data and serve the fine-tuned variant","I want to A/B test different model versions or fine-tuned variants"],"best_for":["teams iterating on model performance with multiple versions in production","applications requiring custom fine-tuned models (e.g., style-specific image generation)","researchers comparing model variants without managing separate deployments"],"limitations":["Fine-tuning support documented only for image models; unclear if LLM fine-tuning is available","No built-in A/B testing framework; version routing must be implemented client-side","Fine-tuning infrastructure details not documented (training time, data format, cost)","No automated rollback mechanism for bad model versions","Version management UI/API not documented; unclear how versions are created and promoted"],"requires":["Replicate account with model deployment permissions","Base model compatible with fine-tuning (image models confirmed)","Training data in appropriate format (format not specified in documentation)"],"input_types":["base model identifier","training data","fine-tuning parameters"],"output_types":["fine-tuned model version","versioned API endpoint"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"replicate__cap_7","uri":"capability://tool.use.integration.multi.language.sdk.support.with.standardized.api.contracts","name":"multi-language sdk support with standardized api contracts","description":"Replicate provides official SDKs for Node.js and Python, plus a documented HTTP API, enabling developers to integrate predictions into applications regardless of language. The SDKs abstract HTTP details and provide consistent interfaces (e.g., replicate.run(model, {input})) across languages. The HTTP API follows RESTful conventions with JSON request/response bodies, enabling integration from any language or environment (Bash, Go, Rust, etc.).","intents":["I want to integrate Replicate predictions into my Node.js or Python application without managing HTTP details","I need to call Replicate from a language without an official SDK (Go, Rust, etc.)","I want to use Replicate in a serverless environment (AWS Lambda, Cloudflare Workers) with minimal dependencies"],"best_for":["full-stack JavaScript teams using Node.js backends","Python data science teams and ML engineers","polyglot teams needing language-agnostic HTTP API access","serverless environments with strict dependency constraints"],"limitations":["Official SDKs limited to Node.js and Python; other languages must use HTTP API","SDK documentation quality not assessed; unclear if SDKs provide feature parity with HTTP API","No CLI tool documented; command-line usage requires curl or similar HTTP clients","SDK versioning and backward compatibility not documented","No async/await patterns documented for Python SDK; unclear if it supports asyncio"],"requires":["Node.js 14+ (for Node.js SDK) or Python 3.8+ (for Python SDK)","Replicate API token (environment variable or constructor parameter)","HTTP client library (included in SDKs; raw HTTP requires curl, fetch, requests, etc.)"],"input_types":["model identifier","input parameters (JSON-serializable)"],"output_types":["prediction result (JSON or file URL)","prediction metadata (ID, status, cost)"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"replicate__cap_8","uri":"capability://automation.workflow.ci.cd.integration.for.model.deployment.and.updates","name":"ci/cd integration for model deployment and updates","description":"Replicate provides guides for GitHub Actions-based CI/CD pipelines, enabling automated model deployment and updates. Users can trigger model deployments from Git commits, run tests on new versions, and manage model lifecycle through version control. The platform supports secrets management for API tokens and model weights, integrating with GitHub Secrets for secure credential handling.","intents":["I want to deploy model updates automatically when I push to a Git repository","I need to test model changes before deploying to production","I want to manage model versions alongside code versions in Git"],"best_for":["teams using GitHub for version control and CI/CD","ML teams adopting GitOps practices for model deployment","organizations requiring audit trails and approval workflows for model changes"],"limitations":["CI/CD integration documented only for GitHub Actions; no GitLab CI, Jenkins, or other platforms mentioned","Deployment automation details not documented; unclear if full model lifecycle (build, test, deploy) is automated","No approval workflow or promotion gates documented (e.g., staging → production)","Secrets management relies on GitHub Secrets; no built-in Replicate secrets vault documented","No rollback automation documented; manual version management may be required"],"requires":["GitHub repository with model code and Cog configuration","GitHub Actions enabled","Replicate API token stored in GitHub Secrets","Workflow YAML file defining deployment steps"],"input_types":["Git commit with model changes","GitHub Actions workflow definition"],"output_types":["deployed model version","deployment logs and status"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"replicate__cap_9","uri":"capability://tool.use.integration.framework.and.platform.integrations.next.js.discord.swiftui.comfyui","name":"framework and platform integrations (next.js, discord, swiftui, comfyui)","description":"Replicate provides integration guides for popular frameworks and platforms, enabling developers to embed predictions into applications without building custom API clients. Documented integrations include Next.js (web applications), Discord (bots), SwiftUI (iOS apps), ComfyUI (visual node-based workflows), and others. Each integration provides boilerplate code and best practices for handling predictions, webhooks, and results within the framework's patterns.","intents":["I want to add image generation to my Next.js website without building a custom API layer","I need to create a Discord bot that uses Replicate models for image or text generation","I want to build an iOS app with Replicate predictions using SwiftUI"],"best_for":["web developers using Next.js, Vite, or similar frameworks","Discord bot developers adding AI capabilities","iOS developers building AI-powered mobile apps","creative professionals using ComfyUI for visual workflows"],"limitations":["Integration guides are documentation-only; no official libraries or SDKs for frameworks (except HTTP API)","Integrations may become outdated as frameworks evolve","No integration for other popular frameworks (Django, FastAPI, React Native, Flutter) documented","ComfyUI integration unclear; appears to be a guide rather than native support","No managed hosting for Discord bots or iOS apps; users must deploy separately"],"requires":["Framework or platform (Next.js, Discord.js, SwiftUI, ComfyUI, etc.)","Replicate API token","HTTP client library (included in most frameworks)"],"input_types":["framework-specific request (e.g., Next.js API route, Discord message)","Replicate model parameters"],"output_types":["framework-specific response (e.g., JSON, Discord embed)","prediction result"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"replicate__headline","uri":"capability://deployment.infra.api.platform.for.deploying.and.running.machine.learning.models","name":"api platform for deploying and running machine learning models","description":"Replicate is an API platform that allows users to deploy and run machine learning models, offering thousands of community models and a pay-per-second pricing model, making it ideal for image, video, and audio processing tasks.","intents":["best API for deploying ML models","ML model hosting platform for image generation","community-driven ML model API","affordable ML model deployment solutions","how to run ML models via API"],"best_for":["developers looking for flexible ML deployment","users needing community-contributed models"],"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":["Replicate API token (generated from account dashboard)","Model selection that specifies hardware tier (e.g., gpu-a100-large)","Sufficient account balance or payment method on file","Replicate account (free tier available for public model access)","API token for programmatic access","Model identifier in format 'username/model-name' or 'organization/model-name'","Model that supports safety checking (not specified)","Replicate API token","Prediction ID (returned from initial prediction call)","HTTP client capable of handling 429 (Too Many Requests) responses"],"failure_modes":["Private model deployments bill for idle time (except fast-booting fine-tunes), making sustained low-traffic deployments expensive","No reserved capacity or commitment discounts documented for predictable high-volume workloads","Cold start latency not documented; potential delays on first inference after idle period","Multi-region deployment not available; all compute appears to be in single region","No built-in model comparison tools (e.g., side-by-side output comparison, benchmark results)","Community models lack standardized quality guarantees; vetting responsibility on user","Model discoverability limited to category browsing; no advanced search or filtering by performance metrics","No community engagement features (likes, discussions, reviews) documented; discovery relies on run counts only","Model availability depends on community maintenance; deprecated models may remain listed","Safety checking implementation not documented; unclear which models support it","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":"2026-05-24T12:16:25.061Z","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=replicate","compare_url":"https://unfragile.ai/compare?artifact=replicate"}},"signature":"GeIhnJe6VTXBeGg8LKkY0eJYComn/k4qCxnqLgwAMtiIdjYDQ/+07UFSbj12oC8o50ZuDu0Oi35qh5zq4YqHDQ==","signedAt":"2026-06-20T14:28:32.579Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/replicate","artifact":"https://unfragile.ai/replicate","verify":"https://unfragile.ai/api/v1/verify?slug=replicate","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"}}