{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"cerebrium","slug":"cerebrium","name":"Cerebrium","type":"platform","url":"https://www.cerebrium.ai","page_url":"https://unfragile.ai/cerebrium","categories":["deployment-infra"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"cerebrium__cap_0","uri":"capability://automation.workflow.sub.second.cold.start.gpu.inference.with.memory.gpu.snapshotting","name":"sub-second cold-start gpu inference with memory/gpu snapshotting","description":"Achieves 3.8-8.2 second cold starts for GPU workloads by capturing and restoring memory and GPU state snapshots rather than rebuilding containers from scratch. Uses proprietary snapshot serialization to preserve model weights and runtime state, enabling near-instant resumption of inference without recompilation or model reloading. Automatically manages snapshot lifecycle across deployments and regions.","intents":["Deploy LLM endpoints that respond in under 4 seconds on first request","Run bursty inference workloads without pre-warming GPU capacity","Minimize latency for latency-sensitive applications like voice agents and real-time video processing"],"best_for":["Teams building real-time AI applications (voice agents, video processing, chat interfaces)","Startups with unpredictable traffic patterns who can't justify reserved GPU capacity","Developers deploying vLLM, Stable Diffusion, or other heavy model inference"],"limitations":["Snapshot format is proprietary to Cerebrium — migrating to another platform requires full container rebuild, losing cold-start advantage","Snapshot overhead adds ~8.2s without optimization; competitors report 42-156s baseline","Snapshots must be regenerated when model weights or dependencies change, adding deployment latency","Multi-region snapshot replication timing not documented — may add latency for non-primary regions"],"requires":["GPU hardware (T4, L4, A10, A100, H100, H200, or B200)","Container image with model weights pre-loaded","Cerebrium CLI or API for deployment"],"input_types":["Docker container image","Python entry point with model loading code","ASGI-compatible web application"],"output_types":["Inference results (JSON, text, binary)","Streaming responses via WebSocket or Server-Sent Events"],"categories":["automation-workflow","infrastructure-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cerebrium__cap_1","uri":"capability://automation.workflow.per.second.gpu.billing.with.automatic.elastic.scaling","name":"per-second gpu billing with automatic elastic scaling","description":"Charges for GPU compute in granular per-second increments (e.g., H100 at $0.000944/sec) rather than per-request or reserved hourly blocks, with automatic scale-out/scale-in based on concurrent request volume. Scales from 0 to 2500+ GPUs across multiple clouds without manual capacity planning. Billing stops immediately when workload completes, eliminating idle GPU costs.","intents":["Pay only for actual GPU time used, not reserved capacity or minimum commitments","Handle sudden traffic spikes (10x-100x) without pre-provisioning expensive GPU capacity","Optimize costs for bursty inference workloads with unpredictable demand patterns"],"best_for":["Startups and small teams with limited budgets who can't afford reserved GPU instances","Applications with highly variable traffic (e.g., batch processing triggered by events)","Cost-conscious teams building proof-of-concepts or MVPs"],"limitations":["Per-second billing requires precise workload metering — no aggregation or batching discounts documented","Scaling latency not specified — 'instant autoscaling' claim lacks P50/P99 metrics for scale-out time","Hobby plan capped at 5 concurrent GPUs; Standard at 30 concurrent GPUs — limits burst capacity without Enterprise upgrade","No spot/preemptible pricing tier documented — all GPU pricing is on-demand rates","Egress/bandwidth costs not documented — potential hidden costs for large model downloads or output streaming"],"requires":["Cerebrium account with payment method","Hobby plan (free) or Standard ($100/month) or Enterprise (custom)","Deployed application or inference endpoint"],"input_types":["Inference requests (REST, WebSocket, async)","Batch job submissions"],"output_types":["Billing records (per-second granularity)","Usage metrics and cost dashboards"],"categories":["automation-workflow","cost-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cerebrium__cap_10","uri":"capability://tool.use.integration.custom.domain.and.inter.cluster.networking.configuration","name":"custom domain and inter-cluster networking configuration","description":"Supports custom domain names (CNAME) for inference endpoints and inter-cluster routing for multi-region deployments. Enables private networking between services without exposing endpoints publicly. Automatic SSL/TLS certificate provisioning and renewal for custom domains.","intents":["Use branded domain names (api.mycompany.com) instead of Cerebrium-provided URLs","Route requests between services in different regions or clusters without public internet","Implement private APIs for internal use without exposing to public internet"],"best_for":["Organizations with branded APIs requiring custom domain names","Teams building multi-service architectures with internal service-to-service communication","Applications with strict security requirements prohibiting public endpoints"],"limitations":["Custom domain setup process not documented — unclear if DNS validation or CNAME records are required","Inter-cluster routing topology not documented — unclear if routing is mesh-based or direct","Private networking security model not documented — unclear if network policies or firewall rules are supported","No VPC or private endpoint support mentioned — all networking appears to be internet-routable","Certificate renewal process not documented — unclear if automatic renewal is guaranteed"],"requires":["Custom domain name owned by user","DNS access to create CNAME records","Cerebrium deployment configuration with custom domain"],"input_types":["Custom domain name (CNAME)","Inter-cluster routing configuration"],"output_types":["HTTPS endpoint with custom domain","Routing rules for inter-cluster communication"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cerebrium__cap_11","uri":"capability://automation.workflow.ci.cd.pipeline.integration.with.automated.deployments","name":"ci/cd pipeline integration with automated deployments","description":"Integrates with CI/CD systems to automatically deploy new model versions on code commits or manual triggers. Supports deployment configuration in version control (TOML or YAML) and automated rollout with gradual traffic shifting. Tracks deployment history and enables rollback to previous versions via CLI or API.","intents":["Automatically deploy new model versions when code is pushed to main branch","Implement GitOps-style deployments where infrastructure is defined in version control","Maintain deployment history and enable quick rollbacks to previous versions"],"best_for":["Teams using GitHub, GitLab, or other CI/CD platforms for model deployment","Organizations practicing GitOps and wanting infrastructure-as-code for ML deployments","Developers building MLOps pipelines with automated testing and deployment"],"limitations":["Specific CI/CD platform integrations not documented — unclear which platforms (GitHub Actions, GitLab CI, Jenkins) are supported","Deployment configuration format not documented — unclear if TOML, YAML, or custom format is used","Pre-deployment testing hooks not documented — unclear if integration tests run before deployment","Deployment approval workflows not documented — unclear if manual approval gates are supported","Rollback mechanism not documented — unclear if rollback is instant or requires redeployment"],"requires":["Git repository with code and deployment configuration","CI/CD platform (GitHub Actions, GitLab CI, Jenkins, etc.)","Cerebrium API key for authentication","Deployment configuration file (format TBD)"],"input_types":["Git commits with code changes","Deployment configuration in version control","Manual deployment triggers via CLI or API"],"output_types":["Deployed model versions","Deployment status and logs","Rollback confirmations"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cerebrium__cap_12","uri":"capability://automation.workflow.preemption.aware.workload.management.with.graceful.termination","name":"preemption-aware workload management with graceful termination","description":"Handles preemption events (e.g., spot instance interruptions, resource reclamation) with configurable grace periods for graceful shutdown. Allows applications to save state, flush buffers, and complete in-flight requests before termination. Automatic retry and rescheduling of preempted workloads with exponential backoff.","intents":["Run cost-optimized workloads on preemptible resources without losing in-flight requests","Implement graceful shutdown logic to save model state and avoid data corruption","Automatically retry failed jobs without manual intervention"],"best_for":["Cost-conscious teams willing to tolerate occasional interruptions for lower GPU costs","Batch processing jobs that can be interrupted and resumed","Stateless inference workloads that don't require session persistence"],"limitations":["Grace period duration not documented — unclear how long applications have to shut down","Preemption frequency not documented — unclear how often workloads are interrupted","Retry logic configuration not documented — unclear if exponential backoff is configurable","No preemption-aware pricing tier documented — all GPU pricing appears to be on-demand","Stateful workloads (e.g., with session state) may lose data on preemption despite graceful shutdown"],"requires":["Application code that handles SIGTERM signals for graceful shutdown","Cerebrium deployment with preemption handling enabled","Idempotent request handling for automatic retries"],"input_types":["Inference requests","Preemption signals (SIGTERM)"],"output_types":["Completed inference results","Graceful shutdown confirmations","Retry status and logs"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cerebrium__cap_13","uri":"capability://tool.use.integration.partner.service.integrations.deepgram.rime.with.native.bindings","name":"partner service integrations (deepgram, rime) with native bindings","description":"Provides native integrations with partner services like Deepgram (speech-to-text) and Rime (data validation) with pre-configured authentication and simplified API calls. Eliminates boilerplate for service initialization and error handling. Automatic credential management via Cerebrium's credential store.","intents":["Add speech-to-text capabilities to voice agents without managing Deepgram API keys","Validate input data with Rime without writing custom validation code","Reduce integration boilerplate by using pre-configured partner service bindings"],"best_for":["Teams building voice agents or audio processing applications","Developers needing data validation without implementing custom rules","Applications requiring multiple third-party service integrations"],"limitations":["Only 2 partner services documented (Deepgram, Rime) — limited ecosystem compared to full API access","Partner service pricing and billing not documented — unclear if Cerebrium passes through costs or adds markup","Credential management scope not documented — unclear if credentials are shared across deployments or isolated","No custom partner service integration framework documented — users can't add their own integrations","Partner service API version pinning not documented — unclear if API updates are automatically applied"],"requires":["Cerebrium deployment","Partner service account (Deepgram, Rime, etc.)","API credentials stored in Cerebrium credential manager"],"input_types":["Audio files (Deepgram)","Data for validation (Rime)","Partner service API requests"],"output_types":["Transcribed text (Deepgram)","Validation results (Rime)","Partner service API responses"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cerebrium__cap_2","uri":"capability://automation.workflow.multi.region.global.edge.deployment.with.automatic.failover","name":"multi-region global edge deployment with automatic failover","description":"Deploys inference endpoints across 4+ regions (us-east-1, eu-west-2, eu-north-1, ap-south-1) with automatic request routing to nearest region for low-latency responses. Supports data residency requirements and graceful failover to alternate regions on primary region outage. Snapshot replication across regions enables consistent cold-start performance globally.","intents":["Serve users globally with sub-100ms latency by routing requests to nearest data center","Meet data residency compliance requirements (GDPR, data localization laws) by constraining inference to specific regions","Ensure high availability with automatic failover if primary region becomes unavailable"],"best_for":["Global applications serving users across multiple continents","Regulated industries requiring data residency (finance, healthcare, EU-based companies)","Teams building latency-sensitive applications (real-time voice, video, chat)"],"limitations":["Only 4 regions documented — less coverage than AWS (30+ regions) or GCP (40+ regions)","Snapshot replication timing across regions not specified — may introduce deployment latency for multi-region rollouts","Failover behavior and RTO/RPO metrics not documented — unclear how quickly traffic reroutes on region failure","No explicit SLA or uptime guarantee mentioned in provided documentation","Cross-region data transfer costs not documented — potential hidden costs for inter-region communication"],"requires":["Cerebrium deployment with multi-region configuration","Custom domain or Cerebrium-provided endpoint URL","Supported regions: us-east-1, eu-west-2, eu-north-1, ap-south-1"],"input_types":["Inference requests from global users","Deployment configuration specifying target regions"],"output_types":["Inference results routed from nearest region","Deployment status across regions"],"categories":["automation-workflow","infrastructure-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cerebrium__cap_3","uri":"capability://code.generation.editing.openai.compatible.llm.endpoint.serving.with.vllm.integration","name":"openai-compatible llm endpoint serving with vllm integration","description":"Hosts vLLM-based LLM inference endpoints that expose OpenAI API-compatible interfaces (chat completions, embeddings, etc.) without requiring custom code rewrites. Automatically manages model loading, batching, and GPU memory optimization through vLLM's kernel-level optimizations. Supports streaming responses and async requests with configurable concurrency limits.","intents":["Deploy open-source LLMs (Qwen, Llama, Mistral) with OpenAI API compatibility for drop-in replacement of OpenAI API calls","Reduce LLM inference costs by self-hosting models instead of paying per-token to OpenAI or Anthropic","Build multi-model applications that switch between proprietary and open-source LLMs without code changes"],"best_for":["Teams building LLM applications who want to reduce inference costs vs. OpenAI API","Developers needing fine-grained control over model selection and inference parameters","Applications requiring on-premises or region-locked model inference for compliance"],"limitations":["Only vLLM-compatible models supported — requires model to be in vLLM's supported format (GPTQ, AWQ, etc.)","No built-in model quantization or optimization — users must pre-quantize models before deployment","Streaming response latency not documented — unclear if streaming adds overhead vs. batch responses","No native support for multi-model serving on single endpoint — each model requires separate deployment","Token counting and billing integration with OpenAI SDK not documented — may require custom billing logic"],"requires":["vLLM-compatible model (Qwen, Llama 2/3, Mistral, etc.)","GPU with sufficient VRAM for model (A100 40GB+ recommended for 7B+ models)","OpenAI Python SDK or compatible HTTP client","Cerebrium deployment configuration"],"input_types":["Chat completion requests (OpenAI API format)","Embedding requests","Streaming requests via Server-Sent Events"],"output_types":["Chat completion responses (JSON, OpenAI-compatible format)","Streaming tokens (Server-Sent Events)","Embedding vectors"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cerebrium__cap_4","uri":"capability://automation.workflow.custom.docker.container.deployment.with.private.registry.support","name":"custom docker container deployment with private registry support","description":"Deploys arbitrary Docker containers without SDK requirements or code modifications — users provide Dockerfile or pull from private registries (ECR, Docker Hub, etc.). Cerebrium orchestrates container startup, GPU attachment, networking, and scaling. Supports ASGI-compatible web frameworks (FastAPI, Starlette) and custom Python entry points with automatic port binding and health checks.","intents":["Deploy existing ML applications or web services without rewriting code for Cerebrium's platform","Use custom runtime environments (specific Python versions, system libraries, CUDA versions) not available in pre-built images","Integrate proprietary or closed-source models and inference code"],"best_for":["Teams with existing Docker-based ML applications wanting to migrate to serverless GPU","Developers needing custom system dependencies or specific CUDA/cuDNN versions","Organizations with proprietary models or inference code that can't be open-sourced"],"limitations":["No automatic dependency optimization — users responsible for keeping Docker images lean to minimize startup time","Private registry authentication must be configured per deployment — no centralized credential management documented","Dockerfile best practices for cold-start optimization not enforced — users may inadvertently create slow-starting images","No built-in image layer caching across deployments — each new deployment pulls full image from registry","Container startup timeout not documented — unclear if long-running initialization code will be killed"],"requires":["Docker image (public or private registry)","Dockerfile with EXPOSE port declaration for web services","Private registry credentials (if using private registries)","Cerebrium CLI or API for deployment"],"input_types":["Dockerfile or pre-built Docker image","Environment variables for configuration","HTTP requests to exposed port"],"output_types":["Running container with exposed HTTP endpoint","Logs and metrics from container stdout/stderr"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cerebrium__cap_5","uri":"capability://automation.workflow.real.time.streaming.inference.with.websocket.and.server.sent.events","name":"real-time streaming inference with websocket and server-sent events","description":"Supports streaming responses via WebSocket and Server-Sent Events (SSE) for real-time applications like voice agents, live video processing, and chat interfaces. Maintains persistent connections and streams tokens/frames incrementally without buffering full responses. Integrates with Pipecat framework for voice agent orchestration and supports async request handling for non-blocking I/O.","intents":["Build voice agents that stream audio responses in real-time without waiting for full generation","Create chat interfaces that display tokens as they're generated (streaming LLM responses)","Process video frames incrementally for real-time object detection or video understanding"],"best_for":["Teams building real-time conversational AI (voice agents, chatbots)","Developers creating interactive applications requiring sub-100ms response latency","Applications processing continuous data streams (video, audio, sensor data)"],"limitations":["WebSocket connection timeout not documented — unclear how long idle connections persist","Streaming backpressure handling not documented — unclear if slow clients can cause server-side buffering","No built-in connection pooling or multiplexing — each client requires separate WebSocket connection","Pipecat integration requires specific framework version — tight coupling may limit framework upgrades","Streaming error handling and reconnection logic must be implemented client-side"],"requires":["WebSocket or SSE-compatible client library","ASGI-compatible web framework (FastAPI, Starlette)","Cerebrium deployment with streaming endpoint configuration","Optional: Pipecat framework for voice agent orchestration"],"input_types":["WebSocket messages (JSON, binary)","HTTP requests with streaming response headers","Audio/video frames for real-time processing"],"output_types":["Streaming tokens (text generation)","Streaming frames (video processing)","Audio chunks (voice synthesis)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cerebrium__cap_6","uri":"capability://automation.workflow.batch.job.execution.with.hardware.specification.and.remote.execution","name":"batch job execution with hardware specification and remote execution","description":"Executes long-running Python scripts and training jobs remotely with explicit hardware selection (e.g., 8x H100 GPUs) via CLI command `cerebrium run script.py::function --hardware HOPPER_100:8`. Manages job lifecycle, resource allocation, and result retrieval without requiring containerization. Supports distributed training across multiple GPUs with automatic environment setup.","intents":["Run training jobs on powerful GPU clusters without managing infrastructure or Docker","Execute data processing pipelines that require more compute than local machines","Prototype distributed training without Kubernetes or Ray cluster setup"],"best_for":["ML researchers and data scientists training models without DevOps expertise","Teams running occasional batch jobs that don't justify reserved GPU capacity","Developers prototyping distributed training before productionizing on Kubernetes"],"limitations":["Job timeout not documented — unclear if long-running training jobs (>24 hours) are supported","No checkpointing or resumption mechanism documented — job failure requires restart from scratch","Hardware specification is static — can't dynamically adjust GPU count mid-job","No distributed training framework integration (Ray, Horovod) documented — users must implement distributed logic manually","Result retrieval mechanism not documented — unclear how large model checkpoints are returned to user"],"requires":["Python 3.7+ with dependencies installable via pip","Cerebrium CLI installed (pip, Homebrew, apt, or PowerShell)","Function signature compatible with Cerebrium's execution model","Hardware specification (e.g., HOPPER_100:8 for 8x H100 GPUs)"],"input_types":["Python script with entry point function","Command-line arguments and environment variables","Input data files or S3 paths"],"output_types":["Function return value (JSON, pickle, or custom serialization)","Logs streamed to stdout/stderr","Output files written to persistent storage"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cerebrium__cap_7","uri":"capability://automation.workflow.native.opentelemetry.observability.with.metrics.export","name":"native opentelemetry observability with metrics export","description":"Integrates OpenTelemetry for distributed tracing, metrics collection, and logging with native support for exporting to external monitoring platforms. Provides real-time in-app logging dashboard with per-request visibility, automatic instrumentation of HTTP requests/responses, and custom metric emission. Tracks scaling events, system performance, and inference latency with configurable sampling rates.","intents":["Monitor inference latency, throughput, and error rates in production without custom logging code","Export metrics to existing observability stacks (Datadog, New Relic, Prometheus) for centralized monitoring","Debug performance issues by correlating logs, traces, and metrics across requests"],"best_for":["Teams with existing observability infrastructure (Datadog, New Relic, Prometheus) wanting to integrate Cerebrium","Developers building production ML applications requiring detailed performance visibility","Organizations with compliance requirements for audit logging and request tracing"],"limitations":["OpenTelemetry configuration options not documented — unclear which exporters are supported","Sampling rate configuration not documented — unclear if high-volume inference can be sampled to reduce costs","Log retention varies by plan (7 days Hobby, 30 days Standard, unlimited Enterprise) — short retention may lose historical data","Custom metric emission API not documented — unclear how to emit application-specific metrics","Trace sampling and span limits not documented — high-cardinality traces may be dropped"],"requires":["Cerebrium deployment","OpenTelemetry SDK for Python (if emitting custom metrics)","External monitoring platform (Datadog, New Relic, Prometheus, etc.) for metrics export","API keys or credentials for external platform"],"input_types":["HTTP requests to inference endpoints","Custom metrics emitted via OpenTelemetry SDK","Application logs via stdout/stderr"],"output_types":["Traces (distributed tracing data)","Metrics (latency, throughput, error rates)","Logs (request/response details, errors)","Dashboards in external monitoring platforms"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cerebrium__cap_8","uri":"capability://automation.workflow.gradual.rollout.deployments.with.multi.version.traffic.splitting","name":"gradual rollout deployments with multi-version traffic splitting","description":"Supports gradual rollout of new model versions with configurable traffic splitting (e.g., 10% to new version, 90% to stable version) and automatic rollback on error detection. Enables A/B testing and canary deployments without manual traffic management. Maintains multiple endpoint versions simultaneously with independent scaling and resource allocation.","intents":["Test new model versions in production with real traffic before full rollout","Minimize blast radius of model updates by gradually shifting traffic to new versions","Compare model performance (latency, accuracy, cost) across versions with live traffic"],"best_for":["Teams deploying frequently-updated models (daily or weekly releases)","Organizations with strict SLAs requiring low-risk deployments","Developers building recommendation systems or ranking models requiring A/B testing"],"limitations":["Traffic splitting configuration options not documented — unclear if percentage-based or weighted routing is supported","Rollback trigger conditions not documented — unclear what error rates or latency thresholds trigger automatic rollback","Version retention policy not documented — unclear how long old versions are kept before deletion","No built-in metrics comparison across versions — users must manually compare performance via logs/metrics","Stateful model versions (e.g., with session state) may not work correctly with traffic splitting"],"requires":["Multiple model versions deployed as separate endpoints","Cerebrium deployment configuration with traffic splitting rules","Monitoring/alerting setup for rollback triggers"],"input_types":["Inference requests routed to endpoints","Traffic splitting configuration (percentage or weight)","Rollback trigger conditions (optional)"],"output_types":["Inference results from selected version","Deployment status with traffic allocation percentages","Rollback events and version history"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cerebrium__cap_9","uri":"capability://data.processing.analysis.persistent.file.storage.with.automatic.cleanup.and.billing","name":"persistent file storage with automatic cleanup and billing","description":"Provides persistent file storage accessible across deployments and requests, billed at $0.05/GB/month with first 100GB free. Supports file uploads, downloads, and inter-request persistence for model weights, datasets, and application state. Automatic cleanup of orphaned files and quota management per deployment.","intents":["Store model weights and datasets that persist across container restarts without re-downloading","Cache intermediate results (embeddings, processed data) across multiple inference requests","Implement stateful applications requiring persistent application data"],"best_for":["Applications with large model weights (>1GB) that would be slow to download on each cold start","Batch processing pipelines that generate intermediate results used by multiple jobs","Teams needing persistent caching to reduce inference latency"],"limitations":["Storage performance characteristics not documented — unclear if storage is SSD or HDD, or what I/O throughput is available","No automatic backup or replication — storage loss risk not addressed","Quota enforcement mechanism not documented — unclear if writes are rejected or queued when quota exceeded","File access patterns not optimized — no documented support for streaming large files or range requests","Cleanup policies not documented — unclear how long orphaned files persist before deletion"],"requires":["Cerebrium deployment with storage enabled","File paths within /tmp or application-specific storage directory","Storage quota (100GB free, additional at $0.05/GB/month)"],"input_types":["File uploads via HTTP multipart or direct file writes","File paths for read/write operations"],"output_types":["Persistent files accessible across requests","Storage usage metrics and billing records"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cerebrium__headline","uri":"capability://deployment.infra.serverless.ai.infrastructure.platform.for.deploying.ml.models","name":"serverless ai infrastructure platform for deploying ml models","description":"Cerebrium is a serverless AI infrastructure platform designed for deploying machine learning models with features like sub-second cold starts, automatic scaling, and multi-GPU support, enabling low-latency inference across global edge locations.","intents":["best serverless AI platform","AI model deployment for low-latency inference","serverless infrastructure for machine learning","how to deploy ML models with automatic scaling","top platforms for real-time AI inference"],"best_for":["real-time AI applications","scalable ML deployments"],"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":["GPU hardware (T4, L4, A10, A100, H100, H200, or B200)","Container image with model weights pre-loaded","Cerebrium CLI or API for deployment","Cerebrium account with payment method","Hobby plan (free) or Standard ($100/month) or Enterprise (custom)","Deployed application or inference endpoint","Custom domain name owned by user","DNS access to create CNAME records","Cerebrium deployment configuration with custom domain","Git repository with code and deployment configuration"],"failure_modes":["Snapshot format is proprietary to Cerebrium — migrating to another platform requires full container rebuild, losing cold-start advantage","Snapshot overhead adds ~8.2s without optimization; competitors report 42-156s baseline","Snapshots must be regenerated when model weights or dependencies change, adding deployment latency","Multi-region snapshot replication timing not documented — may add latency for non-primary regions","Per-second billing requires precise workload metering — no aggregation or batching discounts documented","Scaling latency not specified — 'instant autoscaling' claim lacks P50/P99 metrics for scale-out time","Hobby plan capped at 5 concurrent GPUs; Standard at 30 concurrent GPUs — limits burst capacity without Enterprise upgrade","No spot/preemptible pricing tier documented — all GPU pricing is on-demand rates","Egress/bandwidth costs not documented — potential hidden costs for large model downloads or output streaming","Custom domain setup process not documented — unclear if DNS validation or CNAME records are required","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":"2026-05-24T12:16:21.547Z","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=cerebrium","compare_url":"https://unfragile.ai/compare?artifact=cerebrium"}},"signature":"p/atl4+59rR+nNaKWGjL3Fzs76m7RKNh9LOjCIPztxqDTNcSINM8fe9Bon6qQK6mlNUlKfmoCCLFZTCAYq3CDQ==","signedAt":"2026-06-23T06:36:57.198Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/cerebrium","artifact":"https://unfragile.ai/cerebrium","verify":"https://unfragile.ai/api/v1/verify?slug=cerebrium","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"}}