{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"bentoml","slug":"bentoml","name":"BentoML","type":"framework","url":"https://github.com/bentoml/BentoML","page_url":"https://unfragile.ai/bentoml","categories":["deployment-infra"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"bentoml__cap_0","uri":"capability://code.generation.editing.decorator.based.service.definition.with.class.to.api.transformation","name":"decorator-based service definition with class-to-api transformation","description":"Transforms Python classes into production-grade API services using @bentoml.service and @bentoml.api decorators. The framework introspects decorated methods, generates OpenAPI schemas automatically via src/_bentoml_sdk/service/openapi.py, and maps them to HTTP/gRPC endpoints. Service lifecycle is managed through a factory pattern (src/_bentoml_sdk/service/factory.py) that handles initialization, dependency injection, and multi-process worker spawning.","intents":["Define ML model serving endpoints without boilerplate HTTP/gRPC scaffolding","Automatically generate OpenAPI documentation from service code","Compose multiple models into a single service with shared dependencies","Deploy the same service code to local, containerized, and cloud environments"],"best_for":["ML engineers building production inference APIs","Teams migrating from Flask/FastAPI to standardized ML serving","Organizations needing reproducible service definitions across environments"],"limitations":["Python-only; no native support for services written in other languages","Decorator-based approach requires understanding BentoML conventions; steeper learning curve than plain FastAPI","Service state must be serializable for multi-process worker distribution"],"requires":["Python 3.8+","BentoML package installed","Understanding of Python decorators and class-based design"],"input_types":["Python class definitions","method signatures with type hints"],"output_types":["HTTP endpoints","gRPC service definitions","OpenAPI schema"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"bentoml__cap_1","uri":"capability://automation.workflow.adaptive.dynamic.batching.with.configurable.queue.and.timeout.policies","name":"adaptive dynamic batching with configurable queue and timeout policies","description":"Implements request batching at the serving layer (src/_bentoml_impl/server/serving.py, Task Queue System) that automatically groups incoming requests into batches before passing them to model inference. Batching is configurable per-endpoint with parameters for batch size, timeout, and queue strategy. The system uses a task queue that accumulates requests up to a maximum batch size or timeout threshold, then dispatches them together to maximize GPU utilization and throughput.","intents":["Maximize GPU throughput by batching multiple inference requests together","Reduce per-request latency overhead for high-concurrency scenarios","Configure batching behavior per-endpoint based on model characteristics","Balance latency and throughput with configurable timeout policies"],"best_for":["Teams serving large models on GPU infrastructure","High-throughput inference services with variable request arrival rates","Scenarios where batch inference is significantly faster than single-request inference"],"limitations":["Batching adds latency for individual requests waiting in the queue (typically 10-100ms depending on timeout config)","Requires model to support variable batch sizes; some models have fixed batch size requirements","Batching effectiveness depends on request arrival rate; low-traffic services may not benefit","No built-in adaptive batching based on observed latency/throughput tradeoffs"],"requires":["BentoML 1.0+","Model that supports batched inference","Configuration of batch_size and timeout_ms in service config"],"input_types":["HTTP/gRPC requests","service configuration"],"output_types":["batched inference requests","response aggregation"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"bentoml__cap_10","uri":"capability://data.processing.analysis.framework.agnostic.model.integration.with.automatic.serialization","name":"framework-agnostic model integration with automatic serialization","description":"Supports loading and serving models from multiple ML frameworks (PyTorch, TensorFlow, scikit-learn, XGBoost, ONNX, etc.) with framework-specific serialization and deserialization (Framework Integrations in DeepWiki). The framework detects the model type automatically and applies the appropriate loader, handling framework-specific quirks (e.g., PyTorch device placement, TensorFlow graph mode). Custom frameworks can be integrated via a plugin interface.","intents":["Serve models from any major ML framework without framework-specific code","Automatically handle framework-specific serialization and deserialization","Support ONNX models for framework-agnostic inference","Integrate custom or proprietary model formats via plugins"],"best_for":["Teams using multiple ML frameworks and needing a unified serving interface","Organizations migrating between frameworks without rewriting service code","Services requiring ONNX model support for cross-framework compatibility"],"limitations":["Framework-specific optimizations may be lost in the abstraction; some frameworks have better performance with native serving","Custom model objects (e.g., PyTorch custom layers) must be serializable; some frameworks have limitations","ONNX conversion may lose model features or require manual optimization","No built-in support for framework-specific features (e.g., TensorFlow Serving's model versioning)"],"requires":["BentoML 1.0+","Framework-specific libraries installed (torch, tensorflow, sklearn, xgboost, onnx, etc.)","Models saved in framework-native format or ONNX"],"input_types":["trained models in framework-native format","ONNX models"],"output_types":["loaded model instances","inference results"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"bentoml__cap_11","uri":"capability://automation.workflow.local.development.serving.with.hot.reload.and.debugging.support","name":"local development serving with hot-reload and debugging support","description":"Provides a local development server (Local Development Serving in DeepWiki) that serves Bentos with automatic code reloading on file changes, enabling rapid iteration. The server runs in a single process with full Python debugger support, allowing developers to set breakpoints and inspect service state. Configuration changes are reflected immediately without restarting the server, and detailed error messages are provided for debugging.","intents":["Develop and test services locally with fast iteration cycles","Debug service code with Python debugger (pdb, IDE debuggers)","Test API endpoints locally before deployment","Verify model behavior and data transformations during development"],"best_for":["Individual developers and small teams building services","Rapid prototyping and experimentation phases","Debugging complex inference pipelines or data transformations"],"limitations":["Single-process development server doesn't reflect multi-worker production behavior; concurrency issues may not be caught","Hot-reload may not work correctly for all code changes (e.g., class definitions, imports); full restart may be needed","Performance characteristics differ from production (no batching optimization, no worker pool overhead)","Not suitable for load testing or performance benchmarking; use production deployment for realistic metrics"],"requires":["BentoML 1.0+","Python 3.8+ with development tools","IDE or debugger with Python support (optional but recommended)"],"input_types":["service code","configuration files"],"output_types":["local HTTP/gRPC server","debug output"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"bentoml__cap_12","uri":"capability://tool.use.integration.client.sdk.with.async.await.support.and.remote.service.communication","name":"client sdk with async/await support and remote service communication","description":"Provides Python client libraries (Client SDK in DeepWiki) for consuming BentoML services with both synchronous and asynchronous APIs. Clients automatically discover service endpoints, handle serialization/deserialization, and support streaming responses. The SDK includes task queue integration for asynchronous job submission and result polling, enabling decoupled request/response patterns for long-running inference tasks.","intents":["Call BentoML services from Python applications with type-safe client code","Use async/await for non-blocking service calls in concurrent applications","Submit long-running inference tasks asynchronously and poll for results","Stream responses from services for real-time data processing"],"best_for":["Python applications consuming BentoML services","Async/concurrent applications requiring non-blocking service calls","Batch processing pipelines with long-running inference tasks"],"limitations":["Python-only; non-Python clients must use HTTP/gRPC directly","Async support requires async-compatible service methods; blocking code will block the event loop","Task queue integration requires separate task queue infrastructure; no built-in persistence","No built-in retry logic or circuit breaker patterns; requires external libraries for resilience"],"requires":["BentoML 1.0+","Python 3.8+ with asyncio support","Network connectivity to BentoML service"],"input_types":["service endpoint URL","request data"],"output_types":["response data","streaming responses","task IDs"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"bentoml__cap_13","uri":"capability://automation.workflow.configuration.management.with.environment.specific.overrides.and.validation","name":"configuration management with environment-specific overrides and validation","description":"Provides a hierarchical configuration system (Configuration System in DeepWiki) with support for bentofile.yaml, environment variables, and runtime overrides. Configuration is validated against a schema and supports environment-specific profiles (dev, staging, prod) with inheritance. The system handles service configuration (concurrency, batching), build configuration (dependencies, base image), and image configuration (resource limits, environment variables).","intents":["Define service configuration declaratively in bentofile.yaml","Override configuration per environment without code changes","Validate configuration at build time to catch errors early","Manage resource allocation, concurrency, and batching parameters"],"best_for":["Teams managing multiple deployment environments (dev, staging, prod)","Services with environment-specific resource requirements","Organizations requiring configuration validation and audit trails"],"limitations":["Configuration schema is not fully documented; discovering available options requires reading source code","No built-in secrets management; sensitive values must be injected via environment variables","Configuration validation is limited; some invalid configurations are only caught at runtime","No built-in configuration versioning or rollback; requires external tools for configuration management"],"requires":["BentoML 1.0+","bentofile.yaml in service directory","Understanding of YAML syntax and BentoML configuration options"],"input_types":["bentofile.yaml","environment variables","runtime overrides"],"output_types":["validated configuration","service runtime configuration"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"bentoml__cap_14","uri":"capability://safety.moderation.monitoring.and.observability.with.metrics.collection.and.health.checks","name":"monitoring and observability with metrics collection and health checks","description":"Integrates observability features (Monitoring and Observability in DeepWiki) including Prometheus metrics collection, health check endpoints, and structured logging. The framework automatically collects metrics for request latency, throughput, error rates, and resource utilization. Health checks verify service readiness and liveness, enabling Kubernetes integration. Metrics are exposed via standard Prometheus endpoints for integration with monitoring stacks.","intents":["Monitor service performance with Prometheus metrics","Detect service failures with health check endpoints","Track inference latency and throughput in production","Integrate with monitoring stacks (Prometheus, Grafana, Datadog)"],"best_for":["Production deployments requiring observability","Kubernetes environments with Prometheus monitoring","Teams needing performance baselines and SLO tracking"],"limitations":["Metrics collection adds overhead (~1-2% latency); not suitable for ultra-low-latency services","Health checks are basic (readiness, liveness); custom health logic requires code modification","No built-in alerting; requires external monitoring stack (Prometheus AlertManager, etc.)","Metrics retention is limited; requires external time-series database for long-term storage"],"requires":["BentoML 1.0+","Prometheus or compatible metrics collector","Kubernetes (optional, for health check integration)"],"input_types":["service metrics","health check requests"],"output_types":["Prometheus metrics","health check responses"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"bentoml__cap_2","uri":"capability://tool.use.integration.multi.protocol.serving.with.http.and.grpc.endpoints.from.single.service.definition","name":"multi-protocol serving with http and grpc endpoints from single service definition","description":"Generates both HTTP (ASGI-based, src/_bentoml_impl/server/app.py) and gRPC servers from a single service definition. The HTTP server handles REST endpoints with automatic request/response serialization, while the gRPC server provides low-latency binary protocol support. Both servers share the same underlying service instance and request processing pipeline (src/_bentoml_impl/server/serving.py), with protocol-specific adapters handling serialization and endpoint mapping.","intents":["Serve the same model via REST API for web clients and gRPC for high-performance services","Support both synchronous HTTP and asynchronous gRPC streaming for different use cases","Avoid maintaining separate service implementations for different protocols","Enable gradual migration from HTTP to gRPC without code changes"],"best_for":["Services needing both web-accessible REST and high-performance internal APIs","Microservice architectures mixing HTTP and gRPC communication","Teams wanting protocol flexibility without code duplication"],"limitations":["gRPC requires .proto file generation and client library compilation; adds deployment complexity","HTTP and gRPC servers run as separate processes; no shared connection pooling","Request/response types must be serializable to both JSON (HTTP) and protobuf (gRPC)","gRPC streaming not supported for all model types; requires async-compatible inference"],"requires":["BentoML 1.0+","gRPC libraries installed (grpcio, grpcio-tools)","Service configuration enabling both http and grpc servers"],"input_types":["HTTP requests (JSON)","gRPC requests (protobuf)"],"output_types":["HTTP responses (JSON)","gRPC responses (protobuf)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"bentoml__cap_3","uri":"capability://memory.knowledge.model.versioning.and.storage.with.framework.agnostic.model.registry","name":"model versioning and storage with framework-agnostic model registry","description":"Provides a centralized model registry (Model Management in DeepWiki) that stores and versions ML models across frameworks (PyTorch, TensorFlow, scikit-learn, XGBoost, etc.) using a standardized format. Models are saved with metadata (framework, version, custom objects) and retrieved via bentoml.models.get() with automatic deserialization. The registry supports local filesystem storage and cloud backends, with model artifacts tracked by name and version tag.","intents":["Store and version multiple model checkpoints without manual file management","Load models in services with a single line of code (bentoml.models.get())","Track model lineage and enable rollback to previous versions","Share models across multiple services and deployment environments"],"best_for":["ML teams managing multiple model versions and frameworks","Services requiring model hot-swapping or A/B testing","Organizations needing audit trails for model deployments"],"limitations":["Model registry is local to the Bento artifact; no built-in multi-tenant model sharing across organizations","Large models (>10GB) require significant storage; no built-in deduplication or compression","Custom model objects (e.g., PyTorch custom layers) must be serializable; some frameworks have limitations","No built-in model validation or schema enforcement; relies on framework-specific serialization"],"requires":["BentoML 1.0+","Framework-specific libraries (torch, tensorflow, sklearn, etc.)","Disk space for model artifacts"],"input_types":["trained model objects","model metadata"],"output_types":["versioned model artifacts","model metadata"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"bentoml__cap_4","uri":"capability://automation.workflow.bento.artifact.packaging.with.reproducible.service.bundles","name":"bento artifact packaging with reproducible service bundles","description":"Packages a service definition, models, dependencies, and configuration into a self-contained Bento artifact (standardized container format). The build process (src/_bentoml_impl/loader.py, Bento Packaging) creates a directory structure with bentofile.yaml, Python dependencies (requirements.txt or pyproject.toml), model references, and service code. Bentos are versioned and can be containerized into Docker images or deployed directly to BentoCloud, ensuring reproducibility across environments.","intents":["Bundle service code, models, and dependencies into a single deployable artifact","Ensure reproducibility by pinning dependency versions and model versions","Deploy the same Bento to local, Docker, and cloud environments without modification","Version services and enable rollback to previous Bento versions"],"best_for":["Teams needing reproducible ML service deployments","Organizations with strict dependency management and audit requirements","Services requiring consistent behavior across dev, staging, and production"],"limitations":["Bento artifacts can be large (100MB+) if models are included; requires efficient storage and transfer","Dependency resolution is Python-only; non-Python dependencies must be handled via Docker base image customization","Bento versioning is local to the build environment; no built-in registry for sharing across teams","Rebuilding Bentos for dependency updates requires re-running the full build pipeline"],"requires":["BentoML 1.0+","bentofile.yaml in service directory","Python 3.8+ and pip/poetry for dependency management","Docker (optional, for containerization)"],"input_types":["service code","bentofile.yaml","model references","dependency specifications"],"output_types":["Bento artifact directory","Docker image","deployment configuration"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"bentoml__cap_5","uri":"capability://automation.workflow.multi.process.worker.pool.with.concurrency.and.resource.management","name":"multi-process worker pool with concurrency and resource management","description":"Manages a pool of worker processes (src/_bentoml_impl/worker/runner.py, src/_bentoml_impl/worker/service.py) that execute service methods in parallel. Each worker runs a copy of the service instance, with concurrency controlled via configuration (max_concurrency_per_worker). The framework handles process lifecycle, inter-process communication, and load balancing across workers. Resource limits (CPU, memory) can be configured per worker, enabling fine-grained control over resource utilization.","intents":["Execute multiple inference requests in parallel using multiple worker processes","Isolate model instances across processes to prevent memory leaks and state corruption","Configure concurrency limits to prevent resource exhaustion","Scale horizontally by adjusting worker count based on load"],"best_for":["High-concurrency inference services on multi-core machines","Services with models that are not thread-safe","Teams needing fine-grained control over resource allocation per worker"],"limitations":["Inter-process communication adds latency (~1-5ms per request) compared to in-process execution","Each worker process requires separate model instance in memory; not suitable for very large models on memory-constrained hardware","Process spawning overhead makes worker pools inefficient for very short-lived requests (<10ms)","No built-in auto-scaling based on queue depth; requires external orchestration (Kubernetes, etc.)"],"requires":["BentoML 1.0+","Multi-core CPU for parallel execution","Service configuration specifying num_workers and max_concurrency_per_worker"],"input_types":["service configuration","incoming requests"],"output_types":["distributed request execution","response aggregation"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"bentoml__cap_6","uri":"capability://planning.reasoning.service.composition.and.dependency.injection.with.shared.model.instances","name":"service composition and dependency injection with shared model instances","description":"Enables composing multiple services into a single deployment with shared model instances and dependencies (Service Dependencies in DeepWiki). Services can depend on other services or models, with dependency resolution handled at initialization time. The framework uses a factory pattern to instantiate dependencies once and inject them into service instances, reducing memory overhead and enabling model sharing across multiple endpoints.","intents":["Build complex inference pipelines by composing multiple models in a single service","Share expensive model instances across multiple endpoints","Define reusable service components that can be composed into larger services","Manage dependencies declaratively without manual initialization code"],"best_for":["Multi-stage inference pipelines (e.g., embedding + classification)","Services with shared preprocessing or feature extraction models","Teams building modular, reusable service components"],"limitations":["Dependency cycles are not detected; circular dependencies will cause initialization failures","Shared model instances are not thread-safe by default; requires careful synchronization if models have mutable state","Dependency injection adds initialization overhead; not suitable for services with hundreds of dependencies","No built-in dependency versioning; all services must use compatible versions of shared models"],"requires":["BentoML 1.0+","Service definitions with explicit dependency declarations","Understanding of Python dependency injection patterns"],"input_types":["service definitions","model references","dependency declarations"],"output_types":["composed service instances","shared model instances"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"bentoml__cap_7","uri":"capability://automation.workflow.containerization.with.automatic.dockerfile.generation.and.image.optimization","name":"containerization with automatic dockerfile generation and image optimization","description":"Generates optimized Dockerfiles from Bento artifacts with automatic dependency installation, model inclusion, and runtime configuration (Containerization in DeepWiki). The build process creates a multi-stage Dockerfile that minimizes image size by separating build dependencies from runtime dependencies. Images include the BentoML runtime, service code, models, and all Python dependencies, with support for custom base images and additional system dependencies.","intents":["Generate production-ready Docker images from Bento artifacts without manual Dockerfile writing","Optimize image size by separating build and runtime dependencies","Include models and dependencies in the image for reproducible deployments","Support custom base images and system-level dependencies"],"best_for":["Teams deploying to Kubernetes or Docker-based infrastructure","Organizations requiring reproducible container images","Services with complex dependency graphs or system-level requirements"],"limitations":["Generated Dockerfiles may not be optimal for all use cases; custom optimization may be needed","Large models included in images increase image size and push/pull time; consider external model storage","Multi-stage builds add complexity; debugging image build failures requires Docker knowledge","No built-in image registry integration; requires external tools for image management and versioning"],"requires":["BentoML 1.0+","Docker installed and running","Bento artifact with all dependencies specified"],"input_types":["Bento artifact","optional custom base image","optional system dependencies"],"output_types":["Dockerfile","Docker image"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"bentoml__cap_8","uri":"capability://automation.workflow.bentocloud.managed.deployment.with.auto.scaling.and.monitoring","name":"bentocloud managed deployment with auto-scaling and monitoring","description":"Provides a managed deployment platform (BentoCloud Deployment in DeepWiki) where Bentos can be deployed with automatic scaling, health monitoring, and traffic management. The platform handles infrastructure provisioning, load balancing, and observability without requiring manual Kubernetes configuration. Deployments are managed via CLI commands (bentoml deploy) with configuration for resource allocation, scaling policies, and environment variables.","intents":["Deploy Bentos to production without managing Kubernetes or infrastructure","Enable automatic scaling based on request volume","Monitor service health and performance with built-in observability","Manage multiple service versions and enable canary deployments"],"best_for":["Teams without Kubernetes expertise wanting managed ML serving","Startups and small teams needing quick production deployments","Organizations wanting vendor-managed infrastructure and compliance"],"limitations":["Vendor lock-in to BentoCloud; migrating to other platforms requires re-deployment","Pricing based on compute resources; can be expensive for high-traffic services","Limited customization compared to self-managed Kubernetes deployments","Requires BentoML account and API credentials; not suitable for air-gapped environments"],"requires":["BentoML 1.0+","BentoCloud account with API credentials","Bento artifact built and ready for deployment","Internet connectivity for deployment and monitoring"],"input_types":["Bento artifact","deployment configuration","environment variables"],"output_types":["deployed service endpoint","monitoring dashboard","scaling metrics"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"bentoml__cap_9","uri":"capability://code.generation.editing.openapi.schema.generation.and.interactive.api.documentation","name":"openapi schema generation and interactive api documentation","description":"Automatically generates OpenAPI 3.0 schemas from service definitions (src/_bentoml_sdk/service/openapi.py) with introspection of method signatures, type hints, and decorators. The HTTP server exposes Swagger UI and ReDoc endpoints for interactive API documentation, enabling clients to discover endpoints, request/response schemas, and test endpoints directly from the browser. Schema generation handles complex types, nested objects, and custom serializers.","intents":["Generate API documentation automatically without manual OpenAPI writing","Enable interactive API testing via Swagger UI","Provide clients with machine-readable API specifications","Support API discovery and code generation from OpenAPI schemas"],"best_for":["Teams building public or internal APIs requiring documentation","Services with complex request/response types needing schema clarity","Organizations using API-first development practices"],"limitations":["Schema generation relies on type hints; untyped or dynamically-typed code produces incomplete schemas","Complex custom types may not serialize correctly to OpenAPI; requires manual schema customization","Swagger UI and ReDoc add ~1-2MB to the service image size","No built-in support for API versioning or deprecation warnings in generated schemas"],"requires":["BentoML 1.0+","Type hints on service methods (required for accurate schema generation)","HTTP server enabled (gRPC-only services don't generate OpenAPI)"],"input_types":["service method signatures","type hints","docstrings"],"output_types":["OpenAPI 3.0 schema","Swagger UI","ReDoc documentation"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"bentoml__headline","uri":"capability://deployment.infra.ml.model.serving.framework","name":"ml model serving framework","description":"BentoML is a framework designed for serving machine learning models in production, allowing users to package models into standardized containers called Bentos, enabling easy deployment and management.","intents":["best ML model serving framework","ML model serving for production","how to deploy ML models with BentoML","BentoML vs other serving frameworks","BentoML features for model management"],"best_for":["developers deploying ML models","teams needing scalable model serving"],"limitations":["requires familiarity with Python","may need cloud resources for optimal performance"],"requires":["Python environment","ML models"],"input_types":["ML models"],"output_types":["API endpoints"],"categories":["deployment-infra"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","BentoML package installed","Understanding of Python decorators and class-based design","BentoML 1.0+","Model that supports batched inference","Configuration of batch_size and timeout_ms in service config","Framework-specific libraries installed (torch, tensorflow, sklearn, xgboost, onnx, etc.)","Models saved in framework-native format or ONNX","Python 3.8+ with development tools","IDE or debugger with Python support (optional but recommended)"],"failure_modes":["Python-only; no native support for services written in other languages","Decorator-based approach requires understanding BentoML conventions; steeper learning curve than plain FastAPI","Service state must be serializable for multi-process worker distribution","Batching adds latency for individual requests waiting in the queue (typically 10-100ms depending on timeout config)","Requires model to support variable batch sizes; some models have fixed batch size requirements","Batching effectiveness depends on request arrival rate; low-traffic services may not benefit","No built-in adaptive batching based on observed latency/throughput tradeoffs","Framework-specific optimizations may be lost in the abstraction; some frameworks have better performance with native serving","Custom model objects (e.g., PyTorch custom layers) must be serializable; some frameworks have limitations","ONNX conversion may lose model features or require manual optimization","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.690Z","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=bentoml","compare_url":"https://unfragile.ai/compare?artifact=bentoml"}},"signature":"/nwFwanm9EK/LO8pE+I9BHSecniEaWOk8K0lt6MmTX/bIjnbbU2FqrYR6IjF+Mg2DnqtAEkTWDkKYtMp1a8yCg==","signedAt":"2026-06-21T19:43:07.613Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/bentoml","artifact":"https://unfragile.ai/bentoml","verify":"https://unfragile.ai/api/v1/verify?slug=bentoml","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"}}