macrostack
Browse

The AI stack

Categories

Local & Sovereign AINotes & KnowledgeObservability & MonitoringPassword ManagersWeb AnalyticsTeam ChatSmart HomeNetworking & RoutersVideo ConferencingCloud Storage & SyncPhotos & MediaAPI DevelopmentImage EditingWorkflow Automation & iPaaSDeveloper Tools & ContainersOffice & Productivity SuitesNo-Code DatabasesCode Hosting & Git ForgesProject ManagementEmail Marketing & NewslettersScheduling & BookingError Tracking & Exception MonitoringLog Management & SIEMVPN & PrivacyEmail & Secure MailVector Databases & AI SearchLLM & Agent FrameworksDomains & Web HostingData Removal & PrivacyAuthentication & IdentityHelp Desk & Customer SupportCloud & VPSKubernetes & Container PlatformsEmbedding ModelsPDF & DocumentsAI Coding AssistantsAI Voice & SpeechLLM Observability & EvaluationLLM Gateways & RoutingCloud GPU & AI ComputeCI/CD & build automationData & pipeline orchestrationModel serving & inferenceAI agent frameworksBackend as a serviceSecrets managementFeature flags & experimentationProduct analyticsSearch infrastructureUptime & status monitoringAffiliate & partner platformsVisitor identification & personalisationWikis & internal docsIdentity & access managementData warehouses & analytics enginesCustomer data platformsCRMObject storageBI & dashboardsE-signatureWhiteboards & diagrammingIn-memory data stores & cachingPlatform as a serviceTransactional & bulk emailHeadless CMSDesign & prototypingE-commerce platformsInternal tools & admin panelsManaged databasesForms & surveysFine-Tuning & Model TrainingRAG & Retrieval PlatformsLLM Evaluation & TestingAI Guardrails & Content SafetySpeech Recognition & TranscriptionExperiment Tracking & ML OpsDocument AI & OCR

About

How we rank & score

Layer 3 · self-hosting reality check

What it actually takes to self-host BentoML

The docs say 2 GB. In practice you want 4 GB plus model memory. Here is the honest version — real requirements, real monthly cost, what you will be maintaining, and the one thing that catches people out.

Usually reached from Modal alternatives, where BentoML is one of the picks.

RAM — documented minimum2 GB
RAM — what it really needs4 GB plus model memory
CPU4 vCPU, or a GPU for anything sizeable
DiskModel artifacts plus container images
Monthly cost$40–200/mo depending on hardware, against per-second serverless inference billing
Setup timeHalf a day
How you install itPython framework — you write a service definition and it builds a container
Ongoing maintenanceModerate. You own the serving infrastructure it produces.
Where it stops scalingMulti-model production serving, which is its genuine strength — many models, many frameworks, one interface.

The thing that catches people out

It packages models into containers beautifully and does not orchestrate them. People assume BentoML is the whole deployment story, then find they still need Kubernetes, autoscaling and a load balancer underneath. It is the packaging and serving layer, not the platform — decide where the containers will actually run before you commit to it.

When not to self-host BentoML

You serve one language model and nothing else. vLLM does that better with far less scaffolding.

Every guide here carries this section. A site that only ever tells you to self-host is selling something — the useful answer is sometimes no.

Other Layer 3 self-hosting guides

Common questions

How much RAM does BentoML actually need?
4 GB plus model memory in practice. The documented minimum is 2 GB, which is the figure at which the process starts rather than the figure at which it works under real use. 4 vCPU, or a GPU for anything sizeable alongside it.
What does self-hosting BentoML cost per month?
$40–200/mo depending on hardware, against per-second serverless inference billing This is commodity VPS pricing and excludes your time, which is the larger cost for most people — budget for moderate. You own the serving infrastructure it produces.
How long does it take to set up BentoML?
Half a day, via Python framework — you write a service definition and it builds a container.
When should I NOT self-host BentoML?
You serve one language model and nothing else. vLLM does that better with far less scaffolding.
What is the most common mistake when self-hosting BentoML?
It packages models into containers beautifully and does not orchestrate them. People assume BentoML is the whole deployment story, then find they still need Kubernetes, autoscaling and a load balancer underneath. It is the packaging and serving layer, not the platform — decide where the containers will actually run before you commit to it.
The Macrostack brief

New swaps, worth your inbox.

A short, occasional email when we add a high-intent alternative or ship a new head-to-head. No spam, no selling your address — unsubscribe in one click.