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All BentoML Plans & Pricing

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Starter

  • Pay-as-you-go GPU compute (per second billing)
  • NVIDIA T4, L4, and A100 GPU access
  • Autoscaling including scale-to-zero
  • $10 in free signup credits
  • Open-source BentoML framework (self-host for free)
  • Web console with hourly rate estimates
  • Community support

Scale

  • Everything in Starter
  • Custom invoicing and payment terms
  • Consolidated billing
  • Dedicated Slack channel
  • Faster response times
  • Volume compute discounts

Enterprise

  • Everything in Scale
  • BYOC on AWS, GCP, or Azure
  • Full data residency control
  • Advanced security and compliance
  • 24/7 support with Zoom office hours
  • Priority SLAs
  • Dedicated engineering resources
  • Additional GPU types (H100, H200, etc.)
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Quick Answer
Last verified:
Estimate

BentoML costs Free to $5K per month as of July 2026, with 3 plans available including a free tier. Plan: Starter (free). Enterprise pricing is available on request. Pricing depends on your chosen tier, contract length, and negotiated discounts.

Use the interactive pricing calculator to estimate your exact cost based on team size and requirements.

  • Free tier: Yes

BentoML offers 3 pricing tiers: Starter, Scale, Enterprise. The Scale plan is growing teams with sustained inference workloads needing better support and billing flexibility.

Compared to other ai model hosting & inference software, BentoML is positioned at the premium price point.

  • 6 documented hidden costs beyond list price
Last verified: | Sources: 1 pricing pages and reviews

How much does BentoML cost?

BentoML offers 3 pricing plans, starting with a free tier and scaling to custom enterprise pricing. Plans include Starter (free), Scale (custom pricing), Enterprise (custom pricing).

BentoML Pricing Overview

BentoML has 3 pricing plans, including a free tier. Paid plans range from $0 to $5,000/month. The Starter plan is free and is best for individual developers and small teams building ai-powered apis. The Scale plan requires contacting sales for a custom quote and is designed for growing teams with sustained inference workloads needing better support and billing flexibility. The Enterprise plan requires contacting sales for a custom quote and is designed for enterprises requiring data sovereignty, compliance controls, or on-prem/byoc deployments.

There are at least 6 documented hidden costs beyond BentoML's list price, including implementation, training, and add-on fees.

This pricing was last verified in May 4, 2026 from 1 independent source.

Try BentoML Free

BentoML is an open-source model serving framework with a managed cloud platform called BentoCloud. It lets teams package any ML model as a deployable service and run it on their own infrastructure or on BentoCloud's managed GPU infrastructure. BentoCloud charges per second for active compute, scales to zero automatically, and supports BYOC (Bring Your Own Cloud) deployments for enterprises needing data sovereignty.

How BentoML Pricing Compares

Compare BentoML pricing against top alternatives in AI Model Hosting & Inference.

Baseten $0-$6500/month Compare → Cerebrium $0-$100/month Compare → Banana.dev Custom pricing Compare →

Compare BentoML vs Alternatives

Before committing to BentoML, compare pricing with these 3 alternatives in the same category.

VSBaseten
Free

Teams getting started with model serving or running variable workloads

Full comparison
VSCerebrium
Free

Individual developers and hobbyists experimenting with serverless ML inference

Full comparison
VSBanana.dev
Custom

Historical reference only — service is no longer available

Full comparison

All BentoML alternatives & migration guides

What Companies Actually Pay for BentoML

Review scores
Top pricing complaints
Lack of support for AWS SageMaker. One user noted that BentoML did not have adequate methods for dockerizing for AWS SageMaker, and a related library, bentoctl, was deprecated.Difficulty in deploying Yatai for a production build.

How BentoML Pricing Compares

Software Starting Price Top Price
BentoML Free $5000/month
Banana.dev Custom Custom
Baseten Custom Custom
Cerebrium Free $100/month
Banana.dev (rebranded) $1200/mo + at-cost compute $1200/mo + at-cost compute
Inference.net Free $250/forever

Detailed pricing comparisons:

Browse all AI Model Hosting & Inference pricing →

6 BentoML Hidden Costs Beyond the List Price

Beyond the listed price, BentoML has at least 6 documented hidden costs that can significantly increase total cost of ownership.

Watch for 6 hidden costs
  • Specialized Talent Costs 30-50%
    high 1 source
    industry "Specialized Talent Costs: Hiring and retaining infrastructure specialists with deep AI deployment expertise can be expensive, with salaries often 30-50% higher than standard DevOps roles."
  • Manual Setup Delays
    high 1 source
    industry "Delays from Manual Setup: Establishing production-ready infrastructure and pipelines can take months, delaying time-to-market for AI models."
  • Wasted Compute
    high 1 source
    industry "Wasted Compute from Idle or Over-provisioned GPUs: Without efficient elastic scaling, enterprises often keep GPUs running unnecessarily, leading to increased spending with little added value."
  • DIY InferenceOps Complexity
    high 1 source
    industry "Even a basic setup with fast autoscaling and distributed inference can take an experienced team two to three months to design, implement, and stabilize."
  • Lack of ROI Tracking
    medium 1 source
    industry "Lack of ROI Tracking Frameworks: Many enterprises struggle to connect AI deployments to measurable business impact, making it difficult to justify investments."
  • Vendor Lock-in
    high 1 source
    industry "Operational Rigidity and Vendor Lock-in: Using general-purpose platforms for specialized inference needs can introduce friction, slow iteration, and tie workloads to specific cloud runtimes, complicating cross-cloud or on-premise deployments."
Tip

Ask your BentoML sales rep about these costs upfront. Getting them in writing before signing can save you from surprise charges later.

Full hidden costs breakdown →

Intelligence sourced from 1 independent sources
industry
Key claims include inline source attribution. Data verified against multiple independent sources. 6 source citations total.

BentoML Pricing FAQ

01 How much does BentoML cost?

BentoML the open-source framework is free to use for self-hosting. BentoCloud (the managed cloud platform) charges pay-as-you-go GPU compute billed per second, with T4, L4, and A100 GPUs available on the Starter plan. New accounts receive $10 in free credits. Exact per-hour GPU rates are shown in the BentoCloud console when configuring a deployment.

02 Is BentoML free?

Yes — the open-source BentoML framework is free with no usage limits for self-hosting on your own infrastructure. BentoCloud (managed hosting) offers $10 in free signup credits, then charges per second for active GPU compute. There is no permanently free managed tier after credits are exhausted.

03 What GPUs does BentoCloud support?

BentoCloud's Starter plan includes NVIDIA T4, L4, and A100 GPUs. Enterprise customers have access to additional GPU types including H100 and H200. GPU hourly rates are dynamically shown in the BentoCloud console when configuring deployments.

04 What is BentoCloud BYOC?

BYOC (Bring Your Own Cloud) lets enterprises run BentoCloud's orchestration layer inside their own AWS, GCP, or Azure account. This means model data and inference traffic never leave the customer's cloud environment, satisfying data residency and compliance requirements. BYOC is available on the Enterprise plan.

05 How does BentoCloud pricing work?

BentoCloud's Starter plan is free with $10 in initial credits. Beyond that, Scale and Enterprise plans are custom-priced based on workload requirements. Contact BentoML directly for a quote.

06 Can I self-host BentoML for free?

Yes. The open-source BentoML framework can be self-hosted with no cost and no usage limits. BentoCloud (the managed platform) has a Starter plan with $10 in free credits, but paid tiers are required for sustained managed deployments.

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