Baseten Paid

• Serving a fine-tuned Llama for a support classifier.
• Hosting a Whisper endpoint for transcription at $0.0005-scale per audio minute.
• Running a Flux or SDXL image endpoint behind a product.
• Blue-green deploys of a new model version with instant rollback.
• Keeping inference in a specific US region for compliance.

Quick Summary

• Baseten deploys and serves models on dedicated GPUs.
• Billing is per second of GPU time, roughly $1.21/h on A10G and $4.40/h on H100.
• Supports scale-to-zero but production chat needs warm replicas.
• No free tier — trial credits only.
• Worth it for fine-tuned models with steady demand, not prototypes.

Use Cases

• Serving a fine-tuned Llama for a support classifier.
• Hosting a Whisper endpoint for transcription at $0.0005-scale per audio minute.
• Running a Flux or SDXL image endpoint behind a product.
• Blue-green deploys of a new model version with instant rollback.
• Keeping inference in a specific US region for compliance.

Quick Summary

• Baseten deploys and serves models on dedicated GPUs.
• Billing is per second of GPU time, roughly $1.21/h on A10G and $4.40/h on H100.
• Supports scale-to-zero but production chat needs warm replicas.
• No free tier — trial credits only.
• Worth it for fine-tuned models with steady demand, not prototypes.

Use Cases

• Serving a fine-tuned Llama for a support classifier.
• Hosting a Whisper endpoint for transcription at $0.0005-scale per audio minute.
• Running a Flux or SDXL image endpoint behind a product.
• Blue-green deploys of a new model version with instant rollback.
• Keeping inference in a specific US region for compliance.

Quick Summary

• Baseten deploys and serves models on dedicated GPUs.
• Billing is per second of GPU time, roughly $1.21/h on A10G and $4.40/h on H100.
• Supports scale-to-zero but production chat needs warm replicas.
• No free tier — trial credits only.
• Worth it for fine-tuned models with steady demand, not prototypes.

Good fit: companies with a fine-tuned model and steady traffic who need cost control and data residency they can name. Bad fit: prototypes with 50 requests a day — a serverless API is ten times cheaper at that scale.

Use Cases

• Serving a fine-tuned Llama for a support classifier.
• Hosting a Whisper endpoint for transcription at $0.0005-scale per audio minute.
• Running a Flux or SDXL image endpoint behind a product.
• Blue-green deploys of a new model version with instant rollback.
• Keeping inference in a specific US region for compliance.

Quick Summary

• Baseten deploys and serves models on dedicated GPUs.
• Billing is per second of GPU time, roughly $1.21/h on A10G and $4.40/h on H100.
• Supports scale-to-zero but production chat needs warm replicas.
• No free tier — trial credits only.
• Worth it for fine-tuned models with steady demand, not prototypes.

Who should use it (and who should not)

Good fit: companies with a fine-tuned model and steady traffic who need cost control and data residency they can name. Bad fit: prototypes with 50 requests a day — a serverless API is ten times cheaper at that scale.

Use Cases

• Serving a fine-tuned Llama for a support classifier.
• Hosting a Whisper endpoint for transcription at $0.0005-scale per audio minute.
• Running a Flux or SDXL image endpoint behind a product.
• Blue-green deploys of a new model version with instant rollback.
• Keeping inference in a specific US region for compliance.

Quick Summary

• Baseten deploys and serves models on dedicated GPUs.
• Billing is per second of GPU time, roughly $1.21/h on A10G and $4.40/h on H100.
• Supports scale-to-zero but production chat needs warm replicas.
• No free tier — trial credits only.
• Worth it for fine-tuned models with steady demand, not prototypes.

Costs: GPU hours are the price you pay. An A10G is roughly $1.21 per hour, an H100 around $4.40 per hour, and there is no separate platform fee for basic usage. A single always-on A10G is about $880 per month — cheaper than an engineer’s day rate, more expensive than calling a hosted API at low volume.

Not everything is smooth. There is no free tier, only trial credits; bandwidth and storage are extra; and you are locked into their packaging unless you keep a Dockerfile fallback. Teams without ML ops experience will spend a week on their first deploy.

Who should use it (and who should not)

Good fit: companies with a fine-tuned model and steady traffic who need cost control and data residency they can name. Bad fit: prototypes with 50 requests a day — a serverless API is ten times cheaper at that scale.

Use Cases

• Serving a fine-tuned Llama for a support classifier.
• Hosting a Whisper endpoint for transcription at $0.0005-scale per audio minute.
• Running a Flux or SDXL image endpoint behind a product.
• Blue-green deploys of a new model version with instant rollback.
• Keeping inference in a specific US region for compliance.

Quick Summary

• Baseten deploys and serves models on dedicated GPUs.
• Billing is per second of GPU time, roughly $1.21/h on A10G and $4.40/h on H100.
• Supports scale-to-zero but production chat needs warm replicas.
• No free tier — trial credits only.
• Worth it for fine-tuned models with steady demand, not prototypes.

Costs: GPU hours are the price you pay. An A10G is roughly $1.21 per hour, an H100 around $4.40 per hour, and there is no separate platform fee for basic usage. A single always-on A10G is about $880 per month — cheaper than an engineer’s day rate, more expensive than calling a hosted API at low volume.

Not everything is smooth. There is no free tier, only trial credits; bandwidth and storage are extra; and you are locked into their packaging unless you keep a Dockerfile fallback. Teams without ML ops experience will spend a week on their first deploy.

Who should use it (and who should not)

Good fit: companies with a fine-tuned model and steady traffic who need cost control and data residency they can name. Bad fit: prototypes with 50 requests a day — a serverless API is ten times cheaper at that scale.

Use Cases

• Serving a fine-tuned Llama for a support classifier.
• Hosting a Whisper endpoint for transcription at $0.0005-scale per audio minute.
• Running a Flux or SDXL image endpoint behind a product.
• Blue-green deploys of a new model version with instant rollback.
• Keeping inference in a specific US region for compliance.

Quick Summary

• Baseten deploys and serves models on dedicated GPUs.
• Billing is per second of GPU time, roughly $1.21/h on A10G and $4.40/h on H100.
• Supports scale-to-zero but production chat needs warm replicas.
• No free tier — trial credits only.
• Worth it for fine-tuned models with steady demand, not prototypes.

Costs: GPU hours are the price you pay. An A10G is roughly $1.21 per hour, an H100 around $4.40 per hour, and there is no separate platform fee for basic usage. A single always-on A10G is about $880 per month — cheaper than an engineer’s day rate, more expensive than calling a hosted API at low volume.

Not everything is smooth. There is no free tier, only trial credits; bandwidth and storage are extra; and you are locked into their packaging unless you keep a Dockerfile fallback. Teams without ML ops experience will spend a week on their first deploy.

Who should use it (and who should not)

Good fit: companies with a fine-tuned model and steady traffic who need cost control and data residency they can name. Bad fit: prototypes with 50 requests a day — a serverless API is ten times cheaper at that scale.

Use Cases

• Serving a fine-tuned Llama for a support classifier.
• Hosting a Whisper endpoint for transcription at $0.0005-scale per audio minute.
• Running a Flux or SDXL image endpoint behind a product.
• Blue-green deploys of a new model version with instant rollback.
• Keeping inference in a specific US region for compliance.

Quick Summary

• Baseten deploys and serves models on dedicated GPUs.
• Billing is per second of GPU time, roughly $1.21/h on A10G and $4.40/h on H100.
• Supports scale-to-zero but production chat needs warm replicas.
• No free tier — trial credits only.
• Worth it for fine-tuned models with steady demand, not prototypes.

Product Highlights

Cold starts are the thing people test first. Baseten keeps a cache of loaded models and typically resumes a small model in under 30 seconds. That is fine for internal tools and painful for consumer chat, so for production you keep at least one replica warm.

Costs: GPU hours are the price you pay. An A10G is roughly $1.21 per hour, an H100 around $4.40 per hour, and there is no separate platform fee for basic usage. A single always-on A10G is about $880 per month — cheaper than an engineer’s day rate, more expensive than calling a hosted API at low volume.

Not everything is smooth. There is no free tier, only trial credits; bandwidth and storage are extra; and you are locked into their packaging unless you keep a Dockerfile fallback. Teams without ML ops experience will spend a week on their first deploy.

Who should use it (and who should not)

Good fit: companies with a fine-tuned model and steady traffic who need cost control and data residency they can name. Bad fit: prototypes with 50 requests a day — a serverless API is ten times cheaper at that scale.

Use Cases

• Serving a fine-tuned Llama for a support classifier.
• Hosting a Whisper endpoint for transcription at $0.0005-scale per audio minute.
• Running a Flux or SDXL image endpoint behind a product.
• Blue-green deploys of a new model version with instant rollback.
• Keeping inference in a specific US region for compliance.

Quick Summary

• Baseten deploys and serves models on dedicated GPUs.
• Billing is per second of GPU time, roughly $1.21/h on A10G and $4.40/h on H100.
• Supports scale-to-zero but production chat needs warm replicas.
• No free tier — trial credits only.
• Worth it for fine-tuned models with steady demand, not prototypes.

Product Highlights

Cold starts are the thing people test first. Baseten keeps a cache of loaded models and typically resumes a small model in under 30 seconds. That is fine for internal tools and painful for consumer chat, so for production you keep at least one replica warm.

Costs: GPU hours are the price you pay. An A10G is roughly $1.21 per hour, an H100 around $4.40 per hour, and there is no separate platform fee for basic usage. A single always-on A10G is about $880 per month — cheaper than an engineer’s day rate, more expensive than calling a hosted API at low volume.

Not everything is smooth. There is no free tier, only trial credits; bandwidth and storage are extra; and you are locked into their packaging unless you keep a Dockerfile fallback. Teams without ML ops experience will spend a week on their first deploy.

Who should use it (and who should not)

Good fit: companies with a fine-tuned model and steady traffic who need cost control and data residency they can name. Bad fit: prototypes with 50 requests a day — a serverless API is ten times cheaper at that scale.

Use Cases

• Serving a fine-tuned Llama for a support classifier.
• Hosting a Whisper endpoint for transcription at $0.0005-scale per audio minute.
• Running a Flux or SDXL image endpoint behind a product.
• Blue-green deploys of a new model version with instant rollback.
• Keeping inference in a specific US region for compliance.

Quick Summary

• Baseten deploys and serves models on dedicated GPUs.
• Billing is per second of GPU time, roughly $1.21/h on A10G and $4.40/h on H100.
• Supports scale-to-zero but production chat needs warm replicas.
• No free tier — trial credits only.
• Worth it for fine-tuned models with steady demand, not prototypes.

Product Highlights

Cold starts are the thing people test first. Baseten keeps a cache of loaded models and typically resumes a small model in under 30 seconds. That is fine for internal tools and painful for consumer chat, so for production you keep at least one replica warm.

Costs: GPU hours are the price you pay. An A10G is roughly $1.21 per hour, an H100 around $4.40 per hour, and there is no separate platform fee for basic usage. A single always-on A10G is about $880 per month — cheaper than an engineer’s day rate, more expensive than calling a hosted API at low volume.

Not everything is smooth. There is no free tier, only trial credits; bandwidth and storage are extra; and you are locked into their packaging unless you keep a Dockerfile fallback. Teams without ML ops experience will spend a week on their first deploy.

Who should use it (and who should not)

Good fit: companies with a fine-tuned model and steady traffic who need cost control and data residency they can name. Bad fit: prototypes with 50 requests a day — a serverless API is ten times cheaper at that scale.

Use Cases

• Serving a fine-tuned Llama for a support classifier.
• Hosting a Whisper endpoint for transcription at $0.0005-scale per audio minute.
• Running a Flux or SDXL image endpoint behind a product.
• Blue-green deploys of a new model version with instant rollback.
• Keeping inference in a specific US region for compliance.

Quick Summary

• Baseten deploys and serves models on dedicated GPUs.
• Billing is per second of GPU time, roughly $1.21/h on A10G and $4.40/h on H100.
• Supports scale-to-zero but production chat needs warm replicas.
• No free tier — trial credits only.
• Worth it for fine-tuned models with steady demand, not prototypes.

• Truss packaging — declare the model and it resolves CUDA, torch and Python versions for you.
• Autoscaling — 0 to N replicas based on queue depth; scale-to-zero for dev endpoints.
• Model versions — each deploy gets an immutable version, so rollback is one click.
• Model APIs — ready endpoints for Llama, Whisper, Flux and embedding models if you do not want to package anything.
• GPU choice — T4, L4, A10G, A100 and H100 classes, billed per second of use.
• Python and REST clients — call endpoints from your app with a normal SDK.

Product Highlights

Cold starts are the thing people test first. Baseten keeps a cache of loaded models and typically resumes a small model in under 30 seconds. That is fine for internal tools and painful for consumer chat, so for production you keep at least one replica warm.

Costs: GPU hours are the price you pay. An A10G is roughly $1.21 per hour, an H100 around $4.40 per hour, and there is no separate platform fee for basic usage. A single always-on A10G is about $880 per month — cheaper than an engineer’s day rate, more expensive than calling a hosted API at low volume.

Not everything is smooth. There is no free tier, only trial credits; bandwidth and storage are extra; and you are locked into their packaging unless you keep a Dockerfile fallback. Teams without ML ops experience will spend a week on their first deploy.

Who should use it (and who should not)

Good fit: companies with a fine-tuned model and steady traffic who need cost control and data residency they can name. Bad fit: prototypes with 50 requests a day — a serverless API is ten times cheaper at that scale.

Use Cases

• Serving a fine-tuned Llama for a support classifier.
• Hosting a Whisper endpoint for transcription at $0.0005-scale per audio minute.
• Running a Flux or SDXL image endpoint behind a product.
• Blue-green deploys of a new model version with instant rollback.
• Keeping inference in a specific US region for compliance.

Quick Summary

• Baseten deploys and serves models on dedicated GPUs.
• Billing is per second of GPU time, roughly $1.21/h on A10G and $4.40/h on H100.
• Supports scale-to-zero but production chat needs warm replicas.
• No free tier — trial credits only.
• Worth it for fine-tuned models with steady demand, not prototypes.

Product Features

• Truss packaging — declare the model and it resolves CUDA, torch and Python versions for you.
• Autoscaling — 0 to N replicas based on queue depth; scale-to-zero for dev endpoints.
• Model versions — each deploy gets an immutable version, so rollback is one click.
• Model APIs — ready endpoints for Llama, Whisper, Flux and embedding models if you do not want to package anything.
• GPU choice — T4, L4, A10G, A100 and H100 classes, billed per second of use.
• Python and REST clients — call endpoints from your app with a normal SDK.

Product Highlights

Cold starts are the thing people test first. Baseten keeps a cache of loaded models and typically resumes a small model in under 30 seconds. That is fine for internal tools and painful for consumer chat, so for production you keep at least one replica warm.

Costs: GPU hours are the price you pay. An A10G is roughly $1.21 per hour, an H100 around $4.40 per hour, and there is no separate platform fee for basic usage. A single always-on A10G is about $880 per month — cheaper than an engineer’s day rate, more expensive than calling a hosted API at low volume.

Not everything is smooth. There is no free tier, only trial credits; bandwidth and storage are extra; and you are locked into their packaging unless you keep a Dockerfile fallback. Teams without ML ops experience will spend a week on their first deploy.

Who should use it (and who should not)

Good fit: companies with a fine-tuned model and steady traffic who need cost control and data residency they can name. Bad fit: prototypes with 50 requests a day — a serverless API is ten times cheaper at that scale.

Use Cases

• Serving a fine-tuned Llama for a support classifier.
• Hosting a Whisper endpoint for transcription at $0.0005-scale per audio minute.
• Running a Flux or SDXL image endpoint behind a product.
• Blue-green deploys of a new model version with instant rollback.
• Keeping inference in a specific US region for compliance.

Quick Summary

• Baseten deploys and serves models on dedicated GPUs.
• Billing is per second of GPU time, roughly $1.21/h on A10G and $4.40/h on H100.
• Supports scale-to-zero but production chat needs warm replicas.
• No free tier — trial credits only.
• Worth it for fine-tuned models with steady demand, not prototypes.

Baseten is an inference infrastructure company founded in 2019 (San Francisco). Its Truss tool takes a Python model — a Hugging Face checkpoint, a fine-tuned LoRA, a diffusion pipeline — and builds a container with the right dependencies. You get a versioned endpoint, plus logs and GPU metrics.

Product Features

• Truss packaging — declare the model and it resolves CUDA, torch and Python versions for you.
• Autoscaling — 0 to N replicas based on queue depth; scale-to-zero for dev endpoints.
• Model versions — each deploy gets an immutable version, so rollback is one click.
• Model APIs — ready endpoints for Llama, Whisper, Flux and embedding models if you do not want to package anything.
• GPU choice — T4, L4, A10G, A100 and H100 classes, billed per second of use.
• Python and REST clients — call endpoints from your app with a normal SDK.

Product Highlights

Cold starts are the thing people test first. Baseten keeps a cache of loaded models and typically resumes a small model in under 30 seconds. That is fine for internal tools and painful for consumer chat, so for production you keep at least one replica warm.

Costs: GPU hours are the price you pay. An A10G is roughly $1.21 per hour, an H100 around $4.40 per hour, and there is no separate platform fee for basic usage. A single always-on A10G is about $880 per month — cheaper than an engineer’s day rate, more expensive than calling a hosted API at low volume.

Not everything is smooth. There is no free tier, only trial credits; bandwidth and storage are extra; and you are locked into their packaging unless you keep a Dockerfile fallback. Teams without ML ops experience will spend a week on their first deploy.

Who should use it (and who should not)

Good fit: companies with a fine-tuned model and steady traffic who need cost control and data residency they can name. Bad fit: prototypes with 50 requests a day — a serverless API is ten times cheaper at that scale.

Use Cases

• Serving a fine-tuned Llama for a support classifier.
• Hosting a Whisper endpoint for transcription at $0.0005-scale per audio minute.
• Running a Flux or SDXL image endpoint behind a product.
• Blue-green deploys of a new model version with instant rollback.
• Keeping inference in a specific US region for compliance.

Quick Summary

• Baseten deploys and serves models on dedicated GPUs.
• Billing is per second of GPU time, roughly $1.21/h on A10G and $4.40/h on H100.
• Supports scale-to-zero but production chat needs warm replicas.
• No free tier — trial credits only.
• Worth it for fine-tuned models with steady demand, not prototypes.

Baseten is an inference infrastructure company founded in 2019 (San Francisco). Its Truss tool takes a Python model — a Hugging Face checkpoint, a fine-tuned LoRA, a diffusion pipeline — and builds a container with the right dependencies. You get a versioned endpoint, plus logs and GPU metrics.

Product Features

• Truss packaging — declare the model and it resolves CUDA, torch and Python versions for you.
• Autoscaling — 0 to N replicas based on queue depth; scale-to-zero for dev endpoints.
• Model versions — each deploy gets an immutable version, so rollback is one click.
• Model APIs — ready endpoints for Llama, Whisper, Flux and embedding models if you do not want to package anything.
• GPU choice — T4, L4, A10G, A100 and H100 classes, billed per second of use.
• Python and REST clients — call endpoints from your app with a normal SDK.

Product Highlights

Cold starts are the thing people test first. Baseten keeps a cache of loaded models and typically resumes a small model in under 30 seconds. That is fine for internal tools and painful for consumer chat, so for production you keep at least one replica warm.

Costs: GPU hours are the price you pay. An A10G is roughly $1.21 per hour, an H100 around $4.40 per hour, and there is no separate platform fee for basic usage. A single always-on A10G is about $880 per month — cheaper than an engineer’s day rate, more expensive than calling a hosted API at low volume.

Not everything is smooth. There is no free tier, only trial credits; bandwidth and storage are extra; and you are locked into their packaging unless you keep a Dockerfile fallback. Teams without ML ops experience will spend a week on their first deploy.

Who should use it (and who should not)

Good fit: companies with a fine-tuned model and steady traffic who need cost control and data residency they can name. Bad fit: prototypes with 50 requests a day — a serverless API is ten times cheaper at that scale.

Use Cases

• Serving a fine-tuned Llama for a support classifier.
• Hosting a Whisper endpoint for transcription at $0.0005-scale per audio minute.
• Running a Flux or SDXL image endpoint behind a product.
• Blue-green deploys of a new model version with instant rollback.
• Keeping inference in a specific US region for compliance.

Quick Summary

• Baseten deploys and serves models on dedicated GPUs.
• Billing is per second of GPU time, roughly $1.21/h on A10G and $4.40/h on H100.
• Supports scale-to-zero but production chat needs warm replicas.
• No free tier — trial credits only.
• Worth it for fine-tuned models with steady demand, not prototypes.

What is Baseten?

Baseten is an inference infrastructure company founded in 2019 (San Francisco). Its Truss tool takes a Python model — a Hugging Face checkpoint, a fine-tuned LoRA, a diffusion pipeline — and builds a container with the right dependencies. You get a versioned endpoint, plus logs and GPU metrics.

Product Features

• Truss packaging — declare the model and it resolves CUDA, torch and Python versions for you.
• Autoscaling — 0 to N replicas based on queue depth; scale-to-zero for dev endpoints.
• Model versions — each deploy gets an immutable version, so rollback is one click.
• Model APIs — ready endpoints for Llama, Whisper, Flux and embedding models if you do not want to package anything.
• GPU choice — T4, L4, A10G, A100 and H100 classes, billed per second of use.
• Python and REST clients — call endpoints from your app with a normal SDK.

Product Highlights

Cold starts are the thing people test first. Baseten keeps a cache of loaded models and typically resumes a small model in under 30 seconds. That is fine for internal tools and painful for consumer chat, so for production you keep at least one replica warm.

Costs: GPU hours are the price you pay. An A10G is roughly $1.21 per hour, an H100 around $4.40 per hour, and there is no separate platform fee for basic usage. A single always-on A10G is about $880 per month — cheaper than an engineer’s day rate, more expensive than calling a hosted API at low volume.

Not everything is smooth. There is no free tier, only trial credits; bandwidth and storage are extra; and you are locked into their packaging unless you keep a Dockerfile fallback. Teams without ML ops experience will spend a week on their first deploy.

Who should use it (and who should not)

Good fit: companies with a fine-tuned model and steady traffic who need cost control and data residency they can name. Bad fit: prototypes with 50 requests a day — a serverless API is ten times cheaper at that scale.

Use Cases

• Serving a fine-tuned Llama for a support classifier.
• Hosting a Whisper endpoint for transcription at $0.0005-scale per audio minute.
• Running a Flux or SDXL image endpoint behind a product.
• Blue-green deploys of a new model version with instant rollback.
• Keeping inference in a specific US region for compliance.

Quick Summary

• Baseten deploys and serves models on dedicated GPUs.
• Billing is per second of GPU time, roughly $1.21/h on A10G and $4.40/h on H100.
• Supports scale-to-zero but production chat needs warm replicas.
• No free tier — trial credits only.
• Worth it for fine-tuned models with steady demand, not prototypes.

Baseten is a model serving platform. You package a model, it runs on GPU hardware in the US with autoscaling and per-second billing, and you call it through an HTTP endpoint. H100 capacity runs around $4.40 per hour; scale-to-zero is supported.

What is Baseten?

Baseten is an inference infrastructure company founded in 2019 (San Francisco). Its Truss tool takes a Python model — a Hugging Face checkpoint, a fine-tuned LoRA, a diffusion pipeline — and builds a container with the right dependencies. You get a versioned endpoint, plus logs and GPU metrics.

Product Features

• Truss packaging — declare the model and it resolves CUDA, torch and Python versions for you.
• Autoscaling — 0 to N replicas based on queue depth; scale-to-zero for dev endpoints.
• Model versions — each deploy gets an immutable version, so rollback is one click.
• Model APIs — ready endpoints for Llama, Whisper, Flux and embedding models if you do not want to package anything.
• GPU choice — T4, L4, A10G, A100 and H100 classes, billed per second of use.
• Python and REST clients — call endpoints from your app with a normal SDK.

Product Highlights

Cold starts are the thing people test first. Baseten keeps a cache of loaded models and typically resumes a small model in under 30 seconds. That is fine for internal tools and painful for consumer chat, so for production you keep at least one replica warm.

Costs: GPU hours are the price you pay. An A10G is roughly $1.21 per hour, an H100 around $4.40 per hour, and there is no separate platform fee for basic usage. A single always-on A10G is about $880 per month — cheaper than an engineer’s day rate, more expensive than calling a hosted API at low volume.

Not everything is smooth. There is no free tier, only trial credits; bandwidth and storage are extra; and you are locked into their packaging unless you keep a Dockerfile fallback. Teams without ML ops experience will spend a week on their first deploy.

Who should use it (and who should not)

Good fit: companies with a fine-tuned model and steady traffic who need cost control and data residency they can name. Bad fit: prototypes with 50 requests a day — a serverless API is ten times cheaper at that scale.

Use Cases

• Serving a fine-tuned Llama for a support classifier.
• Hosting a Whisper endpoint for transcription at $0.0005-scale per audio minute.
• Running a Flux or SDXL image endpoint behind a product.
• Blue-green deploys of a new model version with instant rollback.
• Keeping inference in a specific US region for compliance.

Quick Summary

• Baseten deploys and serves models on dedicated GPUs.
• Billing is per second of GPU time, roughly $1.21/h on A10G and $4.40/h on H100.
• Supports scale-to-zero but production chat needs warm replicas.
• No free tier — trial credits only.
• Worth it for fine-tuned models with steady demand, not prototypes.