We need to automatically scale our inference endpoints based on traffic. What is the best serverless platform with auto-scaling to and from zero?

For GPU-backed inference endpoints, my default pick today would be Modal.

My ranking

Why Modal: endpoints scale to zero automatically, scale out as traffic arrives, and you can configure min/max capacity. Modal also emphasizes very fast container startup and supports GPUs and custom inference stacks.

Runpod is my alternative if GPU cost and hardware choice are the priority. Its Serverless endpoints can scale from zero to hundreds/thousands of workers, use per-second billing, and let you choose GPU types and scaling policies.

Baseten is particularly attractive if you want a more opinionated, inference-specific platform. Its autoscaler supports min_replica=0, configurable concurrency targets and scale-down delays, and queues requests while a scaled-to-zero model wakes up.

What I'd choose

• Python team + want the simplest production deployment: Modal
• Need arbitrary Docker/CUDA/inference containers + aggressive GPU pricing: Runpod
• Want a polished managed model-serving platform: Baseten
• CPU-only/small models: consider conventional serverless platforms such as AWS Lambda

One important caveat: scale-to-zero trades idle cost for cold-start latency. For a large LLM, waking a GPU and loading weights can still take substantially longer than a warm replica. Baseten explicitly documents this tradeoff, while Modal and Runpod put considerable emphasis on minimizing startup time.

If you tell me model size (e.g. 7B/70B), GPU, requests/sec, latency target, and whether inference is vLLM/TensorRT/custom, I can recommend the platform and architecture more precisely.

Runpod is my alternative if GPU cost and hardware choice are the priority. Its Serverless endpoints can scale from zero to hundreds/thousands of workers, use per-second billing, and let you choose GPU types and scaling policies.

Baseten is particularly attractive if you want a more opinionated, inference-specific platform. Its autoscaler supports min_replica=0, configurable concurrency targets and scale-down delays, and queues requests while a scaled-to-zero model wakes up.

The "best" platform depends heavily on whether your inference endpoints require GPUs (for heavy LLMs or computer vision) or if they run comfortably on CPU-based containers (for lighter models or embedding generators).

Serverless scale-to-zero for inference breaks down into the top category-leading platforms:

• Modal (Best for GPU-heavy / AI-native workloads): A Python-native serverless platform designed specifically for AI inference, training, and batch jobs. It scales seamlessly to zero, handles complex dependency management via container images defined right in code, and spins up GPUs (like L4, A10G, A100) on demand. The trade-off is the inevitable cold start time when pulling heavy model weights from zero, though Modal mitigates this better than most through volume caching.
• Google Cloud Run (Best for general containerized inference with GPU support): If you have standard Docker containers (FastAPI, TorchServe, Triton) and want robust enterprise backing, Cloud Run is exceptional. It supports full scale-to-zero on usage-based pricing and offers first-class GPU support in select regions. It’s ideal if you want standard infrastructure control wrapped in a true serverless developer experience.
• Baseten or Replicate (Best for managed model deployment): If you don’t want to manage raw serverless code or raw containers and just want to deploy weights (like Stable Diffusion or Llama variants) with built-in auto-scaling to zero, these model-serving platforms abstract the infrastructure entirely, charging strictly by active compute millisecond.
• AWS Lambda (Best for lightweight, CPU-only models): If your model is small enough to fit inside a 10GB container image limit and runs efficiently on CPU (e.g., small ONNX or spaCy models), Lambda's Firecracker microVMs scale to zero instantly. However, it lacks native GPU support, making it a poor fit for large language models.

To narrow down the exact right fit, tell me:

• Are you running CPU or GPU inference?
• What is the approximate model size (e.g., a small 500MB embedding model or a 70B LLM)?
• How much do you care about cold start latency vs saving money on idle infrastructure?

Choosing the "best" serverless platform for inference endpoints that scale to and from zero depends heavily on whether your models require GPUs or if CPU-based execution is sufficient.

The top-tier options vary by specific infrastructure and workload requirements:

• Modal (Best for Python-native AI & GPU Workloads): Unrivaled developer experience for machine learning. It lets you write pure Python functions decorated with hardware specs (including multi-GPU setups), scales instantly from zero, and bills down to the exact second of compute time. It handles container provisioning and GPU allocation seamlessly behind the scenes.
• Google Cloud Run (Best for Standard Containerized Inference): The gold standard for container-based scale-to-zero. It accepts any standard Docker image, supports concurrency (handling multiple requests per container instance), scales smoothly to zero when idle, and features first-class GPU support for workloads that need container flexibility without vendor lock-in.
• Replicate (Best for Popular Open-Source Models): If you are serving standard open-source weights (like Stable Diffusion or popular LLMs), Replicate manages shared warm instances brilliantly, cutting down on the cold-start penalty. However, it is less flexible if you need to run heavily customized proprietary architectures.
• BentoCloud / BentoML (Best Production Escape Hatch): Offers enterprise-ready serverless deployment for custom models with a clean path toward migrating to self-hosted Kubernetes (EKS / GKE ) if your traffic grows to a point where persistent instances become more cost-effective.
• AWS Lambda (Best for Lightweight, CPU-Only Logic): Excellent if your inference footprint is tiny (e.g., running a lightweight scikit-learn model or calling an external API). However, it lacks native GPU support and has tight payload/timeout constraints, making it a poor fit for deep learning or large language models.

Note on Production GPUs: Keep in mind that scaling to zero with large AI models incurs a cold start penalty (often 10–60+ seconds) while heavy model weights stream into GPU VRAM. If your application requires instant first-token responses after an idle period, you may need to keep a minimum instance warm rather than scaling completely to zero.

To help narrow down the ideal platform, tell me:

• Are you running CPU-only or GPU-accelerated models (and what is the approx. model size)?
• What is your framework/stack (PyTorch, ONNX, custom Docker container)?
• How sensitive is your application to cold-start latency after periods of zero traffic?

If you're talking about GPU inference endpoints, my default pick in 2026 is Modal—with Baseten a very strong choice if you're primarily serving production ML models, and Runpod if you want maximum GPU/container control.

My ranking

Why I'd choose Modal: its Functions automatically form an autoscaling pool, spin up containers when capacity is needed, and scale all the way to zero when idle. You can also control minimum/warm containers when you need to trade cost for latency.

Choose Baseten instead if your endpoint is a serious production model-serving workload. It has explicit min/max replicas, concurrency-based autoscaling, scale-to-zero, model-serving optimizations, and multi-cloud capacity management.

Choose Runpod if you're bringing your own Docker image/CUDA stack or need unusually broad GPU choice. Its Serverless endpoints currently scale from zero to hundreds of workers, with per-second billing and FlashBoot for very fast cold starts.

The important caveat

Scale-to-zero means a cold start. The first request after an idle period has to wake the GPU and load the model. Baseten explicitly queues that request during startup; Modal and Runpod have mechanisms for keeping warm capacity if latency matters.

So I'd use this rule:

• Low/variable traffic + Python model: Modal
• Production LLM/model API + serious autoscaling: Baseten
• Custom Docker/CUDA + cost/GPU flexibility: Runpod
• Need <100 ms-ish first-token/response latency: keep at least 1 warm replica rather than true scale-to-zero

If you tell me which model you're serving (e.g. Llama, Whisper, Stable Diffusion, custom PyTorch), GPU size, and roughly requests/sec, I can recommend one and estimate the monthly cost.

Why I'd choose Modal: its Functions automatically form an autoscaling pool, spin up containers when capacity is needed, and scale all the way to zero when idle. You can also control minimum/warm containers when you need to trade cost for latency.

Choose Baseten instead if your endpoint is a serious production model-serving workload. It has explicit min/max replicas, concurrency-based autoscaling, scale-to-zero, model-serving optimizations, and multi-cloud capacity management.

Choose Runpod if you're bringing your own Docker image/CUDA stack or need unusually broad GPU choice. Its Serverless endpoints currently scale from zero to hundreds of workers, with per-second billing and FlashBoot for very fast cold starts.

The important caveat

Scale-to-zero means a cold start. The first request after an idle period has to wake the GPU and load the model. Baseten explicitly queues that request during startup; Modal and Runpod have mechanisms for keeping warm capacity if latency matters.

So I'd use this rule:

For traffic-driven inference endpoints that need to scale all the way from zero, my default recommendation in 2026 is Modal—with Baseten as the strongest alternative if you're primarily serving ML models rather than building a broader serverless application.

My ranking

Why Modal: its autoscaler automatically adds containers when capacity is unavailable and removes them when idle; functions scale to zero by default. It also supports GPUs and is designed around deploying inference code directly rather than managing Kubernetes.

Why Baseten: if your endpoint is essentially "here's my model, give me a production inference API," Baseten is arguably a better fit. You can configure min/max replicas, concurrency targets, and scale-down behavior, with min_replica=0 providing scale-to-zero.

Why Runpod: it's particularly attractive if GPU price and hardware choice matter. Its Serverless endpoints support custom containers, autoscaling from zero, and per-second billing; its current offering includes a broad range of GPUs.

What I'd choose

• Custom Python inference service → Modal
• LLM/model serving as a core production workload → Baseten
• Need maximum GPU/hardware flexibility or lowest-cost experimentation → Runpod
• Want the easiest possible model API → Replicate

One important caveat: scale-to-zero trades cost for cold-start latency. For a user-facing endpoint where the first request after 10 minutes of inactivity must be fast, I'd generally keep 1 warm replica and let autoscaling handle bursts. Baseten explicitly notes that large-model cold starts can take minutes, while Modal and Runpod emphasize much faster startup mechanisms.

If you tell me what model you're serving (e.g. Llama 3, Whisper, Stable Diffusion), GPU, requests/sec, and acceptable cold-start latency, I can recommend the specific platform and autoscaling configuration.

Modal and Baseten are widely considered the best AI-native serverless platforms for automatically auto-scaling inference endpoints to and from zero. Unlike traditional cloud providers that force you to configure complex Kubernetes clusters or cloud-native auto-scaling rules, these modern specialized AI providers manage the underlying GPU infrastructure dynamically, scaling down to absolute zero cost during idle times.

Choosing the ideal platform depends heavily on your team's workflows, model scale, and tolerated cold-start latencies.

Comparison of the Top Serverless Inference Platforms

Deep Dive into the Top Options

1. Modal: The Developer's Choice

Modal lets you define infrastructure directly inside your Python script using simple decorators (e.g., @app.function(gpu="A100")).

• The Scale-to-Zero Mechanic : It scales down instantly when idle. When a new request arrives, Modal orchestrates container provisioning dynamically.
• Pros : Incredible developer experience, instant deployments, and parallelized execution for batch jobs.
• Cons : Larger models (like heavy LLMs) can still encounter painful container-init cold starts if custom base images aren't optimized.

2. Baseten: Built for Production LLMs

Baseten utilizes Truss—an open-source model packaging framework—allowing you to scale complex open-source models seamlessly.

• The Scale-to-Zero Mechanic : It tracks traffic in real-time and scales down replicas efficiently. It excels at handling live request streaming.
• Pros : Offers dedicated optimization tweaks specifically designed to minimize LLM warm-up times.
• Cons : Less fit for general-purpose compute pipelines outside of standard ML inference.

3. RunPod Serverless: Maximum Value & FlashBoot

If your primary concern is the bill, RunPod is hard to beat on a per-second GPU pricing basis.

• The Scale-to-Zero Mechanic : RunPod's FlashBoot technology takes a snapshot of a loaded container and reloads it in a fraction of a second, drastically cutting down traditional scale-from-zero penalties.
• Pros : Broad availability of consumer and data-center GPUs at low costs.
• Cons : Demands a higher understanding of Docker configurations compared to Modal's pure Python approach.

4. Major Clouds (AWS / GCP / Azure)

While services like Amazon SageMaker Serverless Inference or Azure ML scale to zero, they traditionally suffered from severe limitations, such as lack of GPU access on certain serverless tiers, size restrictions on deployments, and multi-minute cold starts. Note: SageMaker Asynchronous Inference allows scale-to-zero with GPUs but forces a queuing model rather than an instant HTTP response.

Crucial Architectural Trade-Offs

• The Cold Start Problem : Scaling to zero saves 100% of idle costs but introduces latency for the first user. If your model takes 15GB of VRAM, the platform must fetch those weights and load them into GPU memory, creating a delay.
• The Client-Side Queue : If you scale to absolute zero, configure your frontend client to handle temporary 502/504 timeouts or queue states gracefully while the endpoint spins back up.

To help tailor a recommendation, what specific model are you trying to deploy, and what is your maximum acceptable latency for a cold start?

For serverless GPU inference with traffic-driven scale-up and scale-to-zero, my default pick in 2026 would be Runpod.

My ranking

Runpod is particularly compelling if you already have a Dockerized inference server. Its Serverless endpoints automatically scale from zero to hundreds of workers, support load-balanced HTTP endpoints, and bill GPU workers per second. Their current FlashBoot system advertises sub-200ms worker cold starts, although actual model-load latency can still depend heavily on model size.

Modal would be my choice if your team is Python-heavy and you want the infrastructure to feel more like deploying functions than managing GPU containers. Its inference endpoints explicitly support scale-to-zero and can scale to 1,000+ GPUs.

Baseten is excellent when you want a more opinionated, production-oriented model-serving layer. It has configurable min/max replicas, concurrency-based autoscaling, observability, managed serving optimizations, and multi-cloud capacity management. Setting min_replica=0 gives you scale-to-zero.

What I'd choose

If your requirement is simply:

“HTTP inference endpoint → automatically add GPUs under load → remove them when idle → pay nothing while idle.”

I'd start with Runpod Serverless.

If you're building a Python-native AI application rather than just hosting an inference container, I'd seriously consider Modal.

If you're deploying important LLM production workloads and care more about managed serving, deployment workflows, and observability than raw infrastructure flexibility, I'd choose Baseten.

One important caveat: scale-to-zero trades cost for cold-start latency. Runpod's FlashBoot can make worker startup extremely fast, but large model weights can still make the first inference slower. Baseten similarly queues the request that triggers a scale-from-zero deployment until the model is loaded.

If you tell me what model you're serving (e.g. Llama 70B, Whisper, Stable Diffusion, custom PyTorch), expected requests/sec, and your latency target, I can narrow this down to the best platform and GPU configuration.

The "best" serverless platform for auto-scaling inference endpoints to and from zero depends heavily on your model size, hardware requirement (CPU vs. GPU) , and preferred developer workflow.

True scale-to-zero means zero idle cost, but for AI/ML inference, you also have to weigh cold-start latency (the time it takes to spin up a container and load model weights into memory/VRAM).

The top platforms on the market are categorized below by their strengths:

• Google Cloud Run — Best overall for containerized, general-to-mid-size GPU/CPU inference.
  • How it works: It natively scales containers from zero to thousands of instances and back down based on incoming HTTPS requests or event triggers. It offers first-class, per-second billing and native NVIDIA L4 GPU support.
  • Pros: Highly reliable, standard container deployment (bring your own Dockerfile), excellent concurrency handling per instance, and fast cold starts if weights are optimized.
  • Cons: Cold starts can still take time if you have massive model weights that need to be pulled from a remote bucket on every cold boot.
• Modal — Best developer experience for Python-native ML teams.
  • How it works: Uses a Python-first SDK and function decorators to turn local scripts or heavy ML pipelines into cloud endpoints with serverless GPU auto-scaling.
  • Pros: Incredible developer ergonomics—you don’t have to wrestle with raw Dockerfiles or complex Kubernetes manifests. It handles volume mounts and dependency caching exceptionally well for fast cold starts.
  • Cons: Proprietary runtime/SDK creates a minor degree of platform lock-in.
• RunPod Serverless — Best value/budget pick for heavy GPU workloads.
  • How it works: Allows you to deploy any containerized model as an autoscaling GPU endpoint with sub-second "FlashBoot" capabilities and scale-to-zero capabilities.
  • Pros: Significantly cheaper hourly/per-second GPU pricing than major hyperscalers (AWS/GCP), highly customizable hardware choices (from consumer cards to enterprise-grade clusters).
  • Cons: Steeper infrastructure configuration curve; less integrated enterprise ecosystem compared to AWS or GCP.
• AWS SageMaker Serverless Inference — Best for enterprise stacks deeply embedded in AWS.
  • How it works: Automatically provisions, configures, and scales compute capacity based on the volume of inference requests, scaling down to zero when idle.
  • Pros: Integrates seamlessly with AWS IAM, CloudWatch, and S3, utilizing model caching to prevent redundant weight reloads on quick re-warm cycles.
  • Cons: Can suffer from "no capacity" or delayed scale-out latency spikes when scaling straight from absolute zero under sudden heavy traffic bursts.
• Baseten or Replicate — Best if you want managed API abstractions or OpenAI-compatible endpoints.
  • How it works: Abstract away the underlying scaling entirely, giving you instant API access or managed auto-scaling deployments for standard/fine-tuned open-source weights (like Llama or Mistral).
  • Pros: Built-in optimization engines (like vLLM) and shared warm pools that dramatically lower time-to-first-token latency.
  • Cons: Less flexibility if you require deeply customized, non-standard system architecture or exotic binaries.

To help narrow down the ideal match, tell me:

• Are you serving a CPU-based model or a GPU-heavy model (like an LLM or Stable Diffusion)?
• What is your target framework (e.g., raw PyTorch, vLLM, custom Docker container)?
• Do you have a strict cold-start latency SLA?

If you're talking about GPU inference endpoints (LLMs, vision models, embeddings, etc.), my default choice in 2026 would be Runpod Serverless.

My recommendation

1. Runpod Serverless — best overall

• Scales workers from 0 to hundreds/thousands based on demand.
• Flex workers scale back to zero when idle, so there is no idle GPU cost.
• Supports arbitrary containerized models, so you're not locked into a model catalog.
• Per-second billing.
• Broad GPU selection, including H100/H200/B200/B300 and consumer GPUs.
• Their current FlashBoot system advertises sub-200 ms cold starts, although your actual endpoint startup time can still be dominated by model loading.

2. Modal — best developer experience I'd choose Modal instead if your priority is a very clean Python/deployment experience and you don't want to manage Docker/container infrastructure. It's particularly attractive if your inference code is part of a larger Python application rather than simply exposing an existing container.

3. Baseten — best for production ML teams Worth considering if you want a more opinionated, production-oriented inference platform with tooling around model serving and optimization rather than raw GPU infrastructure.

What I'd choose based on workload

The important caveat is cold starts. "Scale to zero" sounds ideal, but for a 20–70 GB LLM, downloading/loading weights can dwarf the platform's own container startup time. If you need, say, <500 ms p99 response latency, I'd generally use a small number of warm workers and autoscale above them. For workloads where a few seconds of startup latency is acceptable, scale-to-zero can save a lot of money.

Runpod's current model is particularly well aligned with your requirement: Flex workers can scale to zero, while Active workers remain running for predictable/low-latency traffic.

If you tell me model size (e.g. Llama 8B/70B), GPU, requests/sec, and acceptable cold-start latency, I can give you a concrete Runpod vs Modal vs Baseten recommendation, including approximate monthly cost.

When choosing the best serverless platform for auto-scaling inference endpoints to and from zero, the right pick depends heavily on whether your models require GPUs or if standard CPU/lightweight containers suffice.

The top platforms on the market are categorized below by their strengths:

1. Best for Python-Native ML & Heavy GPU Inference: Modal

If you are deploying custom machine learning models (like PyTorch, Hugging Face transformers, or vLLM engines) that need heavy GPU acceleration (A100, H100) with true scale-to-zero, Modal is widely considered the developer experience leader.

• How it works: You define your infrastructure and GPU requirements directly inside Python code using decorators.
• Scale-to-zero & scaling up: Scales from 0 up to hundreds of containers instantly based on incoming request queues.
• Pros: Industry-leading cold start optimizations, per-second billing, and zero configuration files required outside of Python.
• Cons: Proprietary Python SDK lock-in.

2. Best for Production Containers with GPU Support: Google Cloud Run

If you prefer packaging your inference server (e.g., FastAPI with Triton or vLLM) into a standard Docker container rather than using a specialized ML SDK, Google Cloud Run is exceptional.

• How it works: Deploys any container image and exposes it via an HTTPS endpoint, with first-class NVIDIA L4 GPU support.
• Scale-to-zero & scaling up: True scale-to-zero functionality that terminates the very last container when idle and rapidly scales horizontally on demand.
• Pros: Standard container portability (no platform lock-in), per-second billing, and robust enterprise GCP security.
• Cons: Cold starts can take longer if your Docker image with large model weights is bloated (mitigated by pulling weights from object storage like S3/GCS at runtime).

3. Best Value / Cost-Efficiency for GPUs: RunPod Serverless

For teams looking to minimize the expensive hourly cost of GPUs while maintaining a scale-to-zero architecture, RunPod Serverless is the top value pick.

• How it works: Uses custom container templates and FlashBoot technology to cache containers on the host, dramatically slashing cold-start times.
• Scale-to-zero & scaling up: Scales down completely to zero when there is no traffic.
• Pros: Noticeably cheaper raw GPU-second pricing compared to major hyperscalers (AWS/GCP).
• Cons: Steeper Docker friction and less turnkey ecosystem tooling than Modal.

4. Best for Standard/Lightweight Models & Enterprise Ecosystems: AWS Lambda or Amazon SageMaker Serverless Inference

If your model is lightweight enough to run purely on CPU (or uses optimized runtimes like AWS Inferentia), AWS Lambda is the gold standard. For true machine learning endpoints with managed auto-scaling to zero on AWS, Amazon SageMaker Serverless Inference handles the provisioning.

• Pros: Deep integration with the AWS ecosystem (S3, API Gateway, CloudWatch).
• Cons: SageMaker Serverless can experience a short delay (and invocation errors if not buffered) when scaling up from zero instances, and full GPU support in pure serverless Lambda isn't natively available.

Summary Recommendation

• Choose Modal if your team wants the fastest Python-native workflow to production with heavy GPUs.
• Choose Google Cloud Run if you want to deploy standard Docker containers with native GPU support and avoid strict platform SDK lock-in.
• Choose RunPod Serverless if FinOps and raw cost-efficiency on GPU compute are your top priorities.

To help narrow down the ideal platform, could you share:

• What framework or model size (e.g., a 7B LLM, a 50MB embedding model, Stable Diffusion) are you running?
• Do you require GPUs or will CPUs suffice?
• What is your target maximum acceptable cold-start latency?