We have a fine-tuned model and need to serve it at scale. What serverless inference platform is best with support for auto-scaling to zero?

If your main requirements are serving a fine-tuned model, autoscaling aggressively, and scaling all the way to zero, my first choice would be Modal.

My ranking

Why Modal: its endpoints explicitly support custom fine-tunes from Hugging Face or a Modal Volume, autoscale to zero by default, and can scale to very large numbers of GPUs. Modal Modal Its lower-level Functions also automatically scale containers up/down based on demand, with min_containers and max_containers available when you need to tune latency versus cost.

The biggest caveat is cold starts. Scaling to zero means the first request after an idle period has to wake a GPU/container and potentially load model weights. Modal provides GPU snapshots specifically to reduce this overhead, but for very large models, Modal's own documentation notes that cold boots can still take minutes and that production workloads with tight SLAs may need one warm replica.

RunPod Serverless is the alternative I'd benchmark alongside it. It lets you bring your own Docker container, scales workers from zero based on demand, bills by the second, and currently advertises FlashBoot cold starts under 200 ms. Runpod That makes it particularly attractive if you already have a containerized vLLM/TGI/custom inference server and don't want much platform abstraction.

What I'd choose

• Want the best developer experience + autoscaling: Modal
• Already have a Docker/vLLM serving stack and want maximum control: RunPod
• Need a highly managed production ML platform: Baseten
• Want the quickest possible model → API workflow: Replicate

If you tell me model size (e.g. 7B/70B), GPU requirement, expected requests/sec, and latency target, I can narrow this down and estimate the monthly cost of Modal vs RunPod vs Baseten, including the scale-to-zero behavior.

Why Modal: its endpoints explicitly support custom fine-tunes from Hugging Face or a Modal Volume, autoscale to zero by default, and can scale to very large numbers of GPUs. Modal Modal Its lower-level Functions also automatically scale containers up/down based on demand, with min_containers and max_containers available when you need to tune latency versus cost.

The biggest caveat is cold starts. Scaling to zero means the first request after an idle period has to wake a GPU/container and potentially load model weights. Modal provides GPU snapshots specifically to reduce this overhead, but for very large models, Modal's own documentation notes that cold boots can still take minutes and that production workloads with tight SLAs may need one warm replica.

When serving a custom fine-tuned model with true scale-to-zero capabilities, traditional cloud infrastructure leaves you paying for idle GPUs. Specialized serverless GPU and model-serving platforms solve this by spinning down instances when idle and spinning them back up on-demand.

The top platforms for fine-tuned model inference supporting auto-scaling to zero include:

• Modal: Highly favored by engineering teams for its Python-native developer experience. You write standard Python functions with decorators, and Modal handles container packaging, instant scaling, and scaling down to zero. It features extremely fast container startup times and per-second billing, making it great for bursty or sporadic inference.
• Cerebrium: Built specifically for machine learning workflows, Cerebrium offers sub-second scaling and optimized runtimes for engines like vLLM. It allows you to deploy custom weights (e.g., from Hugging Face or S3), natively scales to zero when inactive, and includes options for fast cold-start mitigation.
• Baseten: Excellent for production-grade model serving. Baseten uses an open-source framework called Truss to package your fine-tuned model and its dependencies into a deployable artifact. It features a polished dashboard, robust API generation, and reliable autoscaling configurations that drop compute down to zero when requests stop.
• RunPod Serverless: A cost-effective infrastructure-level choice that provides serverless GPU endpoints. RunPod handles the scaling from zero to thousands of workers and utilizes optimized "FlashBoot" technology to minimize the cold-start penalty when your fine-tuned weights need to reload into VRAM.
• Hugging Face Inference Endpoints: If your fine-tuned model is already uploaded to the Hugging Face Hub, you can deploy it directly via dedicated endpoints that support scale-to-zero settings, keeping implementation simple if you are already within the HF ecosystem.

Key Tradeoff to Keep in Mind: Cold Starts

The main challenge with scaling a fine-tuned model to zero is the cold start . When a request hits a scaled-to-zero endpoint, the platform must provision a node, download/initialize the CUDA environment, and load your fine-tuned weights from storage into the GPU VRAM. For large models (like 7B+ parameter LLMs), this can introduce a latency spike of several seconds unless the platform uses snapshot-restore or optimized weight-caching.

To help narrow down the ideal platform, tell me:

• What is the size and architecture of your fine-tuned model (e.g., Llama-3-8B, a small LoRA adapter, or a custom BERT)?
• What is your tolerance for cold-start latency?

For a fine-tuned model that you control and need to serve at scale with scale-to-zero, my default pick in 2026 is Modal—with RunPod a very strong alternative if you want maximum container/GPU control.

🥇 I'd choose Modal

Modal treats your deployment as Python rather than requiring you to manage traditional infrastructure. It supports GPU-backed web endpoints, autoscaling, and scale-to-zero, while also providing mechanisms for reducing cold-start overhead.

That makes it particularly attractive if your fine-tuned model is something like:

HF weights → vLLM/TGI/custom Python → GPU → HTTP API

You can keep min_containers=0 (or the equivalent scale-to-zero configuration), so you aren't paying for an idle GPU.

🥈 RunPod if you want more control

I'd pick runpod.io if your team already has a Dockerized inference server or cares about portability.

RunPod explicitly supports custom containers, autoscaling from zero to hundreds of workers, per-second billing, and a broad range of GPUs. Its current FlashBoot offering is also specifically designed to reduce cold-start latency.

The important distinction is:

• Modal: "Give me a Python function and I'll handle the infrastructure."
• RunPod: "Give me your container and I'll run it on GPUs."

For a team already using Docker + vLLM, I'd lean RunPod.

When I'd use Baseten

If this is a serious customer-facing production API and you value a more opinionated, managed model-serving stack over infrastructure flexibility, Baseten is worth considering. It is particularly oriented around production model deployments and optimized serving engines.

My recommendation

If you tell me your model size (e.g. 7B/70B), framework (vLLM/TGI/custom), expected requests/sec, latency target, and whether traffic is bursty, I can give you a much more specific choice—and estimate the monthly cost of Modal vs RunPod vs Baseten for your workload.

When serving a fine-tuned model at scale with scale-to-zero capabilities, standard managed LLM APIs (like OpenAI or Anthropic) won’t work because you need to bring your own custom weights.

The ecosystem for serverless GPU infrastructure has matured significantly, with a few standout platforms built explicitly for custom model serialization, fast container execution, and true scale-to-zero mechanics.

Top Serverless Inference Platforms for Fine-Tuned Models

1. Modal (Best for Developer Experience & Speed)

Modal allows you to run arbitrary Python code and containerized ML workloads on serverless GPUs (A100, H100, L40S, etc.) using a Python-native decorator SDK.

• Scale-to-Zero: Fully supported. You only pay for active compute down to the exact millisecond/second of execution.
• Fine-Tuned Model Handling: You can easily mount model weights from Hugging Face or network volumes directly into the function environment.
• Cold Starts: Exceptionally fast compared to competitors due to optimized image layering and fast container instantiation, though large model weights still require download/initialization time unless cached on a network volume.
• Best for: Teams that want to deploy via code (modal deploy ) without managing Kubernetes manifests or complex Docker registries.

2. Baseten (Best for Production-Grade ML Operations)

Baseten is built specifically for high-performance model serving with Triton and vLLM under the hood, offering native scale-to-zero configurations.

• Scale-to-Zero: Defaults to a minimum of 0 replicas and scales up dynamically on incoming requests. It also features a wake endpoint API, letting you programmatically pre-warm a container (e.g., when a user loads a frontend page) to mitigate cold-start lag.
• Fine-Tuned Model Handling: Excellent support for custom weights via Truss (their open-source model packaging framework), making it trivial to pack custom fine-tunes with vLLM for optimized throughput.
• Best for: Production applications requiring robust monitoring, vLLM acceleration out-of-the-box, and fine-grained control over replica autoscaling limits.

3. RunPod Serverless (Best for Cost Efficiency at Scale)

RunPod provides budget-friendly serverless GPU endpoints.

• Scale-to-Zero: Scales to zero seamlessly, and undercuts major cloud providers significantly on raw GPU hourly costs when active.
• Fine-Tuned Model Handling: Requires a bit more friction—you must containerize your model using Docker and push to a registry. However, features like FlashBoot help cache snapshots to lower cold-start times.
• Best for: Cost-conscious teams that have already containerized their inference stack (e.g., a custom TGI or vLLM container) and want the lowest possible GPU compute rates.

Key Trade-off to Keep in Mind: The Cold-Start Penalty

No matter which serverless GPU platform you choose, scale-to-zero introduces a cold start when an idle model receives a sudden request. Loading a 7B or 70B fine-tuned parameter set from storage into VRAM takes anywhere from 5 to 45 seconds depending on weight size and network streaming.

• If your application cannot tolerate a 15–30 second delay on the first morning request, look into using a platform like Baseten with predictive wake webhooks, or keep a minimum of 1 warm replica active during peak business hours and scale to zero only overnight.

To help narrow down the ideal fit, tell me:

• What is the approximate size of your fine-tuned model (e.g., 8B, 70B parameters)?
• What kind of latency tolerance do you have for cold starts?
• Are you comfortable writing Python-native deployment scripts (like Modal) or do you prefer Docker containers?

For a fine-tuned model that you own and need to serve at scale with auto-scaling to zero, the best choice depends on your latency requirements, model size, and how much infrastructure control you want. The strongest options today are:

My recommendation by scenario

1. Best overall for a fine-tuned LLM: Modal

• Good choice if your team is Python-heavy.
• You can package your model, dependencies, and serving logic together.
• Supports GPU workloads, autoscaling, and scaling down to zero.
• Strong fit for:
  • LoRA/QLoRA fine-tunes
  • custom inference code
  • vLLM/TGI-style serving
  • bursty traffic where idle GPUs are expensive

Pick Modal if: you want the shortest path from “model checkpoint” → “production endpoint.”

---

2. Best for maximum control and lower cost: RunPod Serverless

• Lets you bring your own container.
• Good if you already have a Docker image with CUDA, vLLM, TensorRT-LLM, or custom inference code.
• Supports scale-to-zero workers and pay-for-compute usage.

Pick RunPod if: you have ML infrastructure experience and want fewer platform constraints.

---

3. Best for enterprise inference: Baseten

• Stronger production tooling around model deployments, monitoring, and managed serving workflows.
• Better fit when you need a more managed MLOps experience.

Pick Baseten if: reliability, observability, and operational maturity matter more than minimizing cost.

---

4. Best for simple model APIs: Replicate

• Very easy developer experience.
• Good if you want to expose a model quickly through an API.
• Less ideal if you have a large custom fine-tuned model where cold starts dominate.

Pick Replicate if: you want “upload model → get API” rather than build serving infrastructure.

---

Practical architecture I would use

For a fine-tuned LLM:

Fine-tuned weights
        |
        v
vLLM / TGI / custom server
        |
        v
Modal or RunPod Serverless GPU endpoint
        |
        v
API Gateway + auth + rate limits

A few things matter more than the platform:

• Model loading time: scale-to-zero means the first request after idle pays a cold-start penalty.
• Weight storage: keep weights on fast persistent storage rather than downloading from scratch.
• Batching: use continuous batching (e.g., vLLM) to improve GPU utilization.
• Warm replicas: for latency-sensitive apps, keep one replica warm and scale additional replicas from zero.

Short answer

• Most teams: Modal
• Lowest-cost / most flexible: RunPod Serverless
• Enterprise production serving: Baseten
• Fastest prototype: Replicate

For a serious fine-tuned model serving workload, I would start with Modal or RunPod, then move to Baseten if operational requirements grow.

When looking to serve a fine-tuned model at scale with native scale-to-zero capabilities , the "best" platform depends heavily on whether your model is a Large Language Model (LLM) utilizing parameter-efficient fine-tuning (like LoRA) or a traditional/custom ML model deployed inside a container.

The top platforms for this specific use case are categorized below by their structural strengths.

1. Developer-First Specialized Platforms (Best for Fast Iteration)

These platforms are purpose-built for AI model deployment, providing custom open-source packaging frameworks that abstract away CUDA configurations while fully supporting dynamic scale-to-zero autoscaling.

• Baseten : Uses an open-source packaging framework called Truss. It natively supports scaling to zero and is highly optimized for custom and fine-tuned models. Cold starts are minimized through intelligent image caching.
• Modal : A highly efficient serverless platform where you define your infrastructure in pure Python. It features exceptionally fast cold-start times (often under a few seconds) and scales instantly from zero to hundreds of concurrent GPUs based on container queues.
• RunPod (Serverless vLLM) : Ideal if your model is an LLM. By using their serverless endpoint workers pre-configured with vLLM, you get native scale-to-zero, request batching, and highly competitive per-second GPU billing.

2. Dedicated LLM Serverless Providers (Best for Multi-LoRA Scaling)

If you have fine-tuned an LLM using LoRA (Low-Rank Adaptation) , you do not actually need to spin up a dedicated GPU per fine-tuned model. Instead, look for platforms that dynamically swap fine-tuned adapters over a shared base model.

• Fireworks.ai : Supports "Multi-LoRA" deployment. You deploy your fine-tuned weights, and Fireworks mounts them on top of a running base model (e.g., Llama 3) on the fly. This completely bypasses the traditional cold-start penalty of loading an entire model, scaling from zero seamlessly.
• DeepInfra : Similar to Fireworks, they offer serverless LLM deployment with custom LoRA adapters, billing you strictly per token used rather than for idle uptime.

3. Hyperscaler Serverless (Best for Enterprise & Ecosystem Lock-in)

If your infrastructure must remain inside a major public cloud for data privacy or compliance reasons, use these container-based serverless engines.

• Google Cloud Run (with GPU) : Cloud Run natively scales to zero and supports Nvidia L4 GPUs. You package your fine-tuned model into a container (e.g., using Ollama or vLLM), and GCP manages the scaling infrastructure. Cold starts can be slightly longer here as the container and model weights must initialize.
• AWS SageMaker Serverless Inference : SageMaker offers dedicated serverless endpoints that automatically scale with traffic and scale to zero when idle. Note: Make sure your fine-tuned model fits within SageMaker's serverless total storage allocation limits.

Platform Comparison Summary

Key Recommendation

• If your fine-tune is a LoRA adapter for an open LLM, go with Fireworks.ai.
• If your fine-tune is a fully baked/custom model architecture , use Modal or Baseten to get the absolute best balance of easy scaling, developer tooling, and fast cold-start performance.

To help narrow this down, could you tell me:

• What specific model architecture are you deploying (e.g., Llama-3-8B, Flux, ResNet)?
• What is your expected peak traffic (QPS) versus your average idle period duration?

For a fine-tuned model that you need to serve at scale with auto-scaling to zero, the “best” platform depends mostly on your model type, latency requirements, and how much infrastructure control you want. The strongest options today are:

My default recommendation

For most fine-tuned LLMs: start with Modal or Runpod.

Pick Modal if:

• Your model is a custom fine-tune and you want minimal DevOps.
• Your team is comfortable with Python.
• You want to deploy with code rather than managing containers.
• Traffic is bursty and you care about scaling from zero without paying for idle GPUs.

Modal supports custom model weights and scale-to-zero inference endpoints.

Pick Runpod if:

• You already have a Docker image or inference server.
• You want maximum GPU choice and cost control.
• You are serving with vLLM, TensorRT-LLM, TGI, or a custom FastAPI server.
• You expect high utilization and want to optimize GPU cost.

Runpod Serverless supports custom containers, autoscaling workers, and scaling down to zero when idle.

Pick Baseten if:

• This is a customer-facing production API.
• You want a managed model-serving workflow with monitoring and deployment tooling.
• You value operational simplicity over lowest raw GPU cost.

Baseten supports bringing custom/fine-tuned models and provides managed autoscaling inference deployments.

A practical architecture I’d use

For a fine-tuned Llama/Qwen/Mistral-style model:

Client
  |
  v
API Gateway
  |
  v
Serverless GPU Endpoint
  |
  +-- vLLM/TGI runtime
  |
  +-- Model weights from object storage

Recommended choices:

• Low traffic / unpredictable traffic: Modal → easiest
• High traffic / cost optimization: Runpod Serverless + vLLM
• Enterprise production: Baseten

A few things matter more than the platform:

1. Cold start time — a 70B model loading from zero can dominate latency.
2. Model caching — keep weights on fast persistent storage.
3. Batching — vLLM continuous batching can dramatically improve GPU utilization.
4. Warm replicas — even with scale-to-zero, many teams keep 1 warm worker for interactive applications.

If you share:

• model size (e.g., 7B/13B/70B),
• framework (vLLM, TGI, PyTorch, TensorRT),
• expected requests/sec,
• latency target,
• GPU preference (A10/L40S/A100/H100/etc.),

I can narrow this down to a specific deployment choice.

Choosing the "best" serverless inference platform with scale-to-zero support for a fine-tuned model depends heavily on your tech stack, your model's weight size, and how much you want to deal with infrastructure configuration.

Specialized serverless GPU platforms have largely overtaken traditional hyperscalers (like standard AWS Lambda or basic container setups) for custom machine learning inference because they minimize cold starts and bill strictly per second of active GPU usage.

Top platforms for serving custom, fine-tuned models with auto-scaling to zero include:

• Modal (Best for Python-First Developer Experience) — Modal lets you write and deploy infrastructure directly using Python decorators. It features industry-leading cold start optimization, scales completely to zero, and bills per second. It is ideal if you want to push code from a local environment or notebook straight to a secure endpoint without wrestling with Docker or Kubernetes configuration.
• RunPod Serverless (Best Value & Performance Control) — RunPod uses FlashBoot technology to cache containers on the host, drastically lowering spin-up times for custom models. It offers true scale-to-zero capabilities with per-second billing and gives you access to a wide array of GPU tiers (from consumer cards up to H100s), making it the value pick if you bring your own custom Docker container.
• Baseten (Best for Production-Grade Custom ML Pipelines) — Baseten is built specifically for production machine learning. It supports custom fine-tuned weights, provides performant serving frameworks (like vLLM) under the hood, features auto-scaling to zero, and exposes an OpenAI-compatible API layer so you don't have to rewrite your client code.
• Google Cloud Run with GPUs (Best for Enterprise Cloud Native) — If you are locked into a major cloud ecosystem or require strict enterprise compliance, Cloud Run supports containerized applications with scale-to-zero semantics and NVIDIA L4 GPU attachments. It handles HTTPS routing and scales down to absolute zero when idle, though it requires more manual container tuning than developer-first platforms like Modal.

To help narrow down which platform fits best, could you tell me:

• What framework and size is your fine-tuned model (e.g., Llama-3-8B, a custom BERT, etc.)?
• What kind of expected traffic volume or burstiness do you have (e.g., sporadic internal tool vs. unpredictable public traffic)?

For a fine-tuned model that you own and need to serve at scale, I’d pick Runpod Serverless as the default choice today.

Runpod Serverless supports custom containers, GPU selection, automatic scaling from zero to hundreds of workers, and per-second billing. Its current FlashBoot infrastructure is specifically designed to reduce cold starts.

My shortlist

Runpod is particularly attractive if your fine-tune needs a specific CUDA version, inference engine, custom preprocessing/postprocessing, or model weights. You can bring your own container rather than adapting the model to a platform's supported model catalog.

The important caveat: cold starts

Scale-to-zero isn't free from a latency tradeoff. When the first request arrives after an idle period, the platform has to obtain a GPU and initialize your model. For a large LLM, loading weights into VRAM can dominate that latency even if the container/image is already cached.

So I'd configure:

• min workers = 0 for genuinely bursty traffic
• 1+ warm workers if you need predictable interactive latency
• max workers based on your expected traffic spike
• continuous/in-flight batching (e.g. vLLM) for high request concurrency

Runpod's current serverless offering supports both scale-to-zero and keeping workers warm, so you can move between those modes without changing your deployment architecture.

Bottom line: If you tell me your model size (e.g. 7B/70B), GPU requirement, requests/sec, average tokens/request, and acceptable cold-start latency, I can recommend the specific platform + GPU + serving stack and estimate the monthly cost.

When serving a fine-tuned model at scale with scale-to-zero capabilities, the "best" platform depends on whether you are bringing a raw custom container/weights or utilizing adapter-based fine-tuning (like LoRA).

The leading serverless GPU and inference platforms supporting auto-scaling to zero are categorized below by their strengths:

• Modal (Best Python-First Experience) — Highly optimized for GPU-native workloads with clean, decorator-based Python SDKs. It handles scale-to-zero natively, bills precisely per second of active compute, and avoids heavy Kubernetes/Docker configurations. Trade-off: First-request cold starts can take tens of seconds while loading large custom weights.
• RunPod Serverless (Best Cost-to-Performance Value) — Offers robust serverless endpoints that auto-scale from 0 to thousands of workers and back down to zero. It supports bringing your own custom Docker containers or quick-deploy options via vLLM and SGLang. Trade-off: Requires more hands-on container configuration than Modal.
• Baseten (Best for Production-Grade ML Ops) — Built explicitly for high-scale production AI. It features OpenAI-compatible endpoints, advanced model tracing, optimized streaming via vLLM/TGI, and fast autoscaling down to zero without sacrificing enterprise monitoring.
• Google Cloud Run (with GPUs) (Best Enterprise Managed Infrastructure) — Supports scale-to-zero container execution backed by NVIDIA L4 GPUs in preview/select tiers. Ideal if you want to containerize an open-source inference engine like vLLM and store your fine-tuned weights directly in Google Cloud Storage.
• Together AI / Fireworks AI (Best if using LoRA/Adapters) — If your fine-tuning uses PEFT/LoRA rather than full-weight modifications, these platforms allow you to serve fine-tuned adapters on top of shared warm base models via API, entirely bypassing heavy cold starts.

To help narrow down the optimal choice, please share:

• Is your model a full-weight fine-tune or a LoRA adapter?
• What is the approximate size of the model (e.g., 8B, 70B parameters) and your expected traffic pattern (spiky vs. continuous)?