5 serverless GPU platforms for AI workloads

Managing GPU infrastructure for AI workloads means planning capacity for traffic you can't predict. Idle H100s rack up costs between inference bursts, and cold starts then undercut the user experience when real requests arrive. AI infrastructure splits into compute layers: the model layer on GPU and the execution layer on CPU.

This guide covers how serverless GPU platforms work, which ones are worth evaluating for production workloads, and the CPU execution layer that sits next to them in modern AI stacks.

What "serverless GPU" actually means

A serverless GPU platform allocates a GPU only when a request arrives, scales to zero between jobs, and bills only for active compute time. The platform handles driver setup, CUDA initialization, model weight loading, and autoscaling. You send a request and get results back without managing the underlying hardware.

Traditional GPU cloud works differently. Bare-metal rental gives maximum performance but requires manual driver and OS management. Reserved VMs charge a constant rate whether you're processing requests or not. Kubernetes node pools add orchestration but still require GPU provisioning. All three models force you to predict demand in advance.

The core tradeoff is cold starts in exchange for zero idle cost. When no warm instance exists, the platform must provision hardware, load libraries, and fetch model weights. That delay ranges from seconds to minutes, depending on model size and platform architecture. For bursty or unpredictable workloads, the savings outweigh the cold start penalty. For latency-critical production traffic, dedicated endpoints often make more sense.

1. Baseten

Baseten is a production model-serving platform built around its open-source Truss framework. It packages models into deployable APIs via a Model class and YAML config. The platform handles containerization, TensorRT-LLM compilation, and GPU scheduling across multiple clouds.

Pros

• Truss framework: An open-source CLI for PyTorch, TensorFlow, and Hugging Face models. Write a Model class with load() and predict() methods, configure config.yaml, run truss push, and the platform deploys an optimized endpoint. Supported engines include TensorRT-LLM, vLLM, and SGLang.
• Built-in autoscaling and async inference: Every deployment adjusts replicas based on traffic. Configurable min/max replicas, concurrency targets, and scale-down delays ship with the platform.
• Pre-optimized model APIs: OpenAI-compatible endpoints for DeepSeek, Qwen, and GLM. Point the OpenAI SDK at Baseten's URL and update your Baseten API key and model name as required.
• Strong observability: Metrics and traces ship with every deployment, and the platform exports data to external monitoring tools.

Cons

• Cold starts vary by model size: Very large models can take much longer to cold start than smaller ones. Baseten frames this as a worst-case scenario rather than the norm, but the variance means capacity planning still matters above a certain size threshold. Setting min_replicas to at least one eliminates cold starts but adds continuous billing.
• Model-serving focus: Baseten optimizes the inference path specifically. Teams needing custom training loops, reinforcement learning environments, or non-inference GPU workloads will need a separate platform. The Truss framework and autoscaling infrastructure are built around the load()/predict() pattern.

Best for: ML teams deploying production inference APIs that need autoscaling and observability out of the box.

Pricing note: Active billing applies on dedicated deployments, and a free basic plan covers pay-as-you-go usage. Check the Baseten pricing page for current rates.

2. Together AI

Together AI is a serverless inference platform focused on large language models and multimodal models. It hosts a large model catalog across chat, vision, image, audio, video, and embedding categories. The platform offers an OpenAI-compatible API, serverless inference, and dedicated endpoints.

Pros

• Optimized LLM inference: Strong throughput tuning for Qwen and DeepSeek models. The platform supports multiple quantization formats across its serverless catalog, and its ATLAS speculative decoding system is available on dedicated endpoints.
• OpenAI-compatible API: Migration from the OpenAI SDK requires changing the API key and the base URL. Chat completions, embeddings, vision, and function calling all work.
• Dedicated production endpoints: Reserved, single-tenant compute with configurable autoscaling and multi-GPU support. This gives more predictable latency for traffic that can't tolerate variability.
• Competitive per-token economics: Batch inference offers discounted token pricing, and dedicated endpoints bill on reserved hardware.

Cons

• Narrow scope: Together AI is optimized for model inference and fine-tuning, not arbitrary GPU code. Teams running custom CUDA kernels, custom training loops, or non-standard inference pipelines need a second platform. The dedicated container tier supports custom runtimes, but it's available only to paid-tier users through Together AI's deployment workflow. That adds procurement delay and removes self-serve flexibility.
• Custom model setup friction: The serverless API is instant for catalog models. Pick a model string, send a request, get a response. Deploying your own weights means using dedicated endpoints, configured via the web UI or API/CLI, with GPU selection, autoscaling parameters, and model upload as manual steps. Other platforms make custom model deployment more self-serve through a config file and one CLI command.

Best for: Teams running production LLM inference at volume with open-source models.

Pricing note: Serverless models use per-token billing with separate input and output rates. Check the Together AI pricing page for current figures.

3. Cerebrium

Cerebrium is a Python-native serverless GPU platform for ML models and custom inference code. It targets ML engineers shipping GPU-backed APIs without container pipelines. GPU tiers span entry-level and high-end hardware, and GPU, CPU, and memory bill as separate line items.

Pros

• Python-native deployment: The CLI deploys custom models through a cerebrium.toml config and a Python entry point. Running cerebrium deploy handles containerization and endpoint creation, and cerebrium run executes code in the cloud quickly without CI/CD involvement.
• Fast cold starts on supported models: Cold starts land in the low hundreds of milliseconds for smaller supported models, and warm-start platform overhead is low. Tensorizer can reduce load times for larger models.
• Broad GPU selection: Multiple GPU tiers are available with multi-GPU support, and the platform supports deployment across several regions.
• Transparent billing: GPU, CPU, and memory are billed separately, and cold start container spin-up time isn't billed.

Cons

• Smaller ecosystem: Fewer community examples and third-party tutorials exist compared to longer-established platforms. Expect to spend more time on initial setup and debugging with official documentation as the primary resource. The docs are thorough, but the absence of a broad community knowledge base adds friction during onboarding.
• Narrower ML-only scope: Cerebrium is designed specifically for ML inference and fine-tuning pipelines. Teams needing general-purpose GPU compute for rendering, physics simulation, scientific computing, or video transcoding should look elsewhere.

Best for: Python teams shipping custom ML inference APIs without managing containers or Kubernetes.

Pricing note: Active billing uses separate GPU, CPU, and memory line items. A hobby tier starts at no base cost. Check Cerebrium's pricing page for current rates.

4. Replicate

Replicate is a hosted model marketplace and inference API with a large community-published model library. Teams call pre-built models via a single API request or deploy custom models using Cog, an open-source packaging framework.

Pros

• Large community model library: The platform hosts thousands of models across generative media, language, vision, and audio.
• Zero-config API calls: Hitting a pre-built model takes a single API request, with Python, Node.js, and HTTP REST clients supported.
• Cog framework: An Apache-licensed tool with a large GitHub community. It generates Docker images from a cog.yaml file and handles CUDA and dependency management.
• Fast prototype-to-production path: Strong fit for shipping features on known model architectures. Hardware selection happens by named SKU with no code changes.

Cons

• Pricing complexity on custom deployments: Private models can bill beyond active inference alone, so teams that misconfigure instance settings can end up paying for unused capacity during low-traffic windows. Reaching efficient spend requires tuning autoscaling parameters, monitoring usage, and iterating on instance limits.
• Less hardware control: The platform abstracts GPU selection behind named SKUs. That speeds prototyping but limits teams needing specific GPU configurations for benchmarking or compliance. Engineers can't pin workloads to particular hardware generations or regions with the same granularity as lower-level platforms.

Best for: Teams prototyping with open-source models or shipping features without managing deployment.

Pricing note: Pay-per-prediction on pre-built models with usage-based billing. Check the Replicate pricing page for current rates.

5. Fal.ai

Fal.ai is a real-time inference specialist focused on media generation. Its catalog of models covers image, video, audio, 3D, and music, across two tiers: fal Model APIs and fal Serverless.

Pros

• Configurable warm capacity: Fal.ai exposes configuration options like min_concurrency that keep runners warm for latency-sensitive workloads. Cold starts aren't low by default on every endpoint, so benchmark your specific model before committing.
• Ready-made endpoints: Out-of-the-box APIs for FLUX variants, Seedream, Kling, Wan, Veo, and Sora. The model catalog covers text-to-image, video, and audio.
• Streaming-friendly API: Multiple invocation patterns work with the same platform: synchronous, async queuing with webhooks, and real-time WebSockets. FLUX.1 [dev] supports streaming for real-time generation.
• Specialized fine-tuning tooling: Built-in Low-Rank Adaptation (LoRA) support for image and video models. WAN LoRA Training ships as a managed endpoint, while LoRA-enabled inference endpoints are available for FLUX.1 models.

Cons

• Media-first tooling for non-media workloads: Fal.ai supports LLM inference and streaming text outputs alongside its media catalog, but the platform's tooling, examples, and optimization work are centered on generative media. Teams running LLM-heavy workloads will find better-tuned paths on LLM-specialized platforms.
• Fragmented pricing across tiers: Fal.ai's service tiers use different billing structures across managed APIs, custom serverless deployments, and compute offerings. Predicting total cost across use cases takes more modeling than platforms with a single billing unit.

Best for: Teams building image, video, or audio generation features where latency shapes user experience.

Pricing note: Custom GPU deployments use usage-based billing, while managed model APIs use output-based billing. Check the fal.ai pricing page for current rates.

Beyond GPU: the execution layer AI agents need

The platforms above cover model compute on GPU. Production AI agents often need another compute layer that sits alongside model inference.

A coding agent makes this concrete. It calls an LLM to generate code, and the resulting code then needs to execute somewhere for tests, database queries, or file modifications. Execution happens on CPU, not GPU, and it needs strong isolation when the code comes from a model.

Blaxel is a perpetual sandbox platform that fits this gap. It runs each workload in an isolated microVM environment with hardware-enforced tenant isolation. Sandboxes resume from standby in under 25 milliseconds. They automatically return to standby after 15 seconds of network inactivity. Standby duration is unlimited at zero compute cost.

MicroVMs matter because model-generated code is not trusted code. Sharing a host kernel across tenants is a weaker boundary than hypervisor isolation. Teams using Agents Hosting can co-locate agent logic with the sandbox to reduce network latency. Blaxel's Model Gateway provides unified model routing, telemetry, and cost control. For tool execution, MCP Servers Hosting exposes tool endpoints as serverless MCP servers with built-in authentication and observability.

This pattern extends beyond coding agents. Data analyst agents generate SQL or Python queries against live databases and need isolated environments that prevent malformed queries from affecting production data. PR review agents check out repositories and run test suites against changed code, so their sandboxes need full filesystem access without cross-tenant contamination.

Research agents fetch external data and run analysis scripts on the results, combining network access with code execution in a controlled boundary. In each pattern, the model runs on GPU somewhere upstream, but the code it produces runs on CPU inside an isolated environment.

When evaluating serverless GPU platforms, factor in whether your workload needs that second layer. If it does, the GPU platform decision is only half the architecture.

How to choose the right platform

Match workload type to platform strength. Most production teams use more than one platform.

• Production LLM inference at volume: Baseten gives autoscaling, observability, and TensorRT-LLM optimization for many large customers such as Cursor.
• Custom ML inference or fine-tuning: Cerebrium gives a Python-native path with granular billing, and Replicate gives a faster route through its community library and Cog.
• Real-time media generation: Fal.ai is purpose-built for image, video, and audio, and its streaming APIs are designed for media workloads.
• Agent code execution downstream of model output: Pair your GPU platform with an isolated CPU sandbox. Coding, PR review, and data analyst agents often need environments for running untrusted code.

One threshold matters for user-facing inference. Jakob Nielsen's 1993 usability research cites a 100-millisecond threshold as about the limit for an interaction to feel instantaneous. Delays above a few hundred milliseconds become noticeable and start to disrupt the feeling of immediacy, though interactive experiences can remain usable up to about 1 second.

For latency-critical workloads, dedicated endpoints or a paired CPU sandbox layer often close the gap that raw serverless GPU timing leaves open.

Pick the serverless GPU layer, then close the gap with a sandbox layer

The serverless GPU category has fragmented into specialists, and picking the right one is only the first half of a production AI architecture. The second half is the CPU-side execution layer that runs the code, and the tool calls your model produces. Teams that skip this question ship either slow, cold-started endpoints or shared environments that leak data across tenants.

Blaxel is the perpetual sandbox platform that fills that second slot. Sandboxes resume from standby in under 25 milliseconds. They return to standby after 15 seconds of network inactivity and stay there indefinitely at zero compute cost.

Agents Hosting co-locates agent logic next to the sandbox, which eliminates network hops on every tool call. MCP Servers Hosting exposes tool endpoints with built-in authentication. Model Gateway provides a unified routing and telemetry layer over your LLM providers. See the Blaxel Sandboxes page for the technical details.

To see how this fits your agent stack, book a demo or start building for free.

FAQ

What is a serverless GPU platform?

A serverless GPU platform allocates GPU compute only when a request arrives, scales to zero between jobs, and bills only for active usage. The platform handles driver setup, CUDA initialization, model weight loading, and autoscaling, so you send a request and get results back without provisioning or managing hardware.

When does serverless GPU make sense?

It fits bursty or unpredictable workloads where avoiding idle GPU cost matters more than occasional cold starts. Typical examples include batch inference jobs, internal tools with sporadic usage, and new product features still finding product-market fit. For these workloads, the savings from active-only billing usually outweigh the latency penalty of spinning up hardware on demand.

When should you avoid serverless GPU?

If your workload is latency-critical and cannot tolerate cold starts, dedicated endpoints or reserved capacity often make more sense. User-facing inference on high-traffic features, coding assistants that return completions inline, and real-time media generation all sit in this category. The predictable warm capacity of a dedicated endpoint removes the cold start variance that breaks interactive experiences.

Why do AI agents need a CPU execution layer too?

Models generate outputs on GPU, but actions like running code, testing changes, querying databases, or modifying files happen on CPU. That execution also needs hardware-enforced isolation because the code came from a language model rather than a trusted developer. Without an isolated sandbox, a single malformed or malicious output can reach production data or other tenants.

What does Blaxel add to this stack?

Blaxel is a perpetual sandbox platform that covers the CPU execution side. Sandboxes resume from standby in under 25 milliseconds and stay in standby indefinitely at zero compute cost. Agents Hosting co-locates agent logic next to the sandbox, MCP Servers Hosting exposes tool endpoints as serverless MCP servers, and Model Gateway provides unified model routing and telemetry over your LLM providers.

Do most teams use only one platform?

Not usually. Most production teams mix platforms based on workload type. LLM inference might run on one provider, with media generation on another. A separate CPU sandbox then handles agent-generated code. Splitting the stack by workload keeps each layer specialized. One platform rarely covers every AI compute pattern equally well.