Best runtime platforms for AI applications and production infrastructure

Running an AI application in production involves more than hosting a model. Depending on the workload, you may need GPU inference, isolated environments for AI-generated code, application hosting, databases, storage, CI/CD, secrets, access controls, and observability.

The challenge is that most platforms specialize in one or two of these areas. This guide compares the best runtime platforms for AI applications in 2026, what each platform covers, and which workloads they are best suited for.

An AI runtime platform provides the infrastructure needed to run an AI application in production. Depending on the application, this can include model inference, agent execution, application hosting, databases, and deployment infrastructure.

Some platforms focus on a specific part of the stack, such as GPU inference or isolated environments for AI agents, while others cover multiple layers from a single platform. The right choice depends on what your application needs to run reliably in production and which parts of the infrastructure you want the platform to manage.

AI applications can look very different from traditional web applications, but most production systems eventually need several common infrastructure layers:

• Model inference: Run models on CPUs or GPUs. Managed APIs handle this for you, while self-hosted models require GPU infrastructure and a serving framework.
• Agent and code execution: Run AI-generated code, shell commands, browser sessions, and other tools in isolated environments. Stronger isolation, such as microVMs, can be useful for untrusted or AI-generated code.
• Application hosting: Run APIs, frontends, background workers, scheduled jobs, webhooks, and other services that make up the application.
• Data and storage: Store application state, embeddings, caches, files, datasets, and model artifacts using databases such as PostgreSQL, caches such as Redis, and object storage.
• Security and governance: Manage access control, SSO, secrets, private networking, audit logs, and separate development, staging, and production environments. Regulated workloads may also require control over where infrastructure and data are hosted.

Northflank is a runtime platform for running applications and infrastructure, including AI workloads. It combines GPU compute, application deployment, isolated sandboxes, databases, CI/CD, and infrastructure management in a single platform.

That makes it particularly useful when an AI application needs more than model inference alone.

What it covers

Northflank supports GPU workloads for AI inference and other GPU-intensive applications, alongside standard CPU workloads. It also provides microVM-backed sandboxes for running untrusted or AI-generated code. Sandboxes run in isolated environments with their own kernel and are designed for workloads such as AI agents, generated code, user-submitted code, and CI/CD jobs.

The platform also provides application deployment, CI/CD, environments, networking, persistent storage, and managed databases. This allows the API, workers, inference services, sandboxes, and data infrastructure for an AI application to be managed from the same platform.

Northflank also supports deploying sandboxes into your own cloud infrastructure through BYOC.

Why choose it

Northflank is a strong option when you need multiple parts of an AI production stack rather than a single specialized service.

For example, an AI coding application could run its web application and API on Northflank, use GPU-backed services for inference, create isolated sandboxes for agent-generated code, and connect those workloads to persistent databases and storage.

The same infrastructure can also be deployed in your own cloud environment when greater control over the infrastructure is required.

Limitations

If you only need managed model inference, a specialized inference platform may be a better fit. Northflank is designed for teams that need to run and manage the broader production environment around their AI application.

Best for

Northflank is best for teams building production AI applications that need a combination of GPU compute, agent execution, application hosting, data infrastructure, and infrastructure control.

Modal is a serverless infrastructure platform for AI and compute-intensive workloads. It provides GPU compute, inference, training, batch workloads, and isolated sandboxes through a code-first interface.

What it covers

Modal supports GPUs including A100, H100, H200, B200, and B300, depending on availability. Its Sandbox product provides isolated environments for running arbitrary or AI-generated code, with support for GPUs and persistent storage. Modal also supports inference, training, fine-tuning, and batch workloads.

Why choose it

Modal is a strong choice for teams that want serverless GPU infrastructure through a code-first workflow. You can use the same platform for inference, training, batch jobs, and sandboxed execution.

Limitations

Modal is primarily focused on AI and compute infrastructure rather than broader application infrastructure. Teams that need managed databases, extensive application services, or infrastructure portability may need additional services.

Best for

Modal is best for ML engineers and AI teams that prioritize serverless GPU compute, inference, training, and sandboxed execution.

E2B focuses on running AI-generated code in isolated environments. It provides the execution layer that AI agents use to run code, commands, and other tools.

What it covers

E2B provides sandbox environments where agents can execute code, inspect results, and destroy the environment when the task is complete. This makes it useful for coding agents, data analysis, code interpreters, and applications that execute user-generated code.

Why choose it

E2B is useful when you already have your application hosting and model infrastructure but need a dedicated, secure environment for code execution.

Limitations

E2B is not a complete application runtime. You still need separate infrastructure for your application, databases, model inference, and other services.

Best for

E2B is best for AI applications where isolated code execution is the primary infrastructure requirement.

Amazon Bedrock AgentCore provides a managed runtime for deploying and running AI agents and tools. It is framework-agnostic and supports frameworks such as LangGraph, Strands, and CrewAI.

What it covers

AgentCore handles infrastructure management, scaling, session management, and security isolation for agent workloads. It can also work with different model providers.

Why choose it

Its main advantage is integration with AWS. Teams already using AWS can build their agent runtime around AgentCore while keeping their wider infrastructure in the AWS ecosystem.

Limitations

AgentCore is primarily suited to AWS-centric teams. Organizations that need multi-cloud or infrastructure portability may prefer a platform that runs across different environments.

Best for

Amazon Bedrock AgentCore is best for organizations building AWS-native AI agents that want a managed agent runtime.

Replicate provides a managed API for running AI models without managing GPU infrastructure yourself. You can run published models or deploy your own custom models.

What it covers

Replicate provides access to models for image, video, language, and audio workloads, along with support for custom models and dedicated deployments.

Why choose it

Replicate removes much of the infrastructure work involved in model serving. Instead of provisioning GPUs and configuring a serving stack, you can call models through an API.

Limitations

Replicate primarily handles model execution. Your application, databases, authentication, background workers, and other infrastructure still need to run elsewhere.

Best for

Replicate is best for teams that want managed model inference without operating GPU infrastructure.

Together AI provides managed inference for open-source models through serverless models and dedicated endpoints.

What it covers

Together AI focuses on inference across a broad range of open-source models. Dedicated endpoints provide reserved infrastructure for workloads that need more predictable performance or consistent latency.

Why choose it

Together AI gives teams access to open models without requiring them to operate the underlying GPU infrastructure and serving stack.

Limitations

Together AI focuses primarily on inference rather than the complete application runtime. You still need separate infrastructure for your application, databases, agent execution, and other services.

Best for

Together AI is best for teams that want managed open-source model inference, particularly when model variety and dedicated endpoints are important.

The right platform depends on how much infrastructure your AI application actually needs.

AI applications are becoming more infrastructure-intensive. A production system may need model inference, GPU compute, isolated agent execution, application hosting, databases, storage, CI/CD, and security controls, and no single platform is necessarily the right choice for every layer. Specialized platforms can make sense when you need a specific capability, such as Replicate and Together AI for inference, E2B for isolated code execution, or Amazon Bedrock AgentCore for AWS-native agent workloads.

For teams that need more of the production stack in one place, Northflank provides a broader runtime environment for AI applications, combining GPU workloads, isolated sandboxes, application deployment, databases, CI/CD, and infrastructure control. The best runtime platform ultimately depends on the infrastructure your application needs and how much complexity your team wants to manage.

An AI runtime platform provides the infrastructure needed to run AI applications in production, including compute, inference, agent execution, application hosting, data, and security.

An inference platform focuses on running models, while an AI runtime can cover the broader infrastructure needed to run the application around those models.

Not always. You only need GPUs if you are running models yourself or performing GPU-intensive workloads such as inference or fine-tuning.

If an agent executes AI-generated or user-provided code, isolated sandboxes can provide an additional security boundary and reduce the risk of that code affecting your application or infrastructure.

Yes. You can combine specialized platforms for inference, agent execution, and application hosting, although using multiple platforms increases integration and operational complexity.

Managed inference is simpler because the provider handles the infrastructure and scaling. Self-hosting gives you more control over costs, hardware, latency, and data residency.

There is no single best option. Northflank is a strong choice for teams that need application hosting, GPU workloads, agent sandboxes, databases, CI/CD, and infrastructure control in one platform, while platforms such as Modal, E2B, Replicate, and Together AI focus on specific parts of the AI runtime stack.