The 9 Best Infrastructure Platforms for Running Agentic Workloads in 2026

Key points:

• The best infrastructure platform for agentic workloads depends on which of four jobs you are solving: sandboxed execution of model-generated code (E2B, Daytona), GPU and serverless inference or batch (Modal), durable orchestration of long multi-step runs (Trigger.dev, LangGraph Platform, Temporal), and hosting the agent app plus its data layer (Northflank, Fly.io, TrueFoundry, Qovery). Most 2026 stacks combine two or three, because no single vendor covers all four well.
• If your agents execute model-generated code, use a purpose-built sandbox with kernel-level isolation, not a shared container. E2B and Daytona both run on microVM-class isolation with sub-second start, and Firecracker itself boots a microVM in under 125 ms with under 5 MiB of overhead per VM. Running generated code in your app's container hands that code your app's credentials and network.
• If your agents only call models and read internal systems, and most enterprise agents only do that, sandboxing is not the binding constraint. Data residency, private access to internal databases, egress cost and per-run token spend are. That points to running the agent inside your own cloud account or Kubernetes cluster.
• Qovery covers the fourth job: it deploys agent APIs, workers, queues and databases inside your own AWS, GCP, Azure or Scaleway account, or your existing Kubernetes cluster, so prompts, tool outputs and customer data stay in your VPC and the cloud bill plus committed-spend discounts stay in your name. Northflank and TrueFoundry also do bring your own cloud, so this is a three-way comparison, not a Qovery monopoly.
• Agent bills are dominated by model tokens and idle compute spent waiting on model responses, not CPU efficiency. The levers that move the number are prompt caching (Anthropic and OpenAI both publish steep cached-input discounts), scale-to-zero workers, auto-stop on non-production environments, spot nodes for retryable batch, and hard per-run step and token ceilings.

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What are the top infrastructure platforms for running agentic workloads in 2026?

The nine infrastructure platforms for agentic workloads worth shortlisting in 2026 are E2B and Daytona for sandboxed execution of model-generated code, Modal for on-demand GPUs and serverless batch, Trigger.dev and LangGraph Platform (with Temporal as the general-purpose alternative) for durable orchestration of long runs, and Northflank, Fly.io, TrueFoundry and Qovery for hosting the agent application and its data layer. No vendor covers all four jobs well, so the real decision is which two or three you compose, driven by whether the agent runs its own code, how long a run lasts, and whether prompts may leave your cloud account.

The normal outcome is a composed stack: the agent API and Postgres on Qovery inside your own account, E2B for untrusted code execution, a hosted model API for inference, and LangGraph Platform or Trigger.dev holding run state. Three questions set the shape: does the agent execute code it generated itself, how long does a run last and must it resume mid-flight, and can prompts and tool outputs legally leave your own cloud account.

For enterprise agents in 2026, runtime ownership (your VPC, your account, your Kubernetes) decides the platform far more often than cold-start milliseconds. The stakes are real: Gartner predicts over 40% of agentic AI projects will be canceled by the end of 2027 on cost and weak risk controls, and McKinsey's 2025 State of AI found 62% of organizations are at least experimenting with AI agents but only 23% are scaling one anywhere. This category changes monthly, so every claim below was checked against vendor docs as of early 2026.

What makes agentic workloads different from normal application workloads?

Agentic workloads break four assumptions web infrastructure was built on: a single run lasts minutes to hours instead of milliseconds, the workload executes code no human reviewed, one request fans out into many parallel tool calls, and the bill is dominated by model tokens and idle wait rather than CPU time.

Long, stateful runs break request-response timeouts. A run spans many sequential model and tool calls, so default gateways cut it off: an AWS ALB idles out at 60 seconds by default, Cloudflare returns a 524 after 125 seconds, Cloud Run caps a request at 60 minutes, and a Vercel function runs 300 seconds by default, up to 800 on paid plans. A long loop needs a durable worker, not a longer timeout.

The workload runs untrusted code. Model output is arbitrary code from an untrusted source, which is why prompt injection is ranked LLM01 and excessive agency LLM06 in the OWASP Top 10 for LLM Applications 2025. Both major model vendors ship their code tools inside a sandbox for that reason: Anthropic's code execution tool runs in a "secure, sandboxed environment" and OpenAI's Code Interpreter in a "fully sandboxed virtual machine".

Bursty fan-out turns start latency into user-visible latency. One request can spawn many parallel sub-agents, so sandbox start time stops being backend trivia. When the isolation is Firecracker, which targets a sub-125 ms boot, fan-out stays cheap; when it is a fresh image pull, it does not.

The cost shape is inverted, and the data has gravity. Most wall-clock time in a run is spent waiting on a model, so you pay for idle compute while the CPU does nothing, which is why scale-to-zero and hard timeouts beat CPU efficiency here. Agents also need durable state and a full trace of every tool call, and they read internal Postgres, tickets and documents. Pulling that data out to a vendor runtime costs about $0.09 per GB on AWS and a similar per-GB rate on Google Cloud, so the gravity pulls the agent toward the data.

Which platform should you use for each type of agentic workload?

Match the workload to its category before you compare vendors: E2B or Daytona when the agent runs code it wrote, Modal when you need on-demand GPUs or serverless batch, Trigger.dev, LangGraph Platform or Temporal when runs must survive a crash and resume, and Northflank, Fly.io, TrueFoundry or Qovery when you are hosting the service, workers and database that stay up between runs. Each platform below gets a real best fit and a real limitation.

Prices and limits verified against vendor docs in early 2026; billing rates are list prices and change often.

The rule that follows is short. Executes untrusted code: add a sandbox vendor (E2B or Daytona). Needs on-demand GPUs: use Modal or a hosted inference provider. Data cannot leave your account: host on BYOC or your own Kubernetes (Qovery, Northflank or TrueFoundry).

Do you need an agent sandbox platform, or is a normal container enough?

You need a purpose-built agent sandbox only when the agent executes code, shell commands or files it generated itself. If the agent only calls APIs, models and databases, a normal container in your own cluster is safer to reason about, cheaper per run, and far easier to connect to internal data behind your VPC.

Isolation runs on a spectrum, weakest to strongest: a shared process, an OCI container, a hardened runtime like gVisor or Kata Containers, a microVM in the Firecracker class, and a dedicated VM or node pool. Firecracker boots a microVM in under 125 ms with under 5 MiB of overhead per VM and creates up to 150 microVMs per second per host, the properties its NSDI '20 paper was built to deliver, which is why sandbox vendors sit at the microVM level rather than reusing a container.

E2B and Daytona are that layer as a product: disposable per-run sandboxes with filesystem and network controls, SDK-first, per-second billing at roughly $0.05 per vCPU-hour. E2B defaults a sandbox to a 5-minute timeout and allows sessions up to 24 hours on Pro; Daytona pauses a sandbox to capture full machine state and resumes in under a second. Both are built to be destroyed after each run, which is the point. What they are not is the home for your agent's API, queue, Postgres or vector store, and per-sandbox billing gets expensive if you keep them warm between runs.

The guardrails matter as much as the isolation, on every platform: egress allowlists, no ambient cloud credentials inside the sandbox, short-lived scoped tokens, hard wall-clock and token budgets per run, and a full audit log of every tool call. That credential rule is not theoretical. Datadog's 2024 report found 46% of organizations still use unmanaged users with long-lived credentials, and Orca's 2024 report found 82% of Amazon SageMaker users had at least one notebook exposed to the internet. A sandbox holding your production key inherits every one of those problems.

So: untrusted generated code plus internet access means a microVM sandbox; trusted first-party code plus internal data access means a container in your own cluster; both together means you run the sandbox vendor alongside your own hosting layer, not instead of it. For the network-level detail on keeping agents inside your perimeter, I wrote that up in Governance Starts at the Network.

Can you run agentic workloads inside your own cloud account or Kubernetes cluster?

Yes. Qovery, Northflank and TrueFoundry all deploy agent workloads into your own AWS, GCP, Azure or Scaleway account or an existing Kubernetes cluster, and for any agent touching regulated or customer data that bring-your-own-cloud model is usually the only viable option, not an enterprise upsell. BYOC means the vendor runs the control plane while your workloads and data run in an account or cluster you already operate.

It matters more for agents than for normal apps. Prompts and tool outputs routinely carry PII and secrets, agents need private connectivity to internal databases, and moving internal data out to a vendor runtime costs about $0.09 per GB on AWS every time. The regulatory clock is running too: under the EU AI Act, GPAI model obligations applied from 2 August 2025, and after the 2026 Digital Omnibus revision the high-risk obligations now land 2 December 2027 for stand-alone systems and 2 August 2028 for AI embedded in regulated products. Kubernetes is where this runs: CNCF's 2025 survey reported 82% of container users run Kubernetes in production and 66% of organizations hosting generative AI use it for inference.

Support verified against vendor docs in early 2026; self-host options can change with tier and version.

Where Qovery differs is that it is cloud-agnostic and Kubernetes-native across AWS, GCP, Azure, Scaleway and an existing or self-managed cluster equally, the cloud bill and any Savings Plans or committed-use discounts stay in your name, and it absorbs the boring operational layer: git-push deploys, a preview environment per pull request, environment auto-stop, per-environment RBAC, managed cluster upgrades, and databases backed by managed cloud services. Northflank and TrueFoundry also do real BYOC, so the honest framing is that Qovery is the app and data layer of a stack that composes with E2B, Modal and LangGraph, not a replacement for them. The trade-off is worth stating plainly: running in your own account means you own the blast radius, the upgrade cadence and the on-call rotation, and that is exactly the work a platform layer with managed upgrades and self-service environments exists to absorb.

How do you handle long-running agent runs, retries, and state?

Long agent runs need durable execution, meaning checkpointed state, resumable steps and idempotent retries, not a longer HTTP timeout. A 40-minute run that dies at minute 38 has to resume from its last checkpoint, or you pay for every token twice and risk firing every non-idempotent side effect twice.

The token math is not small. If a run has already consumed two million input tokens and 200,000 output tokens, replaying it from zero at a representative frontier rate of roughly $3 per million input and $15 per million output (check current Anthropic and OpenAI pricing) is about $9 spent again for the same result. Worse than the money is the side-effect risk: a naive retry of a run that already sent an email, wrote a row or issued a refund fires each of those a second time.

Limits verified against vendor docs in early 2026.

Run each layer where it belongs: orchestration state and checkpoints close to your data, stateless tool execution in disposable sandboxes, and long-lived workers as normal deployments that scale to zero when the queue drains. The guardrails are the same every time: a max-steps ceiling per run, a per-run cost ceiling, a dead-letter queue for poisoned runs, an idempotency key on every tool call, and structured traces for the post-mortem. This layer stacks with the hosting layer rather than competing with it, so you can run self-hosted LangGraph or Trigger.dev workers as services inside your own cluster through Qovery and get durable execution and runtime ownership at once.

What do agentic workloads actually cost, and how do you keep the bill down?

For most production agents, model tokens plus idle compute spent waiting on model responses dominate the bill, so the biggest savings come from prompt caching, scale-to-zero workers, non-production auto-stop, spot capacity for retryable work and hard per-run budgets, not from picking a cheaper CPU class. The bill breaks into four lines: model tokens, sandbox and compute seconds, egress plus storage, and the platform and human cost of operating it.

Per-second billing is now the norm, which makes the compute line smaller than teams expect. Modal charges per second, with an H100 at $3.95/hour and an 80 GB A100 at $2.50/hour; E2B and Daytona bill sandboxes at about $0.05 per vCPU-hour; Fly.io charges nothing for CPU and RAM while a Machine is stopped or suspended; and Northflank prices compute at $0.01667 per vCPU-hour with no added cost to run in your own VPC. For comparison, an AWS Capacity Block reserves an 8-H100 p5 instance at about $5.19 per H100-hour, so Modal's serverless H100 is competitive when GPU use is bursty rather than constant.

The single largest per-run saving for agents with big system prompts is prompt caching. Anthropic prices cache reads at one-tenth of the base input rate, a 90% discount, with cache writes at 1.25x base, and OpenAI discounts cached input tokens by up to 90%. An agent re-sending the same 30,000-token system prompt on every step pays roughly a tenth of that after the first call. BYOC then keeps discounts you already negotiated, since AWS Savings Plans cut on-demand by up to 72% and Google committed-use discounts reach up to 70% on memory-optimized machines, while retryable batch runs on AWS Spot at up to 90% off or GCP Spot at 60% to 91% off.

Savings figures are vendor list claims verified in early 2026; realized savings depend on workload shape.

Two traps generate almost every surprise bill: warm sandboxes nobody destroyed, fixed by a hard session timeout and tearing them down after each run, and unbounded agent loops with no ceiling, fixed by a max-steps count and a per-run token budget that hard-stops the run.

How should you choose an agent infrastructure platform? A 5-question checklist

Answer five questions in order and the platform choice falls out, which for most teams is a two or three layer stack rather than a single vendor.

1. Does the agent execute model-generated code? If yes, add E2B or Daytona.
2. Do you need on-demand GPUs? If yes, use Modal or a hosted inference provider like Amazon Bedrock, Google Vertex AI, Fireworks or Together.
3. Do runs exceed a few minutes and need to resume mid-flight? If yes, add durable orchestration with Trigger.dev, LangGraph Platform or Temporal.
4. Can prompts and tool outputs leave your cloud account? If no, use BYOC or your own Kubernetes with Qovery, Northflank or TrueFoundry.
5. Who operates it at 3am? With no platform team, weight managed cluster upgrades, self-service environments and per-environment RBAC heavily.

Two examples show how the answers combine. A seed-stage team shipping a code-executing coding agent runs E2B for the sandbox, a hosted model API for inference, Trigger.dev for run state, and one small hosting layer for the API and Postgres; the bill is mostly tokens plus per-second sandbox seconds, likely a few hundred dollars a month at low volume. An enterprise running internal data agents under EU residency constraints runs the agent API, workers and Postgres on Qovery inside its own EU-region cloud account, LangGraph Platform self-hosted in the same cluster for durable runs, and a hosted model endpoint in-region; the bill is its existing cloud rate with Savings Plans applied plus the platform layer, and the data never leaves the VPC.

What are the top infrastructure platforms for running agentic workloads in 2026?

The top infrastructure platforms for agentic workloads in 2026 are E2B and Daytona for sandboxed execution of model-generated code, Modal for on-demand GPUs and serverless batch, Trigger.dev, LangGraph Platform and Temporal for durable orchestration of long runs, and Northflank, Fly.io, TrueFoundry and Qovery for hosting the agent application and its data layer. No single vendor covers all four jobs well, so most production stacks combine two or three. The deciding factors are whether the agent runs its own code, how long a run lasts, and whether prompts may leave your cloud account.

What is the difference between an agent sandbox platform and an application hosting platform?

An agent sandbox platform like E2B or Daytona runs untrusted, model-generated code in a disposable, kernel-isolated microVM that is destroyed after each run. An AI agent hosting platform like Qovery, Northflank or Fly.io runs the always-on parts of the agent: the API, background workers, the queue, Postgres and the vector store. They solve different problems, so a code-executing agent usually needs both, with the hosted service calling the sandbox rather than one replacing the other.

Can I run AI agents inside my own AWS, GCP, Azure, or Scaleway account?

Yes. Qovery deploys AI agents and their databases into your own AWS, GCP, Azure or Scaleway account or an existing Kubernetes cluster, so the workload, the data and the bill stay in your name. Northflank and TrueFoundry also offer bring-your-own-cloud, Northflank across seven providers and TrueFoundry into your own EKS, GKE, AKS or self-managed cluster. Modal, E2B, Daytona and Fly.io run on their own infrastructure, so choose a BYOC platform when the account must stay yours.

Do I need Kubernetes to run agentic workloads in production?

You do not strictly need Kubernetes, but it is where most production agent infrastructure runs: CNCF's 2025 survey found 82% of container users run Kubernetes in production and 66% of organizations hosting generative AI use it for inference. You can run agents on serverless platforms like Modal or Fly.io without touching Kubernetes directly. If you want the workload in your own cloud account with data residency, a platform like Qovery runs it on managed Kubernetes for you, so you get the substrate without operating the control plane yourself.

How much does it cost to run an AI agent in production?

The cost of running an AI agent in production is dominated by model tokens and idle compute spent waiting on model responses, not by CPU class. Compute is now per-second across the category, with sandboxes around $0.05 per vCPU-hour and a serverless H100 near $3.95/hour on Modal, so the token bill usually dwarfs it. The largest single lever is prompt caching, which cuts cached input tokens by up to 90% on both Anthropic and OpenAI, followed by scale-to-zero workers, non-production auto-stop, spot capacity for batch, and hard per-run token ceilings.

Is E2B, Modal, or Northflank better for agentic workloads?

They win at different jobs, so the answer depends on the workload. E2B is better when the agent executes model-generated code, because it gives you disposable microVM sandboxes with sub-second starts. Modal is better when you need on-demand GPUs or serverless batch, with per-second billing and roughly one-second container boots. Northflank is better for hosting the agent application and its data layer, including inside your own cloud account through BYOC. Many production stacks use all three: E2B for the sandbox, Modal for GPU inference, and Northflank or Qovery for the app and data layer.