Best Koyeb alternatives for AI agent sandboxes in 2026

Your coding agent has spent 20 hours working through a repository migration in a Koyeb sandbox. At hour 24, the platform deletes it: Koyeb sandboxes auto-delete a maximum of 24 hours after creation, or 12 hours after scaling to zero.

There's a second reason teams are re-evaluating. Mistral AI announced an agreement to acquire Koyeb on February 17, 2026, and the platform still runs while transitioning into Mistral Compute. The Starter plan is being removed. This guide compares five alternatives on isolation model, standby and resume behavior, state persistence, and pricing. Match the execution layer to how your agents actually run.

TL;DR:

• Koyeb's limit: Sandboxes auto-delete after 24 hours max, and the platform is transitioning into Mistral Compute with plan changes underway.
• Blaxel for production agents: Perpetual standby with sub-25ms resume, zero idle compute, and no session time limit fits coding and PR review agents.
• Fly.io for DIY orchestration: Raw Firecracker microVMs across global regions suit teams that build their own lifecycle and state management.
• Modal for GPU inference: Python-native serverless with H100 GPUs fits agents whose hot path includes model training or inference.
• E2B and Daytona for prototyping: E2B offers open-source self-hosting with Firecracker isolation. Daytona covers five SDK languages and desktop automation.

Why teams look beyond Koyeb for agent sandboxes

Koyeb is a strong platform for globally distributed web services, with edge routing, scale-to-zero between traffic bursts, and per-second GPU inference pricing.

Agent code execution stresses different parts of the stack. Three limits show up fast:

• Ephemeral local state: Instances reschedule at least weekly, so teams write their own checkpointing.
• Preview-quality volumes: Persistent Volumes are capped at 10 GB and run only in Washington D.C. and Frankfurt. Koyeb calls them "currently only suitable for testing."
• Resume latency: The default Deep Sleep mode wakes in 1-5 seconds. The ~200 ms Light Sleep mode is still in public preview.

Each of those gaps becomes application code your team writes and maintains: checkpoint writers, restore paths, and warm-up logic around every resume.

The best Koyeb alternatives for AI agent sandboxes

Competitor rates are normalized to 1 vCPU and 2 GB RAM. Figures come from each vendor's official pricing page, as of August 2026.

Blaxel

Blaxel is the infrastructure foundation for autonomous agents: the execution layer where agent code runs in production. Its sandboxes are Firecracker microVMs that hold complete filesystem and memory state in standby indefinitely. Blaxel is a first-class sandbox provider in the OpenAI Agents SDK. The platform also includes Batch Jobs, Agent Drive, Volumes, and a Model Gateway. Its certifications include SOC 2 Type II, ISO 27001, and HIPAA. Hardware-enforced boundaries are designed to prevent container escapes.

Key features

• Perpetual standby: Sandboxes wait indefinitely with no compute charge while idle.
• Snapshot resume: Standby snapshots preserve files, memory, and running processes.
• Network-based auto-shutdown: Sandboxes return to standby on their own after approximately 15 seconds of inactivity, with no lifecycle code to write.
• Preview URLs: Any HTTP process in a sandbox is reachable through a generated URL, with managed custom domains.
• Agent Drive (private preview): A distributed filesystem in us-was-1, mountable to multiple sandboxes at once. Use it to share context and artifacts across sessions.
• Compliance: SOC 2 Type II, ISO 27001, and HIPAA.

Pros

• Purpose-built for agent code execution rather than adapted from web hosting or a general-purpose PaaS.
• Firecracker microVMs give each sandbox its own kernel. A kernel-level exploit stays inside that microVM. Hardware-enforced boundaries are designed to prevent container escapes.
• Standby is indefinite, so agent sessions resume days later with full memory state.

Cons

• First-class SDKs ship for Python, TypeScript, and Go. Ruby, Java, and Rust teams integrate over the REST API instead of a native client.

Pricing

• Free: Up to $200 in free credits plus usage costs
• Pre-configured sandbox tiers and usage-based pricing: See Blaxel's pricing page for the most up-to-date pricing information
• Available add-ons: Email support, live Slack support, HIPAA compliance

Who is Blaxel best for?

Coding agents are the primary use case; PR review and data analysis agents follow the same sporadic, stateful pattern. Teams needing multi-day continuity without building external checkpointing get the most from the platform.

Daytona

Daytona is an AI agent sandbox platform that pivoted from developer environments to AI agent sandboxes in January 2024. A $24 million Series A followed in February 2026. It offers sandbox snapshots such as daytona-small, daytona-medium, and daytona-large, plus ephemeral GPU sandboxes via the pre-built daytona-gpu snapshot and Windows sandboxes running in isolated VMs. Only the VM class supports pause, resume, fork, and hot snapshots.

Key features

• Three isolation classes: Containers (default), Linux/Windows VMs with dedicated kernels, and GPU sandboxes up to 192 GB RAM.
• Computer Use: Programmatic desktop control (mouse, keyboard, screenshots) for Linux and Windows.
• Preview URLs: Any HTTP process exposed through a generated URL.

Pros

• Five-language SDK coverage across Python, TypeScript, Ruby, Go, and Java.
• Product Hunt lists Daytona at 5.0/5 across 10 reviews; a related Daytona Cloud forum page describes its "sub-90ms startup times" and "stateful execution."
• $200 in free compute for new users, and up to $50,000 in startup credits.

Two caveats stand out. The production codebase went closed source in June 2026, and the public repository is unmaintained. CLI snapshot creation hangs and never returns, reproduced across versions v0.157.0-0.166.0 (issue #4313).

Cons

• The production codebase is closed source, and the public repository is unmaintained.
• CLI snapshot creation can hang without returning.

Pricing

• vCPU at $0.0504/hour and memory at $0.0162/GiB/hour, about $0.083/hour at 1 vCPU and 2 GB.
• Quotas scale with spend: Tier 1 caps at 10 vCPU, Tier 3 (after a $500 top-up) at 250 vCPU.

Who is Daytona best for?

Daytona fits teams that want one vendor across five language ecosystems, plus desktop automation through Computer Use. Keep in mind the default class is a microVM-based sandbox, not a shared-kernel container, so untrusted AI-generated code needs the VM class.

Fly.io

Fly.io is a platform that runs workloads as Firecracker microVMs across multiple regions. Fly Machines remain an important part of Fly.io's platform and are not being wound down. Sprites, a persistent "computers for agents" product, is in development but not yet generally available.

Key features

• Machine boot: Machines boot in about 300 ms; already-created machines start well under a second.
• Suspend and resume: Firecracker snapshots capture CPU registers, memory, and open file handles, resuming in a few hundred milliseconds.

Pros

• G2 reviewers rate it 4.7/5 across 3 reviews. One estimates it "saves me 3-4 hours per week compared to managing my own VPS."
• Pay-as-you-go with no plan fees, plus 40% discounts through prepaid compute reservations.
• Stopped machines cost only $0.15/GB/month for rootfs storage.

Fly's status page covers only "global incidents" and excludes routine hardware failures. Community threads report outages that never appear there. Regional capacity failures recur: ORD returned "could not reserve resource" errors for three days in March 2026.

Cons

• Suspend works only on machines with 2 GB of memory or less, and snapshots aren't guaranteed to persist.
• The status page excludes routine hardware failures, and community threads report outages that never appear there.
• Regional capacity failures recur, including three days of errors in ORD in March 2026.

Pricing

• performance-1x with 2 GB RAM: $0.0431/hour; volumes at $0.15/GB/month; snapshots at $0.08/GB/month after the first 10 GB free.

Who is Fly.io best for?

Fly.io suits teams with platform engineers who want raw, globally distributed microVMs. They build lifecycle, state, and orchestration themselves. Suspend is limited to smaller machines and snapshots aren't guaranteed. Long-lived session state needs a home outside the machine.

Modal

Modal is a Python-native serverless compute platform. Sandboxes, generally available since January 2025, reuse its GPU infrastructure. Isolation runs on gVisor, Google's userspace kernel-emulation runtime, rather than microVMs. Its sandbox architecture has been tested at 1 million concurrent sandboxes.

Key features

• Extreme concurrency: Modal demonstrated 1 million concurrent sandboxes, all created in under a minute.
• Filesystem snapshots: Configurable retention, including indefinite with ttl=None.
• Directory snapshots: Snapshot a specific directory and mount the result as an image.

Pros

• GPU inference and sandbox execution on one platform, with H100s at $0.001097/second.
• The Starter plan includes $30/month in credits at no base fee.
• Burst capacity suits fan-out agent workloads that spike into thousands of parallel tasks.

Cons

• Memory snapshots terminate the source sandbox; there is no in-place pause and resume.
• Memory snapshots expire after 7 days and "cannot currently be extended."
• Sandboxes bill at a higher rate than standard Modal compute.
• Memory snapshots cannot run with GPUs, and restore requires the exact original instance type.

Pricing

• Starter: $0/month plus compute, with $30/month in credits. Team: $250/month plus compute, with $100/month in credits.
• Modal Sandbox bills per second: $0.00003942/core/second for CPU and $0.00000672/GiB/second for memory, about $0.19/hour at 1 physical core (2 vCPU equivalent) and 2 GiB.

Who is Modal best for?

Modal fits GPU-focused ML workloads where the agent's hot path includes model inference or training. For long-lived sessions, the 7-day snapshot expiry and termination-on-snapshot push state into external storage.

E2B

E2B is a Firecracker microVM sandbox platform operated by FoundryLabs, Inc. It raised a $21 million Series A in July 2025. Its infrastructure code is open source under Apache 2.0 with a documented self-hosting path.

Key features

• Pause and resume: Filesystem and memory preserved by default; paused sandboxes are retained indefinitely until you call sandbox.kill().
• Egress firewall: Allow rules for IP addresses, address ranges, and domains.
• Bring-your-own-cloud (BYOC), Enterprise: Deploys inside your own AWS or GCP VPC.
• Self-hosting: Open-source infra repo built on Terraform, Nomad, and Consul.

Pros

• Firecracker microVMs give each sandbox a dedicated kernel for isolation.
• Paused sandboxes carry no automatic deletion window.
• Product Hunt sentiment is positive, with E2B's main Product Hunt page showing a 5.0/5 rating from three reviews.
• Self-hosting and BYOC paths serve teams that need infrastructure in their own cloud.

Continuous runtime caps at 24 hours on Pro. E2B confirmed it is "technically not possible" to have sandboxes running longer than 24 hours. Its issue tracker logs resume errors returning 404/409 after roughly 10 seconds (issue #899) and swallowed 429 Retry-After timing (issue #1325). No SOC 2 or HIPAA documentation appears on its official site.

Cons

• Continuous runtime caps at 24 hours on Pro.
• Pro costs $150/month before the first second of compute, with 100 concurrent sandboxes by default.
• Its issue tracker logs resume errors returning 404/409 after roughly 10 seconds and swallowed 429 Retry-After timing.
• No SOC 2 or HIPAA documentation appears on its official site.

Pricing

• Hobby: $0/month with $100 in one-time credits, a 1-hour runtime cap, and 20 concurrent sandboxes. Pro: $150/month with a 24-hour cap, 100 concurrent sandboxes, and paid add-ons up to 1,100.
• E2B bills per second: 1 vCPU at $0.000014/second and RAM at $0.0000045/GiB/second, about $0.083/hour at 1 vCPU and 2 GB.

Who is E2B best for?

E2B fits development-stage teams that value open-source infrastructure and self-hosting. Buyers in healthcare, legal, or finance should confirm compliance posture first, since audit documentation isn't published.

Koyeb alternatives at a glance

How to choose among Koyeb alternatives for persistent agent sandboxes

The execution layer decides whether your agent sessions survive contact with production. A sandbox that deletes itself after a day pushes state management into your application code. So does one that loses its filesystem on pause.

Match the platform to the workload. Modal fits agents whose hot path is GPU inference. If your team has platform engineers, Fly.io gives you raw global microVMs and you build orchestration yourself. Daytona covers multi-language SDK needs, and E2B works for open-source prototyping.

For production agents running long, stateful sessions, Blaxel snapshots the full sandbox and holds it in perpetual standby with no compute charge while idle. Sessions resume in under 25ms with memory and running processes intact, inside hardware-isolated Firecracker microVMs.

Network-based detection returns idle sandboxes to standby after roughly 15 seconds, with no lifecycle code on your side. Explore Sandboxes, Batch Jobs, Volumes, and Agent Drive (private preview) on Blaxel's products page.

Sign up free and deploy your first sandbox today. Or book a demo to walk through your architecture.

FAQs about Koyeb alternatives

Why do AI agent sandboxes need to run longer than 24 hours?

Production agents spend most of a session waiting on CI runs, review comments, or async events. E2B caps continuous sandbox runtime at 24 hours on the Pro plan (with longer workloads supported via pause and resume), and Modal does too. Perpetual standby holds filesystem and memory state indefinitely at no compute cost. A multi-day migration or PR review picks back up instead of being rebuilt.

Are containers secure enough for AI-generated code?

AI-generated code is untrusted code, which changes the calculus. Containers share the host kernel, so a kernel exploit in one sandbox can reach neighboring tenants. MicroVMs go further, with hardware-enforced boundaries and a separate kernel per workload. Check whether a platform's default class is a container or a microVM. Blaxel, E2B, and Fly.io run Firecracker microVMs; Koyeb uses Cloud Hypervisor microVMs.

What's the difference between perpetual standby and scale-to-zero?

Scale-to-zero stops a workload when traffic ends and restarts it on the next request. Depending on the platform, the restarted workload may begin from a clean filesystem. Perpetual standby instead snapshots the full memory and filesystem state, then restores it with running processes intact. Blaxel says sandboxes can remain in standby indefinitely with their state snapshotted, but it does not guarantee data persistence; while a sandbox is idle, it charges no compute fees, though snapshot and/or volume storage may still be billed.