You’ve deployed your model on RunPod and inference is working. Then your agent needs to execute the code it generates. It parses documents, runs scripts, and tests changes. RunPod’s GPU infrastructure wasn’t built for this.
AI agents operate on two layers. The inference layer runs on GPUs and handles LLM calls. The execution layer runs on CPUs and handles everything else: file operations, code execution, tool calls, and browser automation. RunPod excels at the first part. This guide covers platforms purpose-built for the second.
Seven platforms now compete for the CPU execution layer. The market shifted in 2025 as Vercel and Cloudflare both launched beta sandbox products. Fly.io entered with Sprites.dev in January 2026. This guide compares isolation technology, resume times, state persistence, and cost structures across sandbox platforms for agent code execution.
Why AI agents need separate sandbox infrastructure
RunPod provides GPU cloud infrastructure for model training and inference. It offers on-demand and spot GPU instances, serverless GPU endpoints, and container-based deployments. RunPod is widely used to serve LLMs, including open‑source and fine‑tuned models, on NVIDIA‑based GPU instances.
The gap appears when agents need to act on their outputs. A coding agent generates Python and needs to run it. A research agent clones a repository and searches through files. A data analysis agent writes SQL queries and executes them against a database. These tasks don’t need GPUs. They need CPU-based compute environments with security isolation, fast startup, and state persistence.
GPU platforms introduce three constraints for code execution workloads:
- Cold starts range from seconds to minutes depending on container size.
- Container-based isolation creates security risks when running untrusted AI-generated code.
- Idle compute billing charges for GPU time during I/O-bound operations like waiting for API responses.
CPU sandbox platforms solve these problems with purpose-built architecture. They provide isolated environments that boot in milliseconds, shut down automatically when idle, and maintain state between sessions. The result is a two-layer stack: RunPod for inference, a sandbox platform for execution.
Essential features to look for in CPU sandbox platforms
Isolation technology and state persistence matter more than headline boot times. A platform’s pause and resume behavior determines what agent architectures you can build.
These criteria separate production-ready platforms from more basic prototyping tools:
- Resume and boot time: The time from standby to active determines whether real-time agent interactions feel responsive. Voice‑like agents can feel laggy above around 800ms end‑to‑end latency. Coding assistants feel broken above 300ms infrastructure delay.
- State persistence duration: How long the sandbox retains memory, files, and running processes between invocations. Persistent sandboxes avoid reloading datasets and reinstalling dependencies on every request.
- Isolation technology: MicroVMs run dedicated kernels with hardware-enforced boundaries. Containers share the host kernel, creating potential container escape vulnerabilities. Teams running untrusted code from external users should look for microVM isolation instead.
- Auto-shutdown and pricing model: Platforms with aggressive auto-shutdown and per-second billing minimize idle costs. Some platforms enforce minimum billing periods of 10 to 15 minutes, which means paying for compute that isn’t doing work.
- Compliance certifications: SOC 2 Type II and HIPAA compliance determine whether the platform works for enterprise deployments handling regulated data.
The following platforms represent the current competitive landscape for CPU-focused agent sandboxes, ordered by production readiness and feature completeness.
Top RunPod alternatives for CPU sandbox platforms
1. Blaxel
Blaxel is a stateful sandbox platform for AI agents that execute code in production. The platform uses Firecracker microVMs with sub-25ms resume times from standby. Sandboxes persist in standby indefinitely with zero compute cost, resuming with complete filesystem and memory state preserved.
The key architectural difference is perpetual standby. Competitors delete sandboxes after 30 days (E2B) or archive them with slow restoration (Daytona). Blaxel keeps sandboxes dormant forever. A coding agent’s stateful sandbox with a cloned repository stays ready for the next pull request without recloning from scratch.
Key features
- Sub-25ms resume from perpetual standby with complete filesystem and memory state preserved
- Firecracker-based microVM isolation providing hardware-enforced tenant separation
- Integrated agent stack including co-located agent hosting, MCP server hosting, batch jobs, and model gateway
- Preview URLs with custom domain support for real-time code rendering
- SOC 2 Type II certified with HIPAA compliance and Business Associate Agreements available
- Multi-language SDK support for Python, TypeScript, and Go
- OpenTelemetry observability with traces, logs, and metrics included at no cost
Pros
- Network-based auto-shutdown after approximately 15 seconds of inactivity reduces idle charges
- Agent co-hosting eliminates network roundtrip latency between agent logic and sandbox execution
- Framework-agnostic hosting works with LangChain, CrewAI, Vercel AI SDK, or custom code
- Can scale to 100,000+ sandboxes created and 50,000+ concurrently running
Cons
- CPU-focused infrastructure doesn’t support GPU workloads for inference or training
- Supports only Python, TypeScript, and Go without Ruby, Java, or Rust support
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 current rates
- Available add-ons: Email support, live Slack support, HIPAA compliance
Who is Blaxel best for?
Blaxel fits AI-first companies building AI agents such as code generation agents, PR review agents, and data analysis agents. The perpetual standby architecture works especially well for coding assistants where repositories need to stay cloned and ready between sessions. Teams needing GPU workloads should pair Blaxel with a GPU platform like RunPod for the inference layer.
2. E2B
E2B is a sandbox platform for AI agents built on Firecracker microVMs. Each sandbox gets its own Linux kernel with hardware-level KVM isolation. Paused sandboxes persist for up to 30 days before deletion. Python and JavaScript SDKs are actively maintained.
Key features
- Firecracker microVM isolation with approximately 50,000 lines of Rust versus QEMU’s 1.4 million lines of C
- 150 to 200ms cold-start boot from snapshot restore
- Full memory and filesystem state preserved on pause
- BYOC deployment on AWS with GCP and Azure in development
Pros
- Open-source SDK with active Python and JavaScript libraries
- Strong enterprise traction with Fortune 100 adoption
- SOC 2 certified with established security track record
Cons
- Paused sandboxes are deleted after 30 days, requiring full environment recreation
- No agent co-hosting capability, so agent-to-sandbox communication adds network latency
- No HIPAA compliance documented
Pricing
- Hobby (free): One-time $100 of usage in credits, community support, up to 1-hour sessions, and up to 20 concurrent sandboxes
- Pro ($150/month): Up to 24-hour sessions, up to 100 concurrent sandboxes, and customizable compute resources
- Enterprise: Custom pricing for BYOC (Bring Your Own Cloud) and self-hosted deployment options
- Usage-based pricing: See E2B’s pricing page for current rates
- Example: 1 vCPU + 2 GB RAM (equivalent to Blaxel XS sandbox, as of February 2026): $0.0828/hour ($0.000014 CPU + 2 × $0.0000045 memory per second)
Who is E2B best for?
E2B works well for developer-focused AI products and prototyping environments where 30-day sandbox retention is acceptable. Teams that value open-source SDKs and don’t need perpetual state persistence will find E2B’s developer experience strong.
3. Daytona
Daytona pivoted from cloud development environments to agent infrastructure in February 2025. The platform uses Docker containers by default, with optional Kata Containers or Sysbox for enhanced isolation. Daytona’s headline number is sub-90ms sandbox creation from a warm pool of pre-created environments.
The most important distinction: Daytona uses containers, not microVMs. Any OCI-compliant image works out of the box, and Docker-in-Docker is supported. However, containers share the host kernel, creating a different security boundary than hardware-isolated microVMs.
Key features
- Sub-90ms sandbox creation from warm pool with 150 to 200ms realistic cold starts
- Auto-pause after 15 minutes of inactivity by default, configurable to 1-minute minimum
- Four SDKs available: Python, TypeScript, Ruby, and Go
- Multi-region deployment across US, EU, and India
Pros
- SOC 2 Type II and HIPAA certified as of October 2025
- Any OCI-compliant Docker image works without modification
- Ruby SDK availability for teams outside the Python and TypeScript ecosystem
Cons
- Container-based isolation shares the host kernel, creating potential escape vulnerabilities compared to microVMs
- 15-minute default auto-pause means paying for idle compute between interactions
- Sandboxes auto-archive after 7 days of inactivity with slower restoration from object storage
- No agent co-hosting, managed custom domains, or dedicated outbound IPs
Pricing
- Usage-based pricing: See Daytona’s pricing page for current rates
Who is Daytona best for?
Daytona fits teams that prioritize container compatibility and need broad language support including Ruby. It works well when agents run trusted code. The security trade-off of container isolation should be acceptable for the workload.
4. Together AI (formerly CodeSandbox)
Together AI acquired CodeSandbox in December 2024, turning a browser-based development IDE into AI infrastructure. Together AI needed an execution layer for AI-generated code. CodeSandbox’s Firecracker implementation and memory snapshotting technology provided that.
Resume from hibernation takes approximately 511ms at the P95 level. Cold starts from scratch run about 2.7 seconds at P95. These latency figures come from Together AI’s public benchmarks for Code Sandbox and may vary by region, VM size, and workload.
Key features
- Firecracker microVMs with custom kernel patches and memory snapshotting
- Live VM cloning completes in 1 to 3 seconds for branching workflows
- VM sizes scale from 2 to 64 vCPUs and 1 to 128 GB RAM, hot-swappable during operation
- SOC 2 Type II certified
Pros
- Richest snapshot capabilities with live VM cloning and copy-on-write
- Backed by Together AI’s $1.25B valuation and $288M in funding
- Established user base handling 2 million weekly VM starts
Cons
- 2.7-second cold starts are the slowest in this comparison
- TypeScript and JavaScript SDK only. No Python SDK from CodeSandbox itself
- Platform migration to Together AI creates transition uncertainty
- Auto-hibernation timeouts of 2 to 7 days at random
Pricing
- Build (free): 5 members, 40 hours of monthly VM credits, private sandboxes
- Scale ($170/month): Up to 20 members, 160 hours of monthly VM credits plus access to on-demand VM credits priced hourly, 250 concurrent VMs
- Enterprise: Custom pricing for unlimited members and bespoke concurrent VMs
- Usage-based pricing: See CodeSandbox’s pricing page for current rates
Who is CodeSandbox best for?
CodeSandbox fits teams that need VM cloning for branching agent workflows and are comfortable with TypeScript SDKs. The Together AI backing provides financial stability, but the ongoing platform migration is worth monitoring before committing to production workloads.
5. Fly.io
Fly.io provides infrastructure-grade Firecracker microVMs with a REST API but no purpose-built sandbox abstractions. Teams gain 30+ deployment regions and low cost at scale. The trade-off is building your own SDK, orchestration layer, and pool management.
Starting a pre-created Machine takes 20 to 50ms in the same region. Creating a new Machine from scratch takes double-digit seconds due to image pulls. The recommended pattern is pre-creating Machines and starting them on demand.
Launched January 2026, Sprites.dev provides purpose-built sandbox environments with checkpoint and restore at approximately 300ms. It includes WebSocket-based command execution and SDKs in TypeScript, Go, Python, and Elixir. Sprites is a Firecracker‑backed VM service, not a container‑runtime API. To use Docker images, they must be converted into Fly Machines or equivalent VM images.
Key features
- Firecracker microVMs with 30+ global deployment regions
- 20 to 50ms start for pre-created Machines in the same region
- Fly Volumes provide persistent NVMe storage attached to Machines
- Sprites.dev adds sandbox-specific abstractions with checkpoint and restore
Pros
- Maximum infrastructure flexibility with 30+ regions and low-level Machines API
- SOC 2 compliant with HIPAA-compliant workload support at $99/month add-on
- Pay-as-you-go with per-second billing and competitive pricing at scale
Cons
- No purpose-built sandbox SDK for the core Machines API, so you must build your own abstractions
- No memory checkpoint or restore on raw Machines, so stopping a Machine loses all in-memory state
- Sprites.dev is very new and CPU-only with no Docker or OCI support
- No agent co-hosting or integrated observability for agentic workloads
Pricing
- Usage-based pricing: See Fly.io’s pricing page for current rates
Who is Fly.io best for?
Fly.io fits teams with infrastructure engineering expertise who want maximum control over their sandbox architecture. It’s the right choice when you have the engineering capacity to build custom orchestration and need global deployment reach that purpose-built sandbox platforms don’t yet offer.
6. Vercel Sandbox
Vercel Sandbox launched in beta at Ship 2025 in June 2025 as part of Vercel’s AI Cloud platform. It runs on Firecracker microVMs with up to 8 vCPUs and 16 GB RAM. Maximum runtime caps at five hours on Pro and Enterprise plans, and 45 minutes on Hobby.
Vercel has since announced that Sandboxes are now generally available, but the product is still relatively new and evolving quickly, so teams should treat it as a maturing, production‑ready primitive rather than a fully static feature set.
Key features
- Firecracker microVMs on Amazon Linux 2023
- Filesystem snapshotting preserves dependencies between runs
- Active CPU billing charges only when code executes, not during idle or wait time
- TypeScript and Python SDKs with coding-agent templates
Pros
- Active CPU pricing model can be significantly cheaper for bursty workloads
- Tight integration with Vercel’s deployment ecosystem
- Coding-agent templates supporting Claude Code and OpenAI Codex CLI
Cons
- 5-hour maximum lifetime is a hard constraint for long-running or even stateful agent workloads
- While now generally available, the API and behavior are still being refined and may change in the near term
- No perpetual standby, so sessions don’t persist indefinitely
Pricing
- Usage-based pricing: See Vercel’s pricing page for current rates
Who is Vercel Sandbox best for?
Vercel Sandbox fits teams already invested in the Vercel ecosystem who need basic sandbox capabilities for agent workloads under five hours. It’s an add-on feature, not standalone infrastructure.
7. Cloudflare Containers (for Workers)
Cloudflare Sandboxes launched in beta on June 25, 2025. The platform uses Docker containers orchestrated through Durable Objects and managed from Workers. However, it’s still in beta with cold starts running two to three seconds. The default sleep timeout is 10 minutes, and all state is lost when a sandbox sleeps.
Key features
- Container-based isolation with edge deployment on Cloudflare’s network
- Active CPU pricing charges only for compute, not provisioned time
- Deep integration with Workers, R2, D1, and Workers AI
Pros
- Strong fit for teams already using Cloudflare’s ecosystem
- Edge deployment provides geographic distribution
Cons
- Container-based isolation shares the host kernel
- All state lost when a sandbox sleeps, so persistent data requires mounting R2 or S3-compatible storage
- Workers orchestration layer adds complexity versus standalone SDKs
Who is Cloudflare Sandboxes best for?
Cloudflare Sandboxes fit teams already deep in the Cloudflare ecosystem who need basic sandbox capabilities without adding another vendor. The beta status and container-based isolation make it less suitable for production workloads running untrusted code.
8. Runloop
Runloop is a sandbox platform focused on AI software engineering workflows. Devboxes use microVM isolation and can start a 10GB image in under two seconds. The platform differentiates through built-in benchmarking to offer on-demand SWE-Bench Verified runs and custom benchmark suites across thousands of parallel environments.
Key features
- MicroVM isolation for AI coding agent workloads
- Support for high‑throughput infrastructure that can scale to tens of thousands of concurrent sandboxes
- Deploy to VPC on AWS and GCP for enterprise requirements
- SOC 2 certified
Pros
- Built-in benchmarking with SWE-Bench and custom suites for coding agent evaluation
- MCP server integration for agent tool discovery
- Deploy to VPC provides data residency control
Cons
- Narrowly focused on coding agents, which limits flexibility for other agent types
- No perpetual standby or sub-25ms resume capability
- No agent co-hosting for latency elimination between agent and sandbox
Pricing
- Free: $50 in usage credits for testing
- Pro ($250/month): Suspend/resume with automatic idle detection, repo connections, custom benchmarks
- Enterprise: Custom pricing for VPC deployment and reinforcement fine-tuning (RFT) for feedback-driven improvements
- Usage-based pricing: See Runloop’s pricing page for current rates
Who is Runloop best for?
Runloop fits teams that prioritize benchmarking workflows and need VPC deployment for data residency control. Teams building broader agent types or needing perpetual state persistence should look at other options.
Find the right execution layer for your agent stack
AI agent infrastructure has split into two layers. RunPod and other GPU platforms handle inference. Meanwhile, CPU sandbox platforms handle code execution. Choosing the right execution layer comes down to your isolation requirements, state persistence needs, and latency tolerance.
Perpetual sandbox platforms like Blaxel address the execution layer with sub-25ms resume from standby, microVM isolation, and infinite state persistence at zero compute cost during idle periods. The integrated agent stack eliminates network latency between agent logic and sandbox execution through co-hosting, while SOC 2 Type II, ISO 27001, and HIPAA compliance handle enterprise security requirements.
Sign up for free with $200 in credits to test sandbox resume performance against your latency requirements, or book a demo to discuss how Blaxel’s perpetual sandbox architecture fits your agent stack.
FAQs about CPU sandbox platforms for AI agents
Can I use RunPod and a sandbox platform together?
Yes. RunPod handles the inference layer where your LLM runs. A sandbox platform handles the execution layer where your agent acts on LLM outputs. The agent sends prompts to RunPod for inference, receives generated code or instructions, then executes them inside an isolated sandbox.
This two-layer architecture separates GPU costs from CPU costs and lets you optimize each independently.
What’s the difference between GPU compute and CPU sandboxes for AI agents?
GPU platforms like RunPod run the LLM itself. They process tokens, generate text, and handle model inference. CPU sandboxes run the code that agents generate or the tools agents call. File operations, script execution, browser automation, and database queries all run on CPUs.
Most agent workloads are I/O-bound and CPU-bound, not GPU-bound. Using a GPU platform for code execution wastes expensive GPU resources on tasks that don’t need them.
What security isolation technologies should teams evaluate for executing AI-generated code?
MicroVM technologies like Firecracker provide hardware-enforced isolation with a dedicated kernel per sandbox. This prevents container escape exploits from reaching the host system. Container-based platforms share the host kernel, which creates a different security boundary.
Teams running untrusted code from external users or handling regulated data under HIPAA should strongly consider microVM isolation instead.
When should teams add a CPU sandbox platform to complement RunPod?
Consider adding a sandbox platform when your agents need to execute generated code, run command-line tools, parse documents, or automate browsers. Signs you need a dedicated execution layer include:
- Agents waiting on multi-second cold starts for code execution
- Paying for GPU time during CPU-bound tasks
- Security concerns about running untrusted code alongside your model serving infrastructure
Allow one to four weeks for proof-of-concept evaluation before committing to a platform.
