So you’re running AI agents in production. That means you’ve got untrusted code running around, and that means you need platforms solving the AI agent sandboxing problem to keep things from going sideways. E2B, Daytona, Modal, and Sprites.dev all fix this problem, but they do it in different ways.
Cold starts range anywhere from 27ms to 150ms. Some use Firecracker microVMs, others go with gVisor containers. BYOC is available on some platforms for compliance work, but not all of them. GPU support matters if you’re doing ML. Session persistence decides whether your agents run for minutes or days.
This article is going to lay out the framework. You match your requirements to what each platform can do. Pick the one that fits. Let’s get into it.
What Makes E2B, Daytona, Modal, and Sprites.dev the Leading Sandbox Platforms?
E2B leads in developer experience. They’ve built Python and TypeScript SDKs that actually feel good to use. 150ms Firecracker cold starts—not the fastest out there, but fast enough for most of what you’re building. Kubernetes orchestration sitting underneath means you can scale to thousands of concurrent sandboxes without breaking a sweat. Think ChatGPT’s Code Interpreter. That’s the model E2B follows. If you’re building code execution features into your SaaS product, this is where you start looking.
Daytona achieves fastest provisioning—27-90ms from request to ready. That’s industry-leading stuff. Go here when high-frequency invocations matter and you need sub-second startup for user-facing features. They support Docker, Kata Containers, and Sysbox isolation options, so you can tune security versus performance to match your threat model.
Modal differentiates through GPUs. T4 for testing, all the way up to H200 for serious ML workloads. gVisor isolation optimised for Python ML. Serverless execution with network filesystem persistence. It’s infrastructure-as-code built specifically for machine learning teams. The catch? SDK-defined images create vendor lock-in. You’re building containers using their Python SDK, which makes migrating away harder later.
Sprites.dev is all about unlimited session persistence. Checkpoint and rollback capabilities on fast NVMe storage. This is for long-running development environments and testing scenarios, not ephemeral execution. When your agent session needs to run for hours or days while maintaining state the whole time, that’s when Sprites makes sense.
All four solve the same problem—secure code execution for untrusted AI agent code. The difference comes down to technology choices, performance characteristics, and which use cases they’re optimised for. Northflank processes over 2 million isolated workloads monthly, which shows production adoption across all these production-ready sandbox providers is real and happening now.
How Do 150ms E2B vs 27ms Daytona Cold Starts Impact Your Use Case?
Cold start time is the latency from request to a ready-to-execute environment. In user-facing applications, every 100ms affects abandonment rates. The difference between 27ms and 150ms might seem trivial until you factor in user expectations and the scale you’re running at.
Daytona’s 27-90ms is fastest in production. You need this speed for high-frequency invocations—think 1000+ requests per second in user-facing chatbots, code interpreters, or real-time analysis tools where your total latency budget is tight. When users expect instant responses, that sub-100ms cold start starts to matter a lot.
E2B’s 150ms Firecracker startup suits most production scenarios perfectly well. Code execution features, data analysis tools, moderate throughput applications where developer experience and SDK quality matter more than squeezing out every last millisecond. For most teams, 150ms is plenty fast.
Modal’s sub-second gVisor cold start works fine for batch processing, ML inference, and async workflows. When you’re already waiting 500-1000ms or more for model inference, the cold start time becomes noise in the signal. It just doesn’t matter that much.
When does 150ms versus 27ms matter? User-facing applications with under 1 second total latency budget, where every millisecond counts. Scale at 10,000+ daily invocations where the difference adds up. Synchronous execution patterns where users are sitting there waiting for results.
When doesn’t it matter? Batch jobs running in the background. Background processing where no one’s watching the clock. Long-running sessions where startup is a one-time cost amortised over hours. Applications already budgeting seconds for LLM inference where cold start is a rounding error.
Here’s the maths. User asks a chatbot to run some Python analysis. Daytona: 27ms startup + 200ms execution + 50ms network = 277ms total. E2B: 150ms startup + 200ms execution + 50ms network = 400ms total. That 123ms difference matters when your latency budget is under 1 second.
At 10,000 daily invocations, 123ms saved adds up to 20.5 minutes daily. But keep in mind, warm pools eliminate cold starts at the cost of idle capacity sitting around doing nothing.
What Isolation Technologies Power These Platforms and Why Does It Matter?
E2B and Sprites use Firecracker microVMs—the same foundation AWS Lambda runs on. Hardware-level virtualisation with kernel-level isolation. Startup time around 125ms. This gives you strong security at the cost of modest performance overhead compared to container-based approaches. But when you’re running untrusted AI agent code, that security matters.
Modal uses gVisor—Google’s user-space kernel that intercepts around 68 syscalls instead of the full 350. It’s kernel-level isolation with 2-9× performance overhead compared to native execution. You get a balance between security and speed—not as isolated as hardware virtualisation, but faster and still secure enough for most threats you’ll face.
Daytona supports multiple options—Docker with Seccomp for speed, Kata Containers with Cloud Hypervisor for maximum isolation, Sysbox for rootless containers. You pick the isolation strength that matches your threat model and performance requirements. This flexibility is Daytona’s advantage.
Northflank provides both Kata and gVisor, giving enterprises the flexibility to choose isolation technology based on workload.
Here’s the isolation hierarchy from weakest to strongest: V8 isolates like Cloudflare Workers (instant startup but WASM-only) < Docker containers (process isolation with shared kernel) < gVisor (user-space kernel) < Firecracker and Kata Containers (hardware virtualisation).
Why this matters: Prompt injection is rated OWASP #1 LLM risk with 84%+ failure rate against prompt-only defences. Trying to stop prompt injection with clever prompting doesn’t work. You need technical isolation to contain the damage—addressing the core sandboxing challenge that platforms like E2B, Daytona, Modal, and Sprites.dev are built to solve. Supply chain attacks are a real threat—19.7% of AI-generated package references point to non-existent packages that attackers can register on npm or PyPI to distribute malware. Kernel-level protection is required to prevent these attacks from compromising your infrastructure.
Firecracker’s minimalist design provides the minimum virtual devices for modern Linux: network, block device, serial console, keyboard. No USB, no graphics, no other components. Attack surface at theoretical minimum.
gVisor’s user-space kernel called Sentry intercepts system calls. It reimplements Linux syscalls in Go, simulating them in user space. This reduces the attack surface.
The trade-off? Every intercepted syscall means an expensive context switch and user-space simulation. Performance overhead for I/O-intensive apps: 2-9×.
E2B – When Should You Choose the Developer-Friendly Firecracker Platform?
E2B specialises in code interpreter functionality. They’ve built polished Python and TypeScript SDKs with excellent documentation. 150ms Firecracker cold starts provide good security. Sessions last 24 hours active, 30 days paused. This is for teams prioritising developer experience and ease of integration over extreme performance.
Kubernetes orchestration underneath enables horizontal scaling for production workloads. You can process thousands of concurrent sandboxes with automated pod management handling all the complexity for you.
BYOC deployment is available experimentally for AWS, GCP, and Azure. It addresses enterprise compliance requirements for teams that need data to stay in their own cloud accounts, though Northflank offers more mature BYOC for production deployments.
Best for: Code execution in SaaS products. Data analysis tools. ChatGPT Code Interpreter-like experiences. Teams valuing SDK quality over raw speed.
Not ideal for: High-frequency under 100ms cold starts—go Daytona. GPU-heavy ML—go Modal. Unlimited persistence—go Sprites.
Pricing at $100/month tier makes E2B mid-market compared to Modal’s higher GPU pricing.
Building a ChatGPT Code Interpreter clone? E2B. Building a real-time chatbot? Daytona.
Daytona – When Does 27ms Provisioning Justify the Platform Choice?
E2B optimises for developer experience. Daytona optimises for raw performance. That’s the difference.
Daytona achieves 27-90ms cold start. Optimised provisioning across Docker, Kata, Sysbox. Targeting high-frequency invocations.
Multi-isolation support lets you match isolation to your threat model. Docker for speed. Kata for security. Sysbox for rootless containers.
Stateful execution enables persistent workflows across invocations. Different from pure ephemeral models.
Best for: Chatbots under 1 second latency budgets. Real-time code execution. 1000+ requests per second. Synchronous workflows where every 100ms impacts abandonment.
Not ideal for: GPU workloads—Modal’s better. Long-running persistent environments—Sprites is better. Managed Kubernetes—E2B’s better.
Daytona pivoted to AI code execution in 2026, making it the youngest platform. Fast, but still maturing.
When does 123ms matter? When your latency budget is under 1s and scale exceeds 1000 req/sec.
Modal – When Do GPU Workloads and Python ML Integration Outweigh Flexibility Trade-offs?
Speed isn’t everything. For ML workloads, GPU access matters most.
Modal provides comprehensive GPU support—T4, A100, H100, H200. gVisor isolation for Python ML. Infrastructure-as-code built for machine learning.
Serverless execution with network filesystem persistence. Batch processing, training jobs, ML inference at scale. Sub-second cold starts are acceptable for async work only.
SDK-defined images create vendor lock-in. You build containers using Modal’s Python SDK. Limited migration options compared to Northflank’s OCI compatibility.
Modal’s H100 base is $3.95/hour, but with the required compute (26 vCPU, 234GB RAM, 500GB NVME), the total hits $7.25/hour. Northflank’s H100 is $2.74/hour—62% cheaper.
Best for: GPU-heavy ML. Python data science teams. Batch processing. Training jobs. ML inference. Teams prioritising integrated experience over flexibility.
Not ideal for: Multi-language—Python only. Low-latency apps—cold start too slow. Cost-sensitive GPU—Northflank 62-65% cheaper. OCI image portability.
When does the integrated UX justify a 44% premium? When your team is Python-first and living in the ML ecosystem. A data science team shipping weekly ML models may prioritise Modal’s experience over platform flexibility.
Sprites.dev – When Does Unlimited Session Persistence Outweigh Cold Start Performance?
GPU and cold start both assume ephemeral execution. Some use cases need the opposite.
Sprites.dev launched January 2026. Stateful sandboxes for AI coding agents on Firecracker microVMs. Unlimited persistence with checkpoint and rollback on fast NVMe.
Copy-on-write implementation for storage efficiency. TRIM-friendly billing—you’re charged only for written blocks. Checkpoint captures disk state in 300ms, enabling rollback.
Firecracker-based isolation delivers hardware security while maintaining state. Same tech as E2B but a different use case.
REST API plus SDKs in Go, TypeScript, Python enable cross-stack integration. Documentation is less mature than E2B’s.
Best for: Persistent development environments. Long-running agent sessions. Testing that requires state preservation. Apps where session setup cost amortises over hours or days.
Not ideal for: High-frequency ephemeral—E2B’s better. Ultra-low latency—Daytona’s better. GPU workloads—Modal or Northflank required.
Persistence economics: If session setup takes 10 seconds but runs for 8 hours, cold start becomes irrelevant. Compare Vercel’s 45-minute timeout or E2B’s 24-hour active limit.
Pricing is $0.07/CPU-hour, $0.04375/GB-hour, $0.000683/GB-hour hot storage. No GPU—you need Fly Machines for that.
When does persistence matter? When session value exceeds setup cost. Usually that’s sessions over 1 hour.
BYOC vs Managed Deployment – Which Model Fits Your Compliance Needs?
Beyond features, the deployment model affects compliance and control.
BYOC deploys the platform in your AWS, GCP, or Azure account. It handles data sovereignty, compliance—HIPAA, SOC 2, FedRAMP—and audit requirements by keeping execution in customer-controlled infrastructure.
Northflank provides production-ready BYOC with full feature parity. Kata and gVisor. GPU support. Kubernetes. Processing 2M+ monthly workloads for Writer and Sentry.
E2B offers experimental BYOC for AWS, GCP, Azure. Good for testing compliance feasibility. Northflank is recommended for production BYOC.
Managed deployment—E2B, Modal, Daytona, Sprites all offer this—provides fastest time-to-value with zero infrastructure management. For teams prioritising velocity over operational control.
Trade-offs: BYOC gives you compliance and control at the cost of operational complexity. Managed gives you simplicity at the cost of vendor dependency.
Decision criteria: Regulated industry like healthcare or finance? BYOC is required. Startup or scale-up? Managed until compliance mandates BYOC. Hybrid uses managed for dev, BYOC for production.
Northflank’s BYOC runs in your VPC with full infrastructure control. Same APIs. Same experience. Your cloud credits and commitments.
BYOC needs a cloud account, IAM policies, network config, monitoring. Managed is zero-ops. Cost: BYOC pays the cloud provider directly plus the platform fee. Managed has bundled pricing.
Migration path: Start with managed. Move to BYOC when compliance requires it.
Which Platform Should You Choose? Decision Matrix and Use Case Mapping
High-frequency user-facing—1000+ req/sec, under 1s latency? Daytona’s 27ms cold start reduces the latency pressure. Real-time chatbots, code execution, analysis tools.
Code interpreter in your SaaS? E2B’s polished SDKs, Kubernetes scaling, and code interpreter focus give you the best developer experience for ChatGPT Code Interpreter-like functionality.
GPU-heavy ML—training, inference, batch? Modal’s comprehensive GPU support and Python ML integration justify the premium for data science teams.
Long-running persistent sessions for dev or testing? Sprites.dev’s unlimited persistence with checkpoint and rollback suits workflows where session value exceeds setup cost.
Enterprise compliance—HIPAA, SOC 2, regulated industries? Northflank’s production BYOC with Kata and gVisor handles enterprise security and compliance.
Cost-sensitive GPU? Northflank’s GPU cost advantage with OCI compatibility provides flexibility.
Multi-language? Avoid Modal’s Python-only SDK. Choose E2B for Python and TypeScript, Sprites for Go, TypeScript, Python, or Northflank for OCI-compatible images.
Decision framework: Prioritise your requirements—latency, GPU, persistence, compliance, cost. Eliminate the non-fits. Compare finalists on the secondary factors—SDK quality, docs, ecosystem maturity.
Real-time chatbot with a 500ms latency budget? Daytona eliminates cold start from the critical path.
Northflank accepts any OCI-compliant image from any registry without modifications. OCI compatibility affects migration flexibility.
Total cost of ownership: Cold start × scale + infrastructure + GPU pricing + engineering time.
FAQ Section
What is the fastest AI agent sandbox platform for cold starts?
Daytona achieves 27-90ms provisioning, significantly faster than E2B’s 150ms or Modal’s sub-second performance. This matters for high-frequency invocations in user-facing apps where latency budgets are tight.
Does E2B support BYOC deployment for enterprise compliance?
E2B offers experimental BYOC to AWS, GCP, and Azure for testing compliance feasibility. Northflank provides production-ready BYOC with full feature parity for enterprises needing mature BYOC deployments.
Which platforms support GPU acceleration for ML workloads?
Modal provides comprehensive GPU support—T4, A100, H100, H200—optimised for Python ML. Northflank offers comparable GPU at lower cost, $2.74 versus $3.95/hour for H100, with OCI compatibility.
What isolation technology does each platform use?
E2B and Sprites use Firecracker microVMs for hardware virtualisation. Modal uses gVisor for user-space kernel. Daytona supports Docker, Kata, and Sysbox. Northflank offers both Kata and gVisor for maximum flexibility.
Can I use my existing Docker images with these platforms?
Northflank accepts any OCI-compliant image without modification. E2B and Sprites support custom images. Modal requires SDK-defined images using their Python SDK, which creates potential lock-in.
How long can sandbox sessions persist across these platforms?
Sprites.dev offers unlimited persistence with checkpoints. Northflank provides unlimited duration. E2B allows 24-hour active sessions with 30 days paused. Vercel limits sessions to 45 minutes maximum.
What’s the cost difference between Modal and Northflank for GPU workloads?
Northflank charges $2.74/hour for H100-equivalent workloads versus Modal’s $3.95/hour, representing 62-65% cost savings for comparable GPU performance on production workloads.
Which platform is best for Python data science teams?
Modal’s Python-centric design with comprehensive GPU support, network filesystem persistence, and serverless execution provides an integrated experience for ML teams, though the SDK requirement creates lock-in.
Do these platforms protect against prompt injection attacks?
All platforms use technical isolation—Firecracker, gVisor, or Kata—addressing OWASP #1 LLM risk. Prompt injection has an 84%+ failure rate against prompt-only defences, requiring kernel-level protection.
Can I migrate between sandbox platforms later?
OCI image compatibility affects migration flexibility. Northflank and E2B accept standard container images, enabling easier migration. Modal’s SDK-defined images require application refactoring to switch platforms.
What’s the minimum viable platform for solo developers?
E2B’s developer-friendly SDKs and $100/month tier provide the lowest friction entry. Daytona suits solo developers needing ultra-low latency. Modal requires commitment to the Python SDK ecosystem.
Which platform integrates with Kubernetes for orchestration?
E2B provides native Kubernetes orchestration for horizontal scaling and automated pod management. Northflank offers Kubernetes integration as part of a comprehensive infrastructure platform. Modal and Sprites use proprietary orchestration.
