Modal made a name for itself letting AI/ML teams run Python code — including model training and inference — on GPUs with sub-second cold starts and pay-per-second billing, no YAML required. It's a genuinely good product, but the "run untrusted code or GPU workloads in a sandbox, pay only while it runs" category has gotten crowded fast. Here are 5 real alternatives from our catalogue, each with a slightly different angle: general-purpose code sandboxes, GPU-first rental, and agent-specific infrastructure.
E2B — the open-source-core pick for AI-agent code execution
E2B focuses specifically on secure, disposable cloud sandboxes for AI agents to run generated code without touching production systems, with sub-200ms startup on Firecracker microVMs and a full Linux environment (not a restricted JS-only isolate).
For who: AI/agent developers who need agents to safely execute code, not necessarily train models.
Price: Free tier with $100 usage credits; Pro $150/month + usage (~$0.000014/sec CPU); Enterprise custom.
Forces: open-source core SDK plus self-hosted/BYOC options for compliance-sensitive teams, sub-200ms sandbox startup, full Linux environment with internet access.
Limites: an increasingly crowded category with real overlap from Modal, Daytona and Northflank, and the $150/month Pro floor plus metered usage adds up at scale.
Verdict: the strongest pick if you specifically want an open-source-core sandbox for agent code execution rather than Modal's broader ML-training focus.
Daytona — sub-90ms sandboxes with the biggest open-source footprint
Daytona targets the same "safely execute AI-generated code" niche as E2B, with sandbox creation under 90ms across Python, TypeScript, Go, Ruby and Java, backed by a $24M Series A and customers including LangChain and SambaNova.
For who: teams building AI coding agents, code interpreters, or automation platforms needing multi-language sandboxes.
Price: Pay-as-you-go on vCPU/GPU/memory/storage, volume discounts; free tier with $200 in compute credits, no card required; startup program up to $50,000 in credits.
Forces: sub-90ms sandbox creation built for workflows that spin environments up and down constantly, open source under AGPL with 72,000+ GitHub stars, multi-language support plus full desktop Computer Use automation.
Limites: pay-as-you-go compute is on top of whatever LLM API calls cost, and it only makes sense for products that genuinely need to execute untrusted AI-generated code.
Verdict: the pick if multi-language support and open-source AGPL licensing matter more to you than E2B's longer track record.
RunPod — the cheapest way to just rent a GPU by the second
RunPod skips the sandbox abstraction entirely and goes straight to pay-by-the-second GPU rental for training and serving models, with no contracts and 30+ GPU types across 31 regions.
For who: AI developers and researchers who want raw GPU compute without long procurement cycles or reserved capacity.
Price: Pods $0.27-$7.39/hr per GPU; Serverless $0.58-$9.98/hr; storage $0.05-$0.14/GB/month; no contracts, billed by the millisecond.
Forces: self-serve with no minimum commitments, sub-200ms serverless cold starts via FlashBoot, generally cheaper than enterprise-focused competitors like CoreWeave or Lambda.
Limites: GPU availability during high-demand periods can be less predictable than reserved capacity, and support is lighter-touch than an enterprise contract.
Verdict: pick this over Modal when you just need raw, cheap GPU hours and don't need Modal's Python-native infrastructure-as-code layer on top.
Cerebrium — GPU snapshotting for the fastest real-time cold starts
Cerebrium rents GPU power by the second like Modal, but leans hard into cold-start speed specifically for real-time production apps (voice agents, video, LLMs), using GPU snapshotting to hit 2-4 second cold starts.
For who: teams deploying real-time AI applications (voice agents, video, LLM inference) in production.
Price: Hobby free + compute cost (3 apps, 5 concurrent GPUs); Standard $100/month + compute (30 concurrent GPUs); Enterprise custom. Example: H100 at $0.000944/sec.
Forces: 2-4 second cold starts via GPU snapshotting, per-second billing with zero idle cost, wide GPU selection (T4 to B200) across regions, enterprise compliance (SOC 2, HIPAA, GDPR, ISO) out of the box.
Limites: the Standard tier caps out at 30 concurrent GPUs (Enterprise needed for unlimited), and log retention is limited to 7 days on the free tier.
Verdict: the better fit than Modal specifically for latency-sensitive real-time apps where cold-start time is the metric that matters most.
Blaxel — built for multi-agent systems, not just single workloads
Blaxel targets a more specific niche than Modal: cloud infrastructure for running autonomous multi-agent AI systems at scale, with millisecond sandbox startup, near-zero idle cost, and a shared distributed filesystem (Agent Drive) built for agents to collaborate.
For who: teams building and deploying autonomous multi-agent AI systems, not single training/inference jobs.
Price: $200 free credits; pay-as-you-go from ~$0.0000115/GB RAM-second; optional subscription tiers from $20/month.
Forces: genuinely zero compute cost while agents are idle, supports 50,000+ concurrent microVMs with ~25ms resume from pause, Agent Drive shared filesystem built specifically for multi-agent collaboration.
Limites: a crowded category with well-funded competitors including Modal itself, no open-source core unlike E2B's SDK, and pay-as-you-go pricing needs careful usage modeling.
Verdict: worth it specifically if your workload is many agents coordinating, not one model doing inference — otherwise Modal or RunPod are the simpler fit.
Side-by-side
| Tool | Core angle | Cold start | Price floor | Best for |
|---|---|---|---|---|
| Modal | Python-native ML infra | Sub-second | Free ($30/mo credits) | Training + inference as Python code |
| E2B | Agent code sandboxes | Sub-200ms | Free ($100 credits) | Open-source-core agent code execution |
| Daytona | Multi-language sandboxes | Sub-90ms | Free ($200 credits) | Open-source (AGPL), multi-language |
| RunPod | Raw GPU rental | Sub-200ms serverless | Pay-as-you-go, no floor | Cheapest raw GPU hours |
| Cerebrium | Real-time inference | 2-4s (GPU snapshot) | Free (Hobby) | Latency-sensitive production apps |
| Blaxel | Multi-agent infra | Milliseconds | $200 free credits | Coordinating many agents at once |
None of these is a drop-in Modal replacement — they split the same market by workload shape. If you're specifically executing AI-generated code from an agent, E2B or Daytona fit better than Modal's broader ML focus. If you just want the cheapest GPU-hour, RunPod wins on price. If cold-start latency for a live, user-facing app is the constraint, Cerebrium is built around exactly that. And if the real problem is coordinating many agents rather than running one workload, Blaxel is the only one built for that shape from the ground up.