Compute Engines for AI Agents: Why One Size Doesn't Fit All

When deploying AI agents that execute code, manipulate data, or orchestrate workflows, choosing the right compute engine becomes critical. The landscape offers everything from microsecond-booting WebAssembly runtimes to bulletproof virtual machines, each optimizing for different priorities. Understanding these trade-offs reveals why successful AI agent platforms need multiple compute options working in tandem—and why we built Noorle with a dual-compute architecture.

The Compute Engine Landscape

Containers: The Production Workhorse

Containers have become the de facto standard for production AI deployments, achieving near-native performance with minimal overhead. When images are pre-pulled and cached, containers can start in under 100 milliseconds, though Kubernetes orchestration and image pulls often push total deployment times to 1-3 seconds in practice.

Strengths:

• Near-native performance (within 0.12% of bare metal for many workloads)
• Mature ecosystem with Kubernetes orchestration
• Excellent GPU support via NVIDIA Container Toolkit
• High density—hundreds to thousands per host
• OCI standardization ensures portability

Weaknesses:

• Shared kernel creates security risks for untrusted code
• Recent vulnerabilities demonstrate ongoing escape risks
• Requires additional hardening layers (gVisor, Kata) for untrusted workloads
• Complex orchestration for large-scale deployments

For AI agents, containers excel at running trusted model servers and tool backends but require additional security layers when executing dynamically generated code.

MicroVMs: Security Meets Speed

Firecracker microVMs revolutionized serverless computing by combining VM-level isolation with container-like agility. Fresh microVM launches take approximately 125-150ms, with snapshot restore achieving millisecond-class resumption. The VMM adds just 5MB overhead (excluding guest memory), enabling AWS to create up to 150 microVMs per second per host.

Strengths:

• Hardware-level isolation with minimal overhead
• Defense-in-depth security (VM isolation + process jails + seccomp)
• High density for VM-based isolation
• Ideal for multi-tenant environments
• Immutable, ephemeral by design

Weaknesses:

• No production GPU support (community discussions ongoing for 2025+)
• Limited to CPU-only workloads currently
• Higher complexity than containers
• Slightly higher latency than native containers

MicroVMs represent the sweet spot for executing untrusted agent code safely at scale, though GPU limitations restrict their use for inference tasks.

WebAssembly: The Edge Pioneer

WebAssembly module instantiation happens in microseconds (approximately 5μs in modern runtimes), with platforms like Cloudflare Workers achieving single-digit millisecond cold starts. This enables thousands of instances per host with minimal memory overhead.

Strengths:

• Near-instantaneous startup for module instantiation
• High density (thousands+ instances per host)
• Portable across browsers, servers, and edge locations
• Language agnostic with growing ecosystem
• Strong sandboxing for untrusted code

Weaknesses:

• Performance varies widely by workload (often slower than native, but gap narrowing with SIMD)
• GPU support remains experimental (WebGPU/WASI-GFX emerging but not production-ready)
• Limited POSIX compatibility
• Restricted system calls limit complex I/O operations

WebAssembly excels for lightweight agent tools and edge deployment, with emerging GPU capabilities via WebGPU showing promise for future AI workloads.

Traditional VMs: Maximum Isolation, Measured Overhead

Modern virtual machines have dramatically reduced their performance penalties. VMware vSphere 8 with vGPU achieves 95-104% of bare metal performance in MLPerf Inference benchmarks. Cold starts range from seconds to tens of seconds depending on image optimization and initialization complexity, with tuned cloud images often booting in under 10 seconds.

Strengths:

• Complete OS isolation with decades of hardening
• Full GPU passthrough with minimal overhead (workload-dependent, typically 2-5%)
• Supports any operating system or software stack
• Meets strictest compliance requirements
• Mature tooling and management

Weaknesses:

• Higher startup latency than containers or microVMs
• Hypervisor overhead of tens to hundreds of MB (plus allocated guest memory)
• Lower density than container-based solutions
• Higher operational complexity
• Overkill for short-lived agent tasks

VMs remain ideal for persistent services, complex agent environments, and workloads requiring full GPU acceleration with strong isolation.

Serverless Platforms: Scale Without Infrastructure

Serverless functions abstract infrastructure entirely, with cold start characteristics varying dramatically by platform. Cloudflare Workers achieve near-zero cold starts using V8 isolates, while AWS Lambda’s cold starts vary by runtime and package size (often 100-500ms with optimizations like SnapStart).

Strengths:

• Infinite scaling with zero management
• Pay only for actual execution time
• No idle costs with scale-to-zero
• Handles traffic spikes automatically
• Built-in high availability

Weaknesses:

• Cold start penalties vary widely by platform and runtime
• Execution time limits (platform-specific)
• Limited local storage and state management
• Vendor lock-in concerns
• Can become expensive for high-frequency operations

Serverless can be cost-effective for variable workloads, but the per-execution pricing model becomes prohibitive when agents perform hundreds or thousands of operations per session.

The Spectrum of Agent Workloads

Rather than a simple binary, AI agent workloads exist on a spectrum:

Lightweight Operations (60-70% of executions)

• Data validation and transformation
• API response parsing
• Simple calculations
• Quick file manipulations
• Testing hypotheses
• Exploratory analysis

These happen hundreds of times per session and need millisecond response times with minimal overhead.

Medium-Complexity Tasks (20-30% of executions)

• Data processing pipelines
• API orchestration
• Lightweight ML inference
• Multi-step workflows with transient state
• Prototyping solutions

These benefit from more resources but don’t require full system access.

Heavy Operations (10-20% of executions)

• Software compilation and installation
• Docker container management
• Persistent service deployment
• GPU-accelerated inference
• Complex system configuration
• Production deployments

These demand full OS capabilities and often GPU access.

The Fundamental Challenge

This spectrum reveals why no single compute engine suffices:

• Secure isolation increases costs: MicroVMs and full VMs provide strong boundaries but add latency and resource consumption that compounds over thousands of operations
• Lightweight runtimes limit capabilities: WebAssembly and isolates boot instantly but lack GPU support and full system access
• Containers require hardening: Default configurations share kernels, requiring additional layers that add complexity
• Serverless becomes expensive at scale: Per-execution pricing that seems reasonable for occasional use becomes prohibitive when agents execute hundreds of operations

Most critically, the economics don’t align with agent behavior. When an agent might test dozens of approaches, validate hundreds of data points, or explore multiple solution paths before finding the right one, even small per-operation costs multiply rapidly.

Why Noorle Built a Dual-Compute Architecture

This reality drove our architectural decision: agents need both near-free exploratory compute AND full-featured environments when required.

The Code Runner: Near-Zero Cost Exploration

Noorle’s Code Runner provides lightweight, sandboxed execution optimized for the thousands of small operations agents perform:

• Instant startup for Python and JavaScript execution
• Process-level sandboxing for controlled execution
• Minimal overhead enabling rapid iteration
• Near-zero cost per execution—agents can run thousands of code snippets without impacting your budget

This isn’t just about cost savings—it’s about enabling a different mode of operation. When execution is virtually free, agents can:

• Test multiple hypotheses in parallel
• Validate every data point thoroughly
• Explore alternative approaches without concern for budget
• Iterate rapidly toward optimal solutions

Virtual Machines: Full Power When Needed

Noorle’s VM infrastructure delivers complete Linux environments for complex operations:

• Full system access for software installation and configuration
• Docker support for containerized workflows
• Persistent state across sessions
• Service management for long-running processes
• Affordable rates that make sense for production workloads

The key is that VMs are available instantly when needed, but you only pay for them when agents require full system capabilities.

Seamless Promotion Between Tiers

The magic happens in the transition. Agents naturally start with lightweight exploration in the Code Runner—testing ideas, validating approaches, prototyping solutions. When they identify a path forward that requires more capabilities, they seamlessly promote to VMs.

This mirrors how human developers work:

1. Sketch in a REPL (Code Runner): Quick tests, data exploration, hypothesis validation
2. Prototype in notebooks (Code Runner): Iterate on approach, refine logic
3. Build for production (VM): Install dependencies, configure services, deploy solutions

The Economic Advantage

Consider a typical agent session:

• 500 data validation checks
• 200 API response transformations
• 100 exploratory calculations
• 50 prototype iterations
• 5 production deployments

With serverless or VM-only approaches, those 850 lightweight operations would accumulate significant costs. With Noorle’s dual-compute model:

• 850 operations in Code Runner: Near-zero cost
• 5 VM deployments: Affordable production rates
• Total cost: Fraction of alternatives

This isn’t theoretical—it’s the difference between agents that can explore freely versus those constrained by per-operation economics.

Future-Proofing Through Flexibility

As the compute landscape evolves—WebAssembly gaining GPU support, Firecracker adding PCIe passthrough, new isolation technologies emerging—our dual-compute architecture positions us to adopt innovations selectively:

• Enhance the Code Runner with new lightweight technologies
• Upgrade VM capabilities as hardware improves
• Add intermediate tiers if workload patterns shift
• Maintain consistent APIs while improving underlying implementations

Operational Simplicity

Despite having two compute tiers, the complexity is hidden from users. Agents automatically:

• Start in Code Runner for exploration
• Detect when operations require more capabilities
• Promote to VMs transparently
• Return to Code Runner when lightweight execution suffices

This abstraction means you get optimal resource allocation without manual intervention.

Conclusion

The search for a universal compute engine for AI agents misses a fundamental point: different operations have vastly different requirements and economic profiles. By providing both a near-free Code Runner for exploration and affordable VMs for production workloads, Noorle enables agents to operate the way developers actually work—iterating rapidly and cheaply, then deploying robustly when needed.

This dual-compute architecture isn’t just a technical optimization—it’s an economic enabler. When agents can explore without cost concerns, they become more effective at finding optimal solutions. When they can seamlessly access full system capabilities, they can implement those solutions completely.

The future of AI agent infrastructure isn’t about choosing the perfect compute engine. It’s about providing the right engine for each task, with economics that encourage exploration and capabilities that enable execution. That’s what Noorle’s dual-compute architecture delivers: the freedom to explore at near-zero cost, with the power to execute when it matters.