AI agents processing user requests face an infrastructure paradox: traffic arrives unpredictably, responses must start instantly, yet the actual request processing can take several seconds or longer. A coding agent might sit idle for hours, then handle 50 concurrent sessions when developers start their workday.
Traditional infrastructure forces you to choose between paying for servers that run 24/7 or accepting multi-second cold starts that break user experience. Serverless computing promises to solve this by automatically scaling from zero to thousands of requests while charging only for actual execution time and sometimes also for the number of requests, like AWS Lambda or GCP Cloudrun.
Agents executing code face cold start penalties from cloning repositories, loading datasets, and installing dependencies. A PR review agent cloning a 50,000-file repository adds over two minutes of latency before the agent can even start analyzing code. Plus, the security isolation requirements for agents executing LLM-generated code introduce further constraints that standard serverless platforms weren't designed to handle.
This guide covers how serverless execution works for AI agents, practical use cases where the model delivers value, and the infrastructure challenges you need to plan for when moving agents from prototype to production.
What is serverless computing?
Serverless computing is a cloud execution model where applications run without infrastructure management. The cloud provider handles server provisioning, operating system patching, scaling operations, and capacity planning. You deploy code, and the platform executes it in response to events.
While serverless is the core idea, Function-as-a-Service (FaaS) is one application of it. Events trigger code execution with automatic resource management and scaling. The name "serverless" is somewhat misleading because the servers do exist, but you don't manage them.
Most serverless platforms use container-based architectures where the platform runs multiple functions on the same kernel. Containers provide fast boot times (typically under 2 seconds) but create potential container escape vulnerabilities because all workloads share the same kernel.
When an AI agent executes LLM-generated code, a single malicious prompt could produce code that exploits kernel vulnerabilities to break out of its container. Once the attacker gains kernel access, they can reach other customers' containers running on the same host, access credentials stored in memory, or exfiltrate data from adjacent workloads.
MicroVM platforms solve this by providing hardware-enforced isolation where each workload runs in its own kernel. An exploit in one agent's generated code cannot reach the host system or other tenants because the hypervisor maintains strict boundaries at the hardware level. This architecture uses the same technology as AWS Lambda, where each function executes in a separate microVM with its own kernel.
Containers consume fewer resources since they share the same kernel, but microVMs prevent the multi-tenant security breaches that end AI startups. If you're building with agents executing untrusted code in production, then this architecture decision determines whether a prompt injection vulnerability stays contained within a single sandbox environment or escalates into a data breach affecting all of your customers.
How does serverless computing work?
Serverless functions execute through an event-driven model rather than running continuously. When a trigger fires, the platform provisions an execution environment, loads your code, runs the function, and releases resources when execution completes.
Common triggers include:
- HTTP requests
- Database change events
- Message queue events
- File storage events
- Scheduled time-based triggers
The execution lifecycle follows a predictable pattern. If no warm execution environment exists, the platform creates one through a cold start by provisioning resources, downloading code, initializing the runtime, and executing initialization code. For agent workloads executing code, this cold start latency is dominated by cloning repositories, loading datasets, and installing dependencies. A coding agent might need 30 seconds just to clone a repository and install packages before it can execute any code.
Warm starts occur when a function invokes while an execution environment remains active from a previous request. The platform reuses the existing container and skips initialization entirely.
Provisioned concurrency (which keeps execution environments perpetually warm by paying a baseline cost) offers a middle ground: you pay to keep a specified number of execution environments perpetually warm, eliminating cold starts for predictable traffic at a higher baseline cost. The higher baseline costs guarantee consistent response times for latency-sensitive AI inference.
AWS Lambda lets users configure memory allocation from 128 MB to 10,240 MB. The platform allocates CPU and other compute resources proportionally. Memory and CPU allocation are inherently linked. This means AI workloads requiring compute resources (whether they’re running sandboxes for coding agents or loading models for inferences) also need higher memory allocations that directly impact costs.
Pricing follows pay-per-execution models across all major providers. You pay nothing when functions sit idle, with provisioned concurrency options available for eliminating cold starts at higher cost.
What are the benefits of serverless computing?
Serverless computing delivers measurable advantages for AI-first startups, particularly for workloads with variable traffic patterns.
Safe execution of untrusted code
AI agents executing LLM-generated code need isolated environments where malicious code can't escape. Serverless platforms with microVM isolation provide hardware-enforced boundaries that prevent one agent's code from accessing another customer's data or compromising the host system.
State persistence across agent sessions
Agents benefit when execution environments maintain state between requests. A coding agent keeps repositories cloned, datasets loaded, and dependencies installed. This eliminates the overhead of re-initializing environments on every request, reducing latency from minutes to milliseconds.
Cost efficiency through pay-per-use pricing
Pay-per-use pricing reduces infrastructure costs for variable workloads. A coding agent that sits idle 90% of the day incurs zero compute charges during downtime. Infrastructure automatically scales to zero when developers close their sessions, eliminating the cost of keeping servers running 24/7.
Concurrent agent session scaling
Production agents handle spikes in concurrent users without infrastructure planning. When 50 developers start their workday simultaneously and launch coding assistants, serverless infrastructure provisions isolated sandboxes for each session automatically.
Reduced operational burden
Managed services handle automatic scaling, security patching, high availability, and monitoring infrastructure. So now your engineering team can focus on improving prompts, tool selection, and agent workflows rather than configuring Firecracker microVMs, managing kernel updates, or debugging container escape vulnerabilities.
8 serverless computing use cases for AI development
Understanding real-world applications helps you identify where serverless architecture delivers the most value for your AI infrastructure. These use cases span the full range of AI workloads, from basic inference to complex agent systems, so you discover which patterns match your specific requirements and traffic characteristics.
1. Coding agents with live preview
AI-powered coding assistants where end users interact with agents that generate code and provide real-time previews require instant responsiveness. Real-time interactions demand infrastructure that resumes in under 100 milliseconds.
However, traditional container-based serverless platforms take two to five seconds to cold start, or even longer if the image is not present on the host kernel. This creates delays that break user experience. Specialized platforms address this through perpetual standby architecture. For example, perpetual sandbox platforms like Blaxel keep sandboxes in standby mode indefinitely, resuming in under 25 milliseconds when needed. The architecture eliminates the cold start penalty while maintaining zero compute cost during idle periods.
2. PR review automation
Automated code review agents analyze pull requests, run code in AI sandboxes, and suggest improvements. PR reviews happen sporadically throughout the day, so paying only during actual review execution makes sense.
Simple static analysis works fine with container-based serverless platforms. But agents executing untrusted code from pull requests need stronger isolation to prevent kernel-level exploits (attacks that compromise the operating system core). MicroVM isolation provides hardware-enforced boundaries because each execution environment runs its own kernel.
Beyond security, perpetual sandbox platforms like Blaxel maintain repository state between reviews. When a new PR arrives, the sandbox resumes with the repository already cloned and dependencies installed, eliminating the 30- to 90-second overhead of pulling large codebases on each review.
3. Testing and QA automation agents
AI agents that generate and execute test cases need isolated environments where tests run without interfering with each other. A QA agent analyzing a new feature generates edge case tests, spawns parallel sandboxes to run each test suite, and reports failures with reproduction steps.
Serverless infrastructure provisions test environments on-demand rather than maintaining dedicated QA infrastructure. When 50 developers merge PRs simultaneously, hundreds of test sandboxes spin up in parallel, execute tests, and terminate automatically. Then you only have to pay compute costs during test execution rather than keeping test servers running 24/7.
4. Code refactoring and migration agents
Agents that modernize codebases need to analyze existing code, apply transformations, and verify changes compile and pass tests. A migration agent upgrading a Python 2 codebase to Python 3 identifies deprecated syntax, rewrites code patterns, and executes the modified code to confirm functionality.
Persistent sandboxes keep the repository cloned and dependencies installed between migration runs. When the agent needs to refactor 500 files, it maintains state across multiple iterations rather than re-cloning the repository for each change. This reduces refactoring time from hours to minutes.
5. Data analysis agents
AI agents that analyze data by generating and executing Python scripts need isolated environments where code runs safely. A natural language analytics agent converts questions like "what's our customer churn rate by region?" into executable SQL or pandas code that queries databases and generates visualizations.
Serverless sandboxes let data analysis agents execute generated code without maintaining dedicated compute infrastructure. Teams asking sporadic questions throughout the day pay only during actual query execution. The agent spins up a sandbox, runs the analysis script, returns results, and releases resources automatically.
6. Production RAG systems with vector databases
AI agents use retrieval-augmented generation (RAG) to access proprietary knowledge bases before taking actions. An agent answering customer questions retrieves relevant documentation, generates a response based on that context, and executes follow-up actions like creating support tickets or updating records.
Serverless infrastructure handles the unpredictable traffic patterns RAG agents generate. Customer support agents sit idle overnight, then handle hundreds of queries when business hours start. Serverless vector search and orchestration functions scale from zero to peak demand automatically, charging only for actual retrieval operations.
7. Conversational AI agents
Conversational agents go beyond answering questions by taking actions based on chat interactions. A customer service agent understands "I need to change my shipping address for order #1234" and executes the database update, verifies inventory at the new location, and confirms the change without human intervention.
These agents maintain conversation state across multiple turns while executing backend operations in isolated sandboxes. Serverless infrastructure scales agent capacity during peak support hours and costs nothing when chat volume drops overnight.
8. Batch agent processing
Agents processing large workloads split tasks into parallel sub-tasks that execute simultaneously. A security audit agent analyzing 500 repositories spawns isolated sandboxes for each repository, runs static analysis in parallel, and aggregates results.
Serverless infrastructure provisions hundreds of concurrent sandboxes automatically, processes the entire batch in minutes rather than hours, and releases resources when the job completes. You pay only for actual processing time rather than maintaining dedicated worker pools that sit idle between batch runs.
Get started with serverless computing for AI agents
AI agents executing untrusted code require infrastructure that solves three problems:
- Cold starts that break real-time interactions
- State persistence for avoiding reload overhead
- Security isolation that prevents kernel-level exploits
Production agents executing LLM-generated code need stronger isolation guarantees and faster response times because the fastest agent will make the difference in a very competitive market. Specialized perpetual sandbox platforms address these constraints by maintaining sandboxes in standby mode that resume instantly while providing hardware-enforced isolation to prevent code execution exploits.
Blaxel provides a perpetual sandbox platform that maintains sandboxes in standby mode indefinitely with zero compute cost during idle periods. Unlike traditional serverless platforms that delete sandboxes after inactivity, sandboxes resume in under 25 milliseconds with complete filesystem and memory state preserved. The microVM architecture runs each sandbox in its own kernel to prevent exploits in one agent's code from reaching the host system or your other customers' workloads.
Ready to eliminate cold starts from your AI agent infrastructure? Start a free trial of Blaxel with $200 in credits or schedule a demo to see how perpetual sandboxes handle production workloads with sub-25 millisecond resume times. Test untrusted code execution at scale, validate microVM isolation, and measure actual compute costs with zero idle charges. No credit card required.
FAQs about serverless computing use cases
What types of AI workloads are best suited for serverless computing?
Serverless works best for AI workloads with variable or unpredictable traffic patterns. ML inference endpoints handling sporadic requests benefit most because you pay nothing during idle periods, and serverless infrastructure can scale quickly to respond to usage spikes. RAG systems, chatbots, and coding agents that need to scale up during spikes often see cost savings compared to always-on infrastructure.
How do cold starts affect AI agent performance and what can you do about them?
Cold starts add latency depending on runtime and model size. Python and Node.js deliver cold starts typically ranging from hundreds of milliseconds to over a second in container-based platforms, while Java often experiences several seconds.
Rather than cold starting from scratch each time, perpetual sandbox platforms use snapshotting to preserve complete sandbox state during idle periods. MicroVM platforms like Blaxel resume from these snapshots in under 25 milliseconds while maintaining filesystem and memory state across sessions.
What security considerations apply to serverless AI agent deployments?
AI agents executing LLM-generated code require strong isolation, particularly when running code from untrusted sources like LLM outputs or user inputs. Container-based isolation shares the host kernel, which creates potential attack vectors for container escape vulnerabilities. A malicious prompt could generate code that exploits kernel weaknesses to break out of its container and access other customers' data.
MicroVM isolation provides stronger security because each execution environment runs its own kernel. When an agent executes potentially malicious code, the microVM's hardware-enforced boundaries prevent exploits from reaching the host system or other tenants, even if the generated code attempts kernel-level attacks.
