Fan-out execution: how to run AI tasks in parallel without provisioning

Fan-out execution splits large jobs into independent tasks that elastic compute can run concurrently. This article explains the pattern’s mechanics, on-demand provisioning, task isolation, and economics. It also covers applications to code review, document processing, and data enrichment. Consider a hypothetical agent pipeline with a large dataset. Each row needs independent analysis. The task parses data, calls a model, validates the output, and writes the result. In this scenario, sequential execution cannot meet the deadline.

Under ideal linear scaling, closing that gap requires enough parallel workers to fit the work into the available window. Provisioning a dedicated VM fleet for a short weekly job is expensive and wasteful. Keeping those VMs alive between runs costs even more.

Fan-out execution is the pattern built for this workload. You split the job into independent tasks and run them simultaneously across elastic compute. With Batch Jobs, the platform provisions workers on demand, processes tasks, and scales down after execution.

TL;DR

• Fan-out splits one job into many parallel tasks: Each task processes one unit of work independently. An application aggregator collects the results after every task completes.
• Elastic compute provisions workers on demand: Submit the job, and the platform launches isolated sandboxes concurrently without pre-allocation or manual capacity planning.
• Batch Jobs use active GB-second billing: Compute usage is metered from allocated memory while tasks are active. Batch Jobs scale with submitted work and scale down after execution, while storage or other separately used resources may carry their own charges.
• Each task runs in its own isolated sandbox: A crash in one task can't reach another. The rest of the batch completes while you log and retry the failure.
• Provisioning complexity moves to the platform: Your team defines the job and a concurrency target. Scheduling, provisioning, distribution, monitoring, and Batch Job cleanup happen underneath.

The fan-out execution pattern for AI workloads

Fan-out shares ancestry with MapReduce, worker pools, and message queues. Engineers routinely conflate the four.

What fan-out execution means

Fan-out execution spreads one input across many independent workers. The input can be a dataset or a batch of pull requests. Each worker processes one slice. After every worker finishes, an application aggregator collects the results into a single output. The distribution step is the fan-out.

The collection step is the fan-in. Task independence is what makes the pattern work. A task never reads the preceding task's result. No local runtime state is shared between tasks while they run, although tasks can intentionally use mounted shared storage for aggregation. Without dependencies, budget rather than task order sets the parallelism ceiling.

Agent workloads that fit share one shape: a unit of work an agent can pick up cold. Dependent tasks belong in a workflow engine.

• Code review: Each task handles one file or one PR.
• Document processing: Each task handles one document.
• Data enrichment: Each task handles one record.
• Test execution: Each task handles one test suite.
• Content generation: Each task handles one article or one variation.

How fan-out differs from worker pools and message queues

Worker pools run a fixed number of long-lived workers that pull tasks from a queue. The pool size is set up front. Growing it requires manual additions or autoscaling. EC2 Auto Scaling applies a 300-second cooldown between scaling activities. GKE nodes take 80–120 seconds to boot before accepting work.

A time-sensitive job can spend a noticeable share of its budget before the pool reaches full size. Pool workers also share a runtime. One code-executing agent task running bad generated code can take its neighbors down. Message queues such as SQS, RabbitMQ, or Kafka decouple producers from consumers and solve distribution well. The consumers still need compute.

The queue routes work while your team provisions, scales, and patches the underlying runtime. Fan-out execution on elastic compute folds distribution and provisioning into the platform. You submit the job with a concurrency target. Batch Jobs create isolated workers, distribute tasks, and track progress. Application code aggregates task outputs directly or through shared storage. For workloads that run arbitrary AI-generated code, isolated sandboxes provide a safer boundary than one shared kernel.

Architecture of elastic fan-out execution

Two mechanisms carry the pattern. One is parallel provisioning from a shared capacity pool. The other is a hard isolation boundary around each task.

The provisioning model: scaling on demand

The platform holds compute capacity that isn't allocated to any customer in advance. When a job arrives, it creates sandboxes up to the concurrency limit. The platform launches sandboxes concurrently rather than sequentially. Available capacity sets the boundary. The platform ramps toward full concurrency rapidly.

Firecracker, the open-source microVM technology, launches 150 microVMs per second on one host. That launch rate supports rapid fan-out without sequential VM provisioning. Perpetual sandbox platforms like Blaxel implement this model on microVM infrastructure. Blaxel Batch Jobs run tasks in individual, isolated sandboxes. Each boots in approximately 30 seconds, runs to completion, writes output, and exits.

You set the ceiling with maxConcurrentTasks in the [runtime] block of blaxel.toml. Batch Jobs scale to that target. Standalone Blaxel Sandboxes follow a different lifecycle. After 15 seconds of network inactivity, they return to standby. CPU and memory compute are not charged in standby. Inside a longer-running job, the Batch Job boot time is a small part of total execution. A self-managed fleet requires pre-allocated VMs, autoscaling policies, failure handling, and teardown logic. The elastic model moves those responsibilities onto the platform.

Isolation at the task level: crash containment in parallel execution

In a hypothetical fan-out job containing many tasks, some can fail. A document can contain malformed data. Generated code can loop forever, or a third-party API call can time out. Without task isolation, a failed worker can corrupt the shared runtime and cascade across the batch.

With each task in its own microVM, the failure stays where it started. Neighboring tasks continue. They share no kernel, memory, or local filesystem; any shared durable storage must be intentionally mounted. Containers share a kernel, so their segmentation “is far less than that provided to VMs by a hypervisor”.

Containers remain the right tool for trusted first-party services. Code-executing agent workloads can run unreviewed code that the model generated moments earlier. This is the exact case for a hypervisor boundary. Isolation also simplifies recovery. Log failures with their input parameters and error output, then reprocess only failed tasks. Successful results remain valid. Operations on external systems must be idempotent so retries cannot duplicate records.

The economics of releasing compute after batch completion

The cost argument reduces to hours billed against hours worked.

Pay for execution, not for waiting

Dedicated infrastructure bills whether it works or waits. A fleet provisioned continuously for a periodic batch remains billable between executions even though those idle hours produce nothing. Batch Jobs instead meter active compute usage and scale down after execution.

Consider a common compute-optimized instance. AWS lists c5.xlarge, with four vCPUs and eight GiB, at $0.17 per hour on demand in us-east-1. A periodically used fleet billed continuously accumulates charges throughout its idle hours, while running the same capacity only during the batch aligns billed usage with useful work.

For a CTO defending cloud spend, that difference can separate a rounding error from a line item. Industry data indicates that this waste is normal rather than contrived. IDC estimates that 10–30% of cloud spending is wasted. That range makes idle-capacity reduction a meaningful optimization target. Dedicated infrastructure wins when the job never stops. Batch Jobs that release compute after execution win for periodic workloads. These include nightly enrichment, weekly backfills, and per-PR review.

Cost predictability through concurrency controls

The concurrency limit caps simultaneous usage and peak spend rate. The platform never runs more sandboxes simultaneously than the configured limit. Total run cost depends on total active GB-seconds across all tasks. That includes allocated memory, task execution time, and relevant retries.

Task count, allocated memory, prior task duration, retry assumptions, and GB-second rates are known inputs. Estimate total active seconds across every task, including expected retries. Then multiply that total by allocated memory and the GB-second rate. This produces a cost estimate rather than a hard upper bound. Several explicit levers trade speed against cost:

• Lower concurrency: Fewer parallel sandboxes stretch wall-clock time but cap simultaneous usage and peak spend rate.
• Shorter tasks: Trimming task code shortens active seconds, which lowers cost per task directly.
• Smaller batches: Fewer items per run produce smaller, more frequent bills that are easier to budget.

Pull task durations from recent runs and estimate the next submission. Dedicated fleets also carry costs absent from compute invoices. Someone provisions, monitors, patches, scales, and debugs them outside normal working hours. The Catchpoint SRE Report 2025 puts median toil at 30% of practitioner time. Reducing infrastructure toil can therefore return meaningful engineering capacity. A managed platform folds that labor into its usage rate.

Fan-out patterns for common AI agent workloads

Common hypothetical workloads illustrate what agent teams fan out today. Each example defines its input, task unit, concurrency target, and limiting constraint.

Parallel code review across a monorepo

Consider a hypothetical monorepo pull request containing a large set of changed files. Each task reviews a file for code quality, security issues, and style compliance. The concurrency target allows many reviews to proceed simultaneously. Every task receives a file path plus the diff context.

The review agent analyzes the file and writes findings as a structured record. After all tasks finish, an aggregator merges the records into a PR review comment. Fan-out parallelizes work that one reviewer agent would otherwise perform serially. A large serial review can take hours, while a parallel fleet divides the files across workers and finishes far sooner.

Because each review starts from its own file and diff context, it does not depend on another task’s findings. The aggregator, rather than the workers, owns final comment ordering and consolidation. That separation keeps the review tasks independent while still producing a coherent result for the pull request.

Dean and Barroso found that 63% of requests wait on a straggler when many parallel servers are each occasionally slow. Set a per-task timeout so one stuck file can't stall the review comment.

Document processing at scale

Assume an input containing a large collection of PDF documents. Each task extracts structured data, classifies content, and writes a JSON record. The hypothetical concurrency target allows a large group of documents to be processed in parallel. Every task receives a document URL and boots its sandbox.

The task runs the extraction agent, writes its result to shared storage, and exits. Under ideal scaling, wall-clock time falls as concurrency rises. Instead of processing every document sequentially, the job completes in successive parallel waves and can finish within a much shorter window.

Each document maps to a corresponding output record. Malformed input can therefore fail independently without invalidating records from successful tasks. The shared filesystem separates durable aggregation from the temporary compute used for extraction. Workers can finish and exit while their JSON results remain available for the final collection step.

Aggregation needs storage that outlives each sandbox. Agent Drive gives sandboxes and agents concurrent read-write access to one intentionally mounted shared filesystem. It is in private preview and limited to the us-was-1 region. Place the job there for now.

Data enrichment and validation pipelines

Assume a customer relationship management (CRM) export containing a large contact list. Each task enriches a contact and validates the result. Enrichment includes company data, a professional profile, and recent news. The hypothetical concurrency target allows many contacts to be processed together. Third-party rate limits, rather than compute, constrain throughput.

Every task takes a contact record, calls external APIs, validates the response, and writes the enriched record. Even if compute supports substantially more simultaneous tasks, the API rejects requests when the request rate exceeds its limit. Model providers impose limits too. OpenAI's GPT-4o Tier 1 allows 500 requests per minute. A task calling that model inherits the limit.

Set the dispatch rate to the tightest external limit. The safe concurrency depends on task duration and each task's request count. A request-per-minute ceiling does not automatically permit the same number of concurrent tasks. Excess requests can trigger rate-limit responses while tasks continue consuming compute. Treat Retry-After as a minimum. Add a small random delay so clients don't retry in lockstep.

Scale on demand and back

Fan-out execution turns one large agent workload into many parallel, isolated tasks. Batch Jobs provide on-demand task execution and progress tracking. Application code or shared storage handles result aggregation. Your team writes the task logic and chooses a concurrency limit.

Elastic fan-out reduces idle infrastructure while containing failures within individual tasks. Batch Jobs run tasks in isolated sandboxes on microVM infrastructure. Billing follows active GB-seconds, while concurrency controls simultaneous usage. See the Blaxel Jobs documentation. Add Agent Drive for output aggregation and cron triggers for recurring runs.

That removes custom scheduling, provisioning, and teardown code, while your application still handles aggregation, retries, and external rate limits. Start building at app.blaxel.ai or talk to the team at blaxel.ai/contact.

FAQ

What distinguishes fan-out from MapReduce?

MapReduce requires map functions to emit key-value pairs, which a framework-defined reduce stage then merges by key. Fan-out imposes no key-grouped reduction model or prescribed aggregation step. Your application defines how outputs are collected, ordered, and combined, making fan-out suitable when independent tasks produce records or artifacts for later application-level collection.

How should failed tasks be retried?

Configure a per-task retry count based on the reliability of external dependencies.

Can fan-out jobs run on a recurring schedule?

On Blaxel, a [[triggers]] block with type = "cron" in blaxel.toml defines the schedule and can include an optional task list.