Cloudflare Computer vs Experiential Labs: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Cloudflare Computer and Experiential Labs — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
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Cloudflare Computer
Cloudflare
Cloudflare's virtual filesystem for AI agents — a Durable Object-backed workspace with three pluggable execution runtimes.
Key features
- Durable Object Workspace: The authoritative filesystem state lives in a Durable Object's SQLite store, so agent state is transactional, colocated, and survives worker restarts.
- Pluggable Runtime Backends: One workspace.runtime.exec entry point lets a Workspace register multiple execution backends under stable IDs and pick per call.
- Container Backend: Projects the SQLite state into a sandbox container as a real FUSE mount via computerd — full Linux userland, real binaries, real network.
- Isolate Shell Backend: Runs just-bash inside a Dynamic Worker that reaches the authoritative Workspace over Workers RPC — no container, no second store, no sync round trip.
- Isolate JavaScript Backend: Evaluates ES modules in a fresh Dynamic Worker with structured input/results, durable relative imports, Workspace-backed node:fs/promises, and trusted ws:git and ws:artifacts modules.
- Egress Policy Controls: The examples/egress worker demonstrates matching none, all, or custom egress policies across all three backends for the same request.
- Filesystem-only Mode: A Workspace can be constructed without any backend at all, giving agents just the filesystem surface for tools that don't need execution.
- Worked Examples: The examples/ directory ships runnable Workers — container, worker-shell, worker-javascript, egress, think, and a compare-runtimes UI — each with its own README.
Best for
- Building Coding Agents on Workers: Give a Workers-native agent its own filesystem and shell so it can write code, run tests, and produce artifacts without leaving Cloudflare's edge.
- Comparing Runtimes Side-by-side: The examples/think compare-runtimes UI runs the same task against the container and worker runtimes to profile latency, isolation, and cost.
- Sandboxed User Scripts: Run untrusted user-supplied JavaScript against a per-user Workspace filesystem in an Isolate JavaScript backend with configurable egress.
- Agent Working Directories: A think-style chat agent uses the Workspace as its scratch directory, so files it writes are durable across sessions and reachable by other backends.
- Prototyping Multi-tenant Runtimes: Preview surface for teams designing agent-runtime products on top of Durable Objects, without committing to a single backend design.
- Document Generation Pipelines: The tutorial builds one endpoint whose agent writes a markdown recipe card on the host and runs pandoc in the container to produce a PDF.
Experiential Labs
Experiential Labs
Open-source AI gateway that routes every model through one endpoint at provider cost, then improves that traffic with caching, routing and fine-tuning.
Key features
- Unified Model Endpoint: One OpenAI-compatible POST endpoint fronts every hosted provider, your own bring-your-own keys and your own GPUs, so switching models is a parameter change rather than an integration.
- Zero-Markup Routed Tokens: Routed traffic bills at the provider's list price with 0% added on top, with the company earning on hosted inference and the Pro plan instead of on your tokens.
- Model Recommendation from Traffic: The intelligence layer watches real request patterns and tells you when switching models would win, including newly released models on the day they ship, with optional per-prompt optimization.
- Caching Opportunity Detection: Identifies where cache hit rate could improve and shows the projected savings, with repeated tokens returning at 90% off once enabled.
- Traffic-Trained Custom Models: Fine-tunes a model on your own traffic and proves it in closed-loop simulation before it ever serves, then exposes it through the same endpoint you already call.
- Spend Attribution Console: Breaks requests and dollars down by agent, person, model, provider and day across the whole organization, alongside catalog, usage and limits.
- Live Request Logs and Metrics: Streams per-request time-to-first-token, token counts, provider, status and cost, with dashboard rollups for requests, spend, p50 TTFT and cache hit rate.
- Governance Controls: Budgets, provider allowlists and attribution are available from the free tier upward for controlling who can spend what on which models.
Best for
- Consolidating Multi-Provider Access: Replace separate SDKs and keys for OpenAI, Anthropic, Google and others with a single endpoint and key across every application.
- Cutting Inference Spend: Use caching recommendations and model-switch suggestions to lower the cost of an existing production workload without changing application code.
- Replacing a Frontier Model with a Small One: Distill or fine-tune a small model on your own traffic for a narrow repetitive task and serve it at a fraction of frontier-model cost and latency.
- Chargeback and Budgeting: Attribute AI spend to individual agents, teams or people for internal cost allocation and to enforce per-key budget caps.
- Evaluating New Model Releases: Compare a newly shipped model against your current one on your own traffic before committing to a migration.
- Hybrid Local and Hosted Serving: Route some workloads to self-hosted GPUs at zero marginal cost while sending the rest to hosted providers through the same interface.
- Self-Hosting the Gateway: Run the open-source gateway inside your own infrastructure when hosted routing is not an option.