Cloudflare Computer vs Oxlo.ai: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Cloudflare Computer and Oxlo.ai — 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.
Oxlo.ai
Oxlo
Privacy-first inference platform to run Kimi K2.6, DeepSeek, and 45+ open-source models on a flat-priced, OpenAI-compatible API.
Key features
- OpenAI-compatible API: Drop-in API that serves 45+ open-source models so existing OpenAI client code works without rewrites.
- Flat monthly pricing: A fixed subscription instead of per-token billing, keeping inference bills predictable at any scale.
- Privacy-first inference: Zero data retention and no training on your data, so prompts and outputs stay private.
- Unlimited agentic tool calls: Run agent workflows with tool calling without metered per-call charges.
- Secure failover: Automatic routing and failover across models to keep agents reliable under load.
- Cost calculator: Compare your current inference spend against Oxlo and competing providers before committing.
- Broad model catalog: Access frontier open models like Kimi K2.6, DeepSeek V4 Flash, GLM-5, Llama, and Qwen plus Whisper, TTS, and image models.
Best for
- Building chatbots and AI assistants for support and internal tools on open models.
- Powering document Q&A and retrieval-augmented generation over PDFs and knowledge bases.
- Generating, rewriting, and summarizing text inside apps and internal systems.
- Running image understanding tasks such as classification and object detection.
- Cutting and stabilizing inference costs for AI teams with high, variable token usage.