Oxlo.ai vs Trigger.dev: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of Oxlo.ai and Trigger.dev — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

Oxlo.ai

Oxlo

Freemium

Privacy-first inference platform to run Kimi K2.6, DeepSeek, and 45+ open-source models on a flat-priced, OpenAI-compatible API.

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.

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.

Trigger.dev, Inc.

Freemium

Open-source TypeScript platform for durable AI agents and long-running workflows with no timeouts, plus queues, retries, and observability.

No-timeout task runtime: Tasks run for as long as they need — hours if necessary — unlike Lambda or Vercel functions, making it usable for long-running agents and heavy batch jobs.
Durable AI agents: Chat agents survive tab closes, refreshes, redeploys, and crashes because their execution state is checkpointed by the platform.
Streaming to the frontend: Stream tokens or intermediate step output straight to your UI with no extra API routes to build or maintain.
Tool calling and human-in-the-loop: First-class primitives for LLM tool calls and for pausing runs on human approval before continuing.
Queues, retries, idempotency: Built-in job queues, retry policies, and idempotency keys so you don't hand-roll reliability around every AI call.
Self-host or managed cloud: Apache 2.0 core with a documented self-hosting path, plus a managed cloud for teams that want elastic scale without ops.

Long-running chat agents: Support or research chat agents that keep working across sessions and stream results back to the browser once the user returns.
Multi-step LLM pipelines: RAG pipelines that fan out to hundreds of documents, retry failed calls, and finish minutes or hours later without a client staying connected.
Human-in-the-loop workflows: Agents that draft output, pause for a human approval step in Slack or a web UI, and resume automatically once approved.
Batch AI processing: Nightly jobs that classify, embed, or transform thousands of records with automatic queueing and observability.
Backend for autonomous agents: Serves as the durable execution layer for autonomous agents built with the OpenAI Agents SDK, Vercel AI SDK, or custom orchestration.