AI/ML Platforms
Reliable event delivery for AI workflows that can't afford to miss a beat
AI workloads are asynchronous by nature. Convoy ensures every training completion, inference result, and pipeline status change reaches the right system at the right time.
Why async AI workloads need dedicated webhook infrastructure
AI pipelines are event-driven at their core. When webhook delivery fails, your entire workflow stalls.
Long-running jobs need reliable completion signals
Model training runs for hours or days. When a run completes, or fails, downstream systems need to know immediately. A missed webhook means wasted GPU time, stalled pipelines, or delayed deployments.
Inference results must reach customers
When your platform processes an image, generates text, or runs a prediction, the result needs to be delivered to your customer's system. Dropped webhooks mean customers see incomplete data and file support tickets.
Pipeline orchestration depends on events
Modern ML pipelines chain together data ingestion, preprocessing, training, evaluation, and deployment. Each stage signals the next via events. Unreliable delivery breaks the chain.
Customers expect self-service
AI platform customers want to configure their own webhook endpoints, filter by event type, and debug delivery issues themselves. Building this management layer from scratch is a distraction from your core product.
Webhook infrastructure built for AI-scale workloads
Convoy handles the event delivery plumbing so your team can stay focused on models, not infrastructure.
Guaranteed delivery for async results
Convoy ensures every model training completion, inference result, and pipeline event is delivered, even if the destination is temporarily unavailable. Automatic retries with backoff handle transient failures.
Event type filtering
Let your customers subscribe only to the events they care about: model.training.completed, inference.result.ready, or pipeline.stage.failed. Fine-grained subscriptions reduce noise and improve integration quality.
Message broker integration
Ingest events from Kafka, Amazon SQS, Google Pub/Sub, or RabbitMQ. Convoy bridges your internal event bus to external webhook delivery, so your ML pipeline doesn't need to speak HTTP.
Developer portal for your customers
Give your AI platform users a self-service portal to register endpoints, view delivery logs, retry failed events, and manage their webhook configuration, embeddable directly in your dashboard.
High-throughput event processing
AI platforms generate massive volumes of events, from batch inference jobs to streaming predictions. Convoy's architecture handles billions of events without breaking a sweat.
Payload flexibility
Send event references for large model outputs, or include result summaries directly in the payload. Convoy supports flexible payload structures to match your platform's needs.
Frequently asked questions
Can Convoy handle the high event volumes from batch inference jobs?
Yes. Convoy's control and data plane architecture is built for high throughput. Whether you're processing thousands of inference results per minute or handling burst traffic from batch jobs, Convoy scales automatically.
How does Convoy integrate with our existing event bus?
Convoy natively ingests events from Kafka, Amazon SQS, Google Pub/Sub, and RabbitMQ. Your ML services publish events to your broker as they normally would, and Convoy handles the external webhook delivery.
Can our customers filter which AI events they receive?
Absolutely. Convoy supports fine-grained subscription filtering, your customers can subscribe to specific event types, and even filter based on payload content (e.g., only receive events for a specific model ID).
What about large inference result payloads?
We recommend sending lightweight event notifications via webhooks with a reference ID, then having the recipient fetch the full result via your API. This pattern keeps webhook delivery fast and reliable regardless of result size.
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