July focused on helping teams control AI spend in production without sacrificing workflow flexibility. We shipped spending limits and cost guardrails at the workflow and project level, multi-step event-driven pipelines, a cleaner workflow builder, better request log filtering, and free public cost calculators.
AI spending limits and cost guardrails
We added spending limits so teams can cap AI costs before a runaway bill shows up. Limits work at the model slot level inside a workflow and at the project level across production traffic.
What shipped:
- Per-model spend caps with hourly, daily, weekly, and monthly periods
- Soft budget enforcement that fails over to backup models instead of hard-stopping traffic
- Project-level spend caps shared across production workflows
- Spend visibility on overview and workflow cards with alerts at 80% and 90%
- Budget guardrail events in request logs
See spending limits for configuration, the AI spending limits overview for how guardrails fit production workflows, and cost analytics for tracking spend over time.
Multi-step event-driven workflows
ModelRiver now supports chained backend webhooks and callback AI steps in a single workflow. Teams can build pipelines where AI generation, backend processing, and follow-up AI steps run in sequence without stitching together separate integrations.
What shipped:
- Pipeline editor and preview in the console
- Playground support for testing multi-step flows end to end
- Documentation for chained steps and callback patterns
Follow the multi-step pipeline guide for setup details, or start with the event-driven AI overview. Test callbacks locally with the CLI callback command.
Workflow builder and playground UX
The workflow console got a full redesign to make complex setups easier to scan and edit. Creating and tuning workflows should feel closer to editing a product surface than wiring infrastructure.
What shipped:
- Fullscreen workflow editor and cleaner workflow list cards
- Simplified create wizard with Primary model and Backup models steps
- Drag-to-reorder models in the failover path
- Playground model pinning to test a specific workflow slot
Start in the console, or follow build a workflow and Test Mode docs to try changes safely before production.
Request logs improvements
Request logs are easier to use when debugging production traffic or tracing a failed workflow run. Filtering and navigation got tighter so teams can find the right request faster.
What shipped:
- Source and endpoint filters via request URL type
- Timeline refresh and clearer back navigation between log views
- Improved browsing when moving between related requests
Read request logs for the full reference, or timeline observability for how events appear across multi-step flows.
Public AI cost calculators
We published free calculators on the marketing site so teams can estimate token usage and model cost before signing up. Both tools use the current provider catalog.
- Token calculator for estimating input and output tokens
- AI pricing calculator for comparing model cost across providers
Also this month
We refreshed the provider catalog so routing, credential checks, and public pricing data stay aligned with current working models.