Diagnose
Start with a spend review and prioritized recommendations. Use the traces and usage data your team can share.
THE OPEN45 PRODUCT
Understand the cost.
Change the outcome.
A cost control layer for teams running AI agents. Start with a hands-on spend review. Build toward cost-aware deployment and quality-checked fixes.
STARTING WITH FOUNDER-LED REVIEWSA useful answer to
A useful answer to
“why did our bill go up?”
A higher bill can mean more customers—or an agent that’s doing unnecessary work. Open45’s approach separates volume, token usage, model mix, and pricing so you know what deserves attention.
- Identify repeated context, retries, and tool overhead
- Rank opportunities by estimated dollars, not noise
- Connect each finding to a concrete next step
EXAMPLE FINDINGContext growth
Repeated history is driving up cost per task.
Step 01
Step 02
Step 03
Step 04
PROPOSED CHANGE
Keep the task state. Summarize older history. Replay representative tasks before rollout.
Illustrative output · Quality validation required
A PRODUCT BUILT IN STAGES
Earn trust. Then get closer to the action.
Start here
Prevent
Planned: replay representative tasks in CI and flag changes that increase cost per task without improving quality.
Optimize
Planned: propose fixes as pull requests, with supporting evidence and quality checks before your team approves them.
Control at runtime
Longer term: apply proven budgets, retry caps, caching, and model-routing policies as your agents run.
FITS THE WAY YOU WORK
Your traces stay the starting point.
Our planned data sources include Langfuse, LangSmith, OpenTelemetry, and LiteLLM. We’re building around the tools teams already use, with initial review scope agreed directly.
Let’s find your next cost-saving fix.
Start with a conversation about your agents and your spend.