Control the cost per
completed task.
Palma.ai reduces token waste and tool-call loops by making MCP agents more accurate, more efficient, and measurable—so costs stay predictable as usage grows.
Efficiency, not guesswork
How Palma.ai reduces cost
Reduce wasted tool calls
Palma.ai limits tool sprawl and encourages focused capability exposure so agents don't attempt dozens of irrelevant calls before succeeding.
Shorten multi-hop chains
Palma.ai supports composing tool sequences into fewer, higher-signal capabilities. Fewer hops = less context = lower cost.
Improve accuracy to cut retries
The most expensive agent is the one that fails and retries. By improving task success, Palma.ai reduces the compounding cost of multi-hop drift.
FinOps visibility for MCP
Track token usage, tool-call counts, retries/failure rates, and cost per completed task by agent, tool, and workflow.
The problem
Agent cost rarely fails "a little." It fails catastrophically:
Multi-hop loops inflate tokens and tool calls
Agents retry when interfaces are ambiguous or too broad
No visibility: bills arrive, but root causes are unclear
Every new agent doubles cost variance
FinOps visibility
See where your spend goes
Cost / Task
Token Savings
Tool Calls
Success Rate
What "good" looks like
Cost measured as € per completed business outcome, not tokens alone
Tool use constrained so agents don't "thrash"
Clear allocation: which agents, which tools, which workflows drive spend
Controls that scale alongside adoption
Outcomes
Lower cost per completed task
More predictable spend as you scale
Faster identification of "cost leaks" (tools, workflows, teams)
Better ROI narrative for executive stakeholders
Make agent spend predictable—and worth it.
Use Cases
What are you solving for?
Different problems, one platform. See how Palma.ai addresses your specific challenges.
Scaling Agents
Pilots work, but enterprise rollout breaks.
One MCP layer for the whole enterprise.
Cost of Agents
Token waste and tool-call loops spike costs.
Predictable cost per completed task.
Agent Reliability
Multi-hop drift and tool boundary failures.
Agents that finish work correctly.