For CFOs & Finance Leaders
Cut AI Agent Costs by Up to 10×
Swap in lower-cost models like DeepSeek, curate toolboxes to shrink the context window, cap spend per workspace, and stay vendor-independent — without giving up PII redaction, policy enforcement, or audit. Up to 10× cost reduction.
Four levers on the AI bill — without giving up governance
Every ContextGate proxy decouples the policy layer from the backing model. That lets you move cost without moving risk.
Swap in lower-cost models
Route any governed proxy to a cheaper backing model — DeepSeek-V3.1, open-source models on OpenRouter, or your own self-hosted endpoint. Same policies, fraction of the per-token cost.
Shrink the context window
Toolboxes only ship the MCP tool definitions an agent actually needs. Prompt baselines drop from 100k+ tokens to a fraction, on every call, forever.
Cap spend per workspace
Set a hard USD ceiling per workspace. When the cap is hit, new requests are rejected — no surprise overage on the model vendor invoice next month.
Stay vendor-independent
Policies, audit, and PII redaction live in ContextGate — not in the model vendor. Switch from OpenAI to Anthropic to DeepSeek without rebuilding the governance layer.
Stop paying for tool definitions your agent never calls
Wiring a raw MCP server into an agent dumps every tool definition into every prompt — even tools the agent will never use. ContextGate toolboxes let you pick the handful that matter and discard the rest. The savings compound across every call, every day.
- Salesforce MCP (full suite)38,400
- GitHub MCP (867 tools)41,200
- Slack MCP12,800
- HubSpot MCP14,600
- Linear MCP7,200
Every call ships this whole context. Pay for it on every turn.
- Salesforce: create_lead, update_opportunity1,800
- GitHub: create_issue, comment_on_pr1,400
- Slack: send_message450
Only the tools the agent actually needs. Same agent, smaller prompt.
Cheap models don't have to mean broken agents
When a cheaper model trips a policy check, ContextGate can auto-retry against the same model with the policy feedback injected — up to 3 attempts per rule. The agent fixes itself. No human in the loop, no rebuild every time you swap a backing LLM.
Summarise this customer call.…The customer, Sarah Jenkins (sarah.j@acme.com, +1-555-0142), is unhappy with…Policy warn · PII detected in output (EMAIL, PHONE)
Same prompt + policy feedback injected: "Remove customer PII (email, phone) before returning."(model regenerates against the same backing LLM)Policy feedback sent back into the model — no human in the loop
…The customer, [REDACTED_NAME] ([REDACTED_EMAIL], [REDACTED_PHONE]), is unhappy with…Policy pass · Response delivered to caller
Set a budget. Hit it. Stop.
Every workspace has a hard USD spend ceiling. When it's hit, ContextGate rejects new requests with a 402 — agents can't burn through the budget while nobody's looking. One workspace per team or business unit, one budget each.
Swap the LLM. Keep the governance.
Your PII redaction, policy rules, audit trail, and retry logic live in ContextGate — not in the model vendor. When a new model lands at 1/10th the price, you swap one config field. Same governance, same compliance posture, new bill.
Get in Touch
Ready to govern your AI agents? Let us know about your use case and we'll help you get started.