Stop overpaying
for AI inference
Setta analyzes your AI usage and automatically optimizes costs, so you can build smarter without spending more.
- Cached prefix hits−$0
- Tool-def bloat removed−$0
- Agent workflow compiled−$0
- Sub-task contexts isolated−$0
- Tool patterns collapsed−$0
If we don't save you money, you don't pay.
Your engineers are fighting costs
instead of building product.
Understand first. Then optimize.
Map Your Usage
Connects to your AI infrastructure and monitors every API call, mapping how models are used across your product to find the patterns of waste specific to your workload.
Prove savings before applying
Setta replays your traces against proposed optimizations and projects the cost impact without spending a single token on a real model. Every recommendation comes with a defensible savings estimate, so nothing reaches production unless we can prove it works.
Track the Frontier
New models, new APIs, new pricing, new optimization techniques. The landscape shifts monthly. Setta tracks all of it and adapts continuously, so your team never has to.
Today's techniques. Tomorrow's will be different.
Prompt-Cache Optimization
Providers ship real prompt-cache discounts now (up to 90% off on hits), but most teams leave them on the table. The wins take the right prompt shape, the right tool placement, and knowing which workloads have cacheable patterns at all. Setta audits your traces for the missed hits and shows you what to fix: broken prefixes, tool defs inlined wrong, calls where nobody noticed caching was an option.
Agent Workflow Compilation
Agents redo the same tool sequences run after run, paying full LLM cost for routine logic. Compilation turns the repeats into deterministic shortcuts that bypass the model.
Semantic Tool Filtering
You pay for every tool definition you send, even ones the model never reaches for. Filtering sends only the tools this request actually needs.
Sub-Agent Delegation
Every step of a long-running agent pays for its full accumulated context. Sub-agents work in isolated contexts and return only the final answer, so the parent never carries their intermediate work.
Trace-Based Meta-Tools
When the same primitive tool calls recur in stable patterns, they're really one higher-level operation in disguise. Meta-tools collapse the pattern into a single call.
Point solutions vs. Setta
Point Solutions
Setta
Common questions, answered.
What is Setta?
Setta is an AI cost optimization platform for companies building with large language models. It looks at how your application uses AI, identifies waste in your specific workload, and keeps applying new optimizations as they prove out. The optimization landscape changes weekly; Setta tracks it so your engineers don't have to.
How does Setta reduce AI costs?
Setta starts by mapping how your product calls models in production. That analysis surfaces waste patterns (broken prompt-cache prefixes, redundant tool definitions, recurring tool-call sequences, parent agents dragging bloat through every step). From there, Setta replays your traces against proposed optimizations and projects the cost impact without spending tokens on a real model. Only the optimizations that proved out on your workload get applied. Typical savings reach 90%.
How does Setta's pricing work?
Performance-based. Setta only gets paid when it saves you money. If we don't save you money, you don't pay. The fee is a percentage of the verified savings, so we make money when you do.
How is Setta different from a model router or LLM gateway?
Routers pick a model per request. Gateways add observability or routing on top of provider APIs. Both commit to one technique and apply it universally. Setta is workload-aware: it analyzes your traces to pick what helps your specific product, drawing from a stack of techniques (prompt-cache opportunities, agent-workflow compilation, tool filtering, sub-agent delegation, trace-based meta-tools). It proves savings on your traces before anything reaches production, and it optimizes total cost (input plus output), not just input tokens.
Will Setta hurt my AI's quality or performance?
No. Setta doesn't route to cheaper models, and it doesn't delete context the model needs. We cut waste, not capability. Prompt-cache optimization captures provider discounts on the same model and the same prompt. Tool filtering sends only the tools your traces show the model reaches for. Sub-agent delegation isolates work in clean contexts so the parent never drags around the sub-agent's intermediate state. Workflow compilation only skips the LLM on tool sequences that have already proven deterministic. The reasoning the model does on your problem is unchanged.
What is prompt-cache optimization?
Anthropic, OpenAI, Google, and DeepSeek now offer prompt-cache discounts of up to 90% on cache hits. Capturing them isn't automatic. The prompt has to be shaped right, the tools have to live in the right place, and plenty of workloads have cacheable patterns nobody noticed. Setta audits your traces for the misses and tells you what to fix: broken prefixes, tool definitions inlined where they shouldn't be, calls where caching was never considered in the first place.
Will I have to change my code to use Setta?
No. Setta plugs into trace data you already have (LangSmith, Langfuse, or direct provider logs) and doesn't need any code changes. Most teams are integrated in under five minutes.
What models, frameworks, and tools does Setta work with?
Models: GPT-5.5, Claude Opus 4.7, Gemini 3.1 Pro, Llama 4, DeepSeek V4, Grok 4.3, Mistral Small 4, Qwen 3.6 Plus, Kimi K2.6. Agent platforms: LangGraph, CrewAI, Mastra, OpenAI Agents SDK, AutoGen, Google ADK, LlamaIndex, Pydantic AI, Dify, Smolagents, n8n. Coding harnesses: Claude Code, Cursor, Copilot, OpenClaw, OpenAI Codex, Kilo Code, Cline, Devin, Antigravity, Kiro, Windsurf, Aider, Zed.
Is Setta available now?
Setta is in early access. We're onboarding design partners now. Join the waitlist at setta-ai.com.
Cut your AI bill
starting today
We're onboarding design partners now. Join the waitlist and we'll reach out to discuss your setup.