Deploy specialized AI models trained on your data, running entirely within your infrastructure. 100x lower cost than cloud LLMs. Zero data exposure.
Cloud LLM APIs cost $30K–$60K per million queries. At enterprise scale, AI budgets reach hundreds of thousands annually.
Sending sensitive infrastructure data to external APIs creates compliance nightmares and security vulnerabilities.
GPT-4 doesn't understand your service names, topology, or runbooks. It can't interpret your organization's context.
AppLeap deploys small, specialized AI models—each optimized for a specific task—trained on your data, running entirely in your environment.
Most AI solutions fail because they focus on model size and data quantity. We take a fundamentally different approach: quality over quantity.
Our proprietary tools analyze, validate, and enrich your operational data before training. We extract meaningful patterns and relationships that generic models miss.
We train models to understand causation, dependencies, and context—not just pattern matching. This means accurate insights on situations they've never seen before.
High-quality training data means smaller, faster models that outperform bloated alternatives. Less compute, lower costs, better results.
"1,000 high-quality examples outperform 1,000,000 mediocre ones. We've built the tools to ensure every piece of training data counts."
"Customer data never touches external AI services."
| Capability | Cloud LLMs | Rule-Based AIOps | AppLeap |
|---|---|---|---|
| Data Privacy | External | ✓ On-prem | ✓ On-prem |
| Natural Language | ✓ Full | Limited | ✓ Full |
| Org-Specific Training | Generic | Rules only | ✓ Custom models |
| Cost per 1M Queries | $30,000+ | ~$1,000 | $200–500 |
| Deployment Time | N/A | Weeks | Hours |
| Multi-tool Integration | ✓ Yes | ✓ Yes | ✓ Yes |
Join our early access program. Limited spots for mid-market IT teams.