Fly Your AI Infrastructure
on Autopilot.
Autonomous multi-cloud GPU cluster orchestration, multi-agent SRE fleet, intelligent token arbitrage, and sub-15ms semantic caching from a single unified developer API.
Pre-Configured & Compatible with Leading Cloud AI & Startup Accelerator Ecosystems
Test the Inference Speed & Semantic Cache
See how CloudPilot AI transparently intercepts, deduplicates, and routes AI requests in under 15ms.
import cloudpilot
client = cloudpilot.Client(api_key="cp_live_sec_99a8x")
@client.telemetry.on_threshold(metric="gpu_memory_pct", threshold=88.5)
def handle_memory_spike(event):
cluster = client.clusters.get(event.cluster_id)
return cluster.rebalance(target_latency_ms=12, spot_fallback="aws:us-east-1a:g5.12xlarge")
*Semantic Vector Hit: Cache verified in 3.8ms. Token synthesis bypassed.*
Interactive Cloud Cost Calculator
Calculate your exact monthly savings on AWS GPU compute and LLM token bills with CloudPilot AI.
Built for Modern AI Startups & ML Engineering
Eliminate GPU idle time, reduce token waste, and prevent out-of-memory cascading outages.
Autonomous GPU Orchestrator
Automatically claims spot instances across AWS, GCP, and CoreWeave. Shifts loads dynamically before AWS sends spot interruption notices.
Multi-Agent SRE Fleet
Autonomous AI agents patrol your Kubernetes pods 24/7. Detect memory leaks, rebalance GPU VRAM, and resolve latency spikes in under 800ms.
Sub-15ms Semantic Cache
High-dimensional vector embeddings cache query intents at edge POPs. Bypasses redundant LLM synthesis and slashes token costs by up to 68%.
Intelligent Token Arbitrage
Smart router balances Claude 3.5, GPT-4o, and Llama 3.3 dynamically based on prompt complexity, real-time cloud pricing, and SLA tier.
One Line Drop-In Replacement for Any LLM
No need to rewrite your backend codebase. CloudPilot AI provides OpenAI-compatible proxy endpoints, lightweight Python SDKs, and native Kubernetes operators.
- Zero change to prompt syntax or streaming handlers
- Built-in exponential backoff and multi-region failover
- Full telemetry tracing compatible with OpenTelemetry & Datadog
# pip install cloudpilot-sdk from cloudpilot import CloudPilotClient client = CloudPilotClient( api_key="cp_live_sec_99a8x", cache_policy="sub_15ms_aggressive", auto_failover=True ) # Autonomous inference with sub-15ms semantic caching response = client.chat.completions.create( model="claude-3-5-sonnet-20241022", messages=[{"role": "user", "content": "Optimize Kubernetes GPU manifests"}], temperature=0.2 ) print(f"Latency: {response.cloudpilot_metrics.latency_ms}ms") print(f"Cache Status: {response.cloudpilot_metrics.cache_status}") print(response.choices[0].message.content)
Applying for AWS Activate or Cloud Credits?
CloudPilot AI is 100% cloud credit friendly. Deploy on your existing AWS account and offset your GPU/EC2 usage directly with your AWS Activate credits while slashing your burn rate.
Frequently Asked Questions
Ready to Fly Your AI Infrastructure on Autopilot?
Join 450+ high-growth tech startups and engineering teams orchestrating reliable, low-cost multi-cloud AI clusters today.