Production-ready model serving without the infrastructure headache
| Option | Problem for SMEs |
|---|---|
| OpenAI / Anthropic | Too expensive at scale |
| Together / Replicate | Still expensive, adds margin |
| AWS SageMaker | Complex setup, vendor lock-in |
| Self-host (vLLM) | Requires infra expertise you don't have |
You need AI that's cheaper, simpler, and reliable.
Synkti runs your AI models on spot instances at 10x less cost—without the reliability headache.
Spot instances at a fraction of on-demand cost, with automatic failover
No config hell, no Kubernetes, no PhD required
Not an academic prototype—built for real workloads
Your app → Synkti API → Spot workers (vLLM) → Response
↓
Auto-discovery & routing
Spot termination handled
Workers replace themselves
You never think about infrastructure. You just get completions.
Simple, transparent pricing based on actual compute cost.
Qwen, Llama, Mistral
Llama-13B, Qwen-14B
Llama-70B, Qwen-72B
Running Qwen-7B costs ~$360/month on-demand. With Synkti spot: ~$36/month.
Ready to cut your AI inference costs by 10x?
Include in your email: Model size, expected volume, compliance requirements
Synkti is developed by Bobby Mathews, AI infrastructure researcher with expertise in distributed systems and spot instance economics.
Yes. Synkti handles spot terminations transparently. Workers monitor for termination notices, notify the orchestrator, and continue serving until AWS terminates. Your requests route to healthy workers automatically.
Minimal. Most requests complete in <500ms. Routing adds ~10ms.
We support any model compatible with vLLM (Llama, Qwen, Mistral, etc.). Specify your model when you sign up.
Your data stays in your AWS account. We provide the orchestration layer.
The orchestrator is open source. GitHub: bobby-math/synkti