Dedicated AI inference infrastructure, optimized for cost and latency.
Latens provides OpenAI-compatible inference APIs, private model operations, and dedicated managed deployments across EU, APAC, and US infrastructure. Region selection is deployment-specific and agreed during onboarding.
Built by infrastructure operators.
The team behind Latens has spent 5 years operating performance-sensitive infrastructure: distributed systems, high-availability nodes, latency-sensitive operations, monitoring, and cost-efficient compute. Latens brings that operational discipline to AI inference.
Distributed systems experience
Infrastructure designed for deployment-specific, latency-sensitive workloads across supported regions.
Operational reliability
Monitoring, incident response, production support workflows, and a 99.95% monthly availability SLO for covered dedicated production deployments.
Cost-aware compute
Serving strategies focused on efficiency, utilization, and predictable inference costs.
Inference services for production AI teams.
Three deployment models — pick what matches your security, performance, and cost requirements.
Hosted Open-Weight Model Inference
Access production-ready open-weight and open-source models through an OpenAI-compatible API. Integrate with familiar SDKs while reducing provider lock-in and improving cost control.
- OpenAI-compatible API endpoints
- Access to open-weight and open-source LLMs
- Model benchmarking and selection
- Token cost optimization
- Usage visibility and performance monitoring
Private and On-Prem Model Operations
Deploy and operate models inside customer-controlled environments for teams with strict security, compliance, privacy, or data residency requirements.
- On-prem or private cloud deployment
- Model serving setup and management
- Monitoring and operational support
- Cost and latency tuning
- Data control and deployment isolation
Dedicated Managed Inference
Run dedicated model deployments managed by Latens for predictable performance, workload isolation, and production-grade reliability.
- Dedicated capacity
- Fully managed model serving
- Deployment-specific regional options
- Performance and latency optimization
- Custom operational support
Open-weight and open-source models.
Run curated open-weight and open-source models through an OpenAI-compatible API. Exact model availability, tokenizer behavior, context limits, and deployment configuration are provided during onboarding.
Deployable model families
Supported model families may include Qwen, DeepSeek-derived distilled models, Mistral, Gemma, and other open-weight or commercially deployable models, subject to license review, hardware fit, and deployment-specific approval. Exact model IDs are provided during onboarding.
Distributed infrastructure across EU, APAC, and US.
Latens provides inference deployment options across Europe, Asia-Pacific, and the United States, helping teams place model workloads closer to their users and infrastructure. Region selection is deployment-specific and agreed during onboarding.
European deployment options for low-latency access and regional deployment needs.
Asia-Pacific deployment options for globally distributed AI applications.
North American deployment options for high-capacity inference workloads.
Token cost optimization is an infrastructure problem.
Efficient inference depends on more than the price per token. Latens helps teams optimize model choice, serving architecture, continuous batching, context usage, regional placement, and infrastructure utilization.
Model selection
Match workloads to models benchmarked on quality, latency, and cost.
Prompt and context efficiency
Reduce wasted tokens with structured prompts and context discipline.
Runtime optimization
Continuous batching and non-persistent in-request KV-cache reuse to improve throughput without retaining prompts, completions, or result caches after request completion on ZDR production endpoints.
Regional placement
Place deployments in the agreed region and capacity pool for each workload.
Throughput and batching
Continuous batching and serving tuning for higher utilization.
Dedicated vs shared analysis
Choose the right deployment shape for each workload profile.
OpenAI-compatible by default.
Use Latens with familiar OpenAI-compatible clients and minimal application changes.
import OpenAI from "openai";
const client = new OpenAI({
apiKey: process.env.LATENS_API_KEY,
baseURL: "https://api.latens.ai/v1"
});
const completion = await client.chat.completions.create({
model: "your-enabled-model-id",
messages: [
{
role: "user",
content: "Explain inference cost optimization."
}
]
});Model IDs are deployment-specific and are provided during onboarding. Do not use placeholder model IDs in production.
Drop-in API
Swap baseURL — keep your existing SDKs, prompts, and tooling.
Standard endpoints
Chat-completions style APIs and streaming inference on enabled models. Additional endpoints are available only when explicitly enabled for a deployment.
Observability
Per-request metrics for latency, token usage, and cost attribution.
Token accounting
Token usage is counted with the model-native tokenizer used by the deployed model. Latens uses llama.cpp tokenization for deployed models, including chat-template and special-token handling for prompt tokens and generated token IDs for completion tokens.
Production commitments
Covered dedicated production inference deployments are operated against a 99.95% monthly availability SLO. Covered production incidents receive a 6-hour initial response Support SLA through the designated support contact. The availability SLO is an operational target unless a signed agreement states otherwise.
Evaluate Latens for your workload.
Talk to our team about hosted, private, or dedicated deployments.