AI Model & Inference Operations
Put your AI models to work
Deploy open and custom models on the infrastructure you choose. Connect inference to your applications and agents, while Langstack handles deployment, scaling, security, and operations.
Companies we’ve helped
- Coloplast
- Lokal Forsikring
- 3
- SK Energi
- Mindshare
- Energi Fyn
- Røde Kors
- OK
- Telenor
- Envafors
- Frederikshavn Forsyning
- Vestforsyning
- Andel Energi
- APC Forsikringsmæglere
- Bonnier
- Cognito · Bording Group
- CA a-kasse
- Call me
- Egmont
- Falck
- Læger uden Grænser
- Modstrøm
- Roskilde Kommune
- SEAS-NVE
- Storm Group
- Goodwin Company
- Verisure
- Visma e-conomic
- Dantaxi
- Proactiv
A working foundation for model inference
Put your models into production
Deploy open and custom models for language, vision, audio, and predictive tasks. Make them part of your everyday applications.
Match resources to the workload
Choose resources for your model, response-time requirements, and expected usage. Adjust capacity as demand changes.
Choose where inference runs
Run inference on cloud resources, dedicated servers, or supported edge devices. Choose the location that fits your data and operational needs.
Connect models to real work
Connect models to applications, agents, and business systems through Langstack identities. Put model outputs to work in your workflows.
Add enterprise context
Use Langstack Knowledge to ground models and agents in your company’s information, with hybrid retrieval and governed access.
Simplify model operations
Let Langstack handle deployment, scaling, security, and monitoring. Manage inference alongside the services your applications depend on.
Production AI, connected to your enterprise
Run open and custom models, individually or in ensembles, on your own inference clusters. Combine infrastructure providers to suit each workload, with native connections to enterprise knowledge, applications, workflows, and agents.
Enterprise Knowledge
Your production AI
AI models
Open models
Custom models
Compose inference clusters for your workloads
LLM cluster
Prediction cluster
Vision cluster
On-premise
Applications
Workflows
AgentsTaking AI models into production
Is private AI inference limited to language models?
No. Different workloads can use language, vision, audio, tabular, forecasting, or custom models. Select models and inference resources around the task, and connect their outputs to the application or workflow that uses them.
Can different workloads use different inference clusters?
Yes. Compose clusters around the models, latency, capacity, and deployment locations each workload needs. Langstack connects inference to your knowledge, applications, and agents while managing deployment and cluster operations.
How do we assess hardware needs and operating costs?
Start with the model, input sizes, expected concurrency, and response-time requirements. Benchmark a representative workload before choosing capacity. Hardware and model choices affect cost and performance; moving a workload to private infrastructure is not automatically cheaper.
Discuss your inference workloadA foundation for Zero Trust
Zero Trust makes access explicit, limited and subject to reassessment. Langstack’s identity and networking controls provide a foundation for applying these principles to applications, models and agents.
Explore Security- Explicit verification
- Verify identities and authorize access to each resource. Reassess access as context and security signals change.
- Least-privilege access
- Grant users, workloads and agents only the access their task requires. An agent’s decision to use a tool does not itself grant permission.
- Assume breach
- Design for the possibility that a workload or identity is compromised. Isolate workloads, limit lateral access, and monitor activity to help contain the impact.
“From complex integrations to intricate data processes. Langstack handles it all quickly, professionally and with measurable results.”
Thomas Emil MortensenSenior Lead Manager, Telenor
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