Enterprise AI. No vendor lock-in
Deploying open-source LLMs with the right balance of performance, security, and freedom.
Why open wins
Freedom. Flexibility. Fit
Open-source LLMs offer real advantages:
Gartner forecasts that by 2026, 50% of enterprise GenAI deployments will adopt open-source models for mission-critical tasks
- 01
Local and on-premise deployability
- 02
Transparent model weights and behavior
- 03
Customizable with domain data
- 04
Lower TCO and no API usage fees
- 05
Independence from throttling or region restrictions
What we integrate
Not just the model
Calsoft builds full-stack, open LLM pipelines:
Security first
Private. Compliant. Enterprise-grade
We ensure:
Paying too much for closed-model APIs?
Proven outcomes
Measurable ROI without lock-in
| KPI | Closed-source | Open-source |
|---|---|---|
| Cost per 1M tokens | $0.12–$0.25 | ~$0.005 |
| On-prem deployment | Not available | Fully supported |
| Response latency | 3–5s | 1.5–2s |
| Governance control | Minimal | Full-stack |
| Model customization | Limited | Deep (with LoRA, QLoRA) |
How to start
Integrate open models in 4 steps
Select the use case
Identify LLM-enabled flows—knowledge assistants, summarization, query rewriting, copilots, etc.
Evaluate the model fit
Benchmark open-source models (LLaMA 2, Mistral, etc.) based on latency, accuracy, infra, and licensing.
Build & integrate stack
Connect model to embedding, vector DB, RAG, UI layers. Use LoRA/QLoRA for domain alignment.
Deploy, monitor, secure
Launch within VPC/on-prem. Add observability, feedback loop, and governance controls.
