model · Meta
LLM Application Development with Llama
LLM Application Development built on Llama, chosen where it genuinely fits, and swapped where it does not.
- Category
- model
- Vendor
- Meta
- Alternatives we also use
- 9
Why Llama for this
Orqent Labs builds LLM applications for teams that have outgrown the prototype and need something their compliance function will actually sign off on.
Llama is strongest at full control, no per-token cost, and viable air-gapped deployment. For llm application development that matters because the failure modes of this kind of system tend to cluster exactly there.
The honest trade-off: you own the infrastructure, the scaling and the evaluation work that a hosted API absorbs for you. We say that up front because a stack chosen for fashion rather than fit becomes someone's migration project two years later. Integration comes before intelligence. A model that cannot reach your systems of record is a demo with good manners.
We hand over with runbooks, tests and a team that knows how it works, not a dependency.
The honest assessment
- What it is
- Open-weight models you can host yourself, the default when data cannot leave your building.
- Strongest at
- full control, no per-token cost, and viable air-gapped deployment
- Trade-off
- you own the infrastructure, the scaling and the evaluation work that a hosted API absorbs for you
- Category
- model
We are not a reseller for Meta and hold no commission on this choice. Where a different option fits your workload better, the recommendation will say so. That is the entire value of asking us.
What is included
- Model selection and routing across providers
- Prompt architecture with versioning
- Structured output and schema validation
- Evaluation suite run on every change
- Token cost monitoring and budget controls
- Streaming UX and graceful degradation
Questions
Which model should we use?
It depends on the task, not on the leaderboard. We benchmark your actual workload across providers and usually end up routing, a strong model for reasoning, a cheaper one for classification and extraction.
How do you control the token cost?
Caching, routing, prompt compression and hard budget ceilings, with per-feature cost monitoring so a runaway loop shows up in hours rather than on the monthly invoice.
Can you work with our existing codebase?
Yes. Most of our LLM work lands inside an existing product rather than as a greenfield app, and we match the conventions already in your repository.
Alternatives for llm application development
Same capability, different stack. Each page states its own trade-off.
What else we build on Llama
Building with Llama?
Bring us the workload and we will tell you whether this is the right stack for it.
Or email bd@dtrasglobal.com · call +91 74118 77878