Open-Source LLM Deployment
FISTA Solutions puts open-weight models into production properly: candidate selection evaluated on your own tasks, a serving stack tuned for your traffic, deliberate quantization trade-offs, evaluation that catches regressions, and an upgrade path for when a better model appears next quarter.
- 150+
- projects delivered
- 50+
- companies served
- 99.9%
- verified uptime
- 47%
- efficiency gains
- 12+
- countries reached
What we build
What does open-source LLM deployment include?
Open-weight deployments cover candidate evaluation on your golden set, quantization and precision decisions with measured quality impact, serving stack configuration, gateway integration, monitoring, and a documented path for adopting newer models.
- 01
Candidate evaluation
Shortlisted models scored on your own tasks, with quality, latency, and cost compared side by side.
Selection - 02
Quantization decisions
Precision trade-offs measured rather than assumed, because quality loss varies by model and task.
Trade-offs - 03
Serving configuration
Batching, concurrency, and memory settings tuned to your prompt and output length distribution.
Serving - 04
Gateway integration
An API layer so applications are decoupled from the specific model and serving implementation.
Integration - 05
Upgrade path
Evaluation and rollout process for adopting newer models without an application rewrite.
Lifecycle
Requirements
Which requirements shape open-source LLM deployment?
Open-weight deployment is an engineering exercise in measurement. Requirements cover task-level quality evaluation, honest quantization trade-offs, serving throughput under real traffic, licence compliance, and a rollout path for constant model churn.
| Requirement | Why it matters | How FISTA implements it |
|---|---|---|
| Task-level quality | General benchmarks mislead. | Evaluation on your golden set per candidate, with results reported by task rather than as one score. |
| Quantization impact | Quality loss is task-dependent. | Quantized variants evaluated on the same golden set, so the trade is chosen with evidence. |
| Throughput | Serving efficiency determines cost. | Load testing with your real prompt and output length distribution, tuning batching and concurrency. |
| Licence compliance | Open weights carry varied licence terms. | Licence review per model against your commercial use, with terms documented for legal review. |
| Model churn | Better models arrive constantly. | A repeatable evaluation and rollout process so adopting a new model is routine rather than a project. |
Where AI fits
How should you sequence open-source LLM deployment?
Adopt open-weight models where evidence supports them: define the tasks, evaluate candidates honestly including quantized variants, then deploy behind a gateway so the inevitable next model is a configuration change.
- 01
1. Define the tasks
Golden cases from your real workload, because model choice is task-specific rather than general.
- 02
2. Evaluate candidates
Shortlist scored on quality, latency, and cost, including quantized variants.
- 03
3. Check licences
Licence terms reviewed against your commercial use before engineering commits to a model.
- 04
4. Deploy behind a gateway
Applications decoupled from the model so swapping is configuration rather than code.
- 05
5. Re-evaluate on a cadence
A repeatable process for assessing new releases, because the frontier moves quarterly.
Cost and timeline
What does open-source LLM deployment cost, and how long does it take?
Cost is driven by serving efficiency and capacity rather than by licence fees; timeline by evaluation and capacity provisioning. FISTA does not quote blind: the scoping call returns an evaluation plan and a serving architecture.
Open weights remove licence cost and add infrastructure and operational cost. Whether that trade wins depends entirely on volume and utilization, which FISTA models against your actual traffic.
Evaluation is the recurring investment that makes open-weight strategy work. A repeatable golden-set process turns each new model release from a research project into a routine comparison.
Send the scope you have, even if it is a paragraph. You get a written brief, an architecture sketch, and a phased estimate before any commitment.
Get a scoped quoteDelivery
How does FISTA deliver an AI deployment?
FISTA deploys AI in four phases: an assessment that inventories workloads, data boundaries, and constraints and produces a target architecture; a platform build with networking, identity, secrets, and observability as code; a migration with evaluation gates and shadow traffic; and a production cutover with dashboards, budgets, runbooks, and rollback.
- 1
Assess and target
Workload inventory, data classification, latency and volume profile, compliance constraints, and a target architecture with cost model.
OutputTarget architecture, cost model
- 2
Build the platform
Networking, identity, key management, model endpoints, gateway, tracing, and evaluation pipeline delivered as infrastructure-as-code.
OutputPlatform as code, control matrix
- 3
Migrate with gates
Move applications behind the gateway, run evaluation and shadow traffic, and tune routing, caching, and capacity.
OutputEval reports, shadow results
- 4
Cut over and operate
Graduated production rollout, dashboards for quality, latency, and cost, runbooks, on-call, and a change process with rollback.
OutputProduction platform with SLOs
Why FISTA
Why choose FISTA Solutions for open-source LLM deployment?
FISTA selects open-weight models by measurement on your tasks, treats quantization as an evidence-based trade, and builds an upgrade path for constant model churn. Work is contracted through a US entity with full IP assignment.
Open-Source LLMs specifics
- Candidates are scored on your own golden set by task, not ranked from public benchmarks.
- Quantized variants are evaluated on the same set, so precision trade-offs are chosen with measured quality impact.
- Licence terms are reviewed against your commercial use before engineering commits to a model.
- A repeatable evaluation and rollout process makes adopting newer models routine rather than a project.
How FISTA engineers
- Spec-Driven Development: every deliverable starts as a written specification with acceptance criteria, so scope is testable before it is built.
- AI-native delivery: engineers direct coding agents under review gates and evaluation harnesses, compressing build time without loosening verification.
- Official Anthropic partner, with production experience across Claude, OpenAI, Google, and open-weight models, chosen per workload rather than by default.
- One accountable delivery lead, weekly demos on your environment, and code in your repositories from week one.
What you get as a client
- 150+ projects delivered for 50+ companies across 12+ countries since 2017, with 99.9% verified uptime on systems we operate.
- A US entity (FISTA Solutions Inc., Wilmington, Delaware) for contracting, invoicing, and IP assignment, with an engineering center in Faisalabad, Pakistan for cost-efficient senior capacity.
- US business-hours overlap for standups and reviews; written decision logs so nothing depends on a meeting you missed.
- Flexible engagement: fixed-scope build, embedded forward deployed engineers, or a dedicated team that you can scale month to month.
Clear answers
What platform teams ask before deploying AI.
Straightforward guidance for evaluating scope, fit, and the next step.
01Which open-weight model is best?
The one that scores best on your tasks at acceptable latency and cost, which differs by workload. FISTA shortlists candidates and evaluates them on your golden set rather than recommending from a leaderboard.
02Does quantization hurt quality?
Sometimes noticeably and sometimes negligibly, depending on model and task. It is measured on the same golden set so the trade between memory, speed, and quality is made with evidence.
03Are open-weight models free to use commercially?
Licences vary meaningfully between model families and versions. FISTA reviews terms against your intended commercial use before engineering commits, and documents them for your legal review.
04How do we keep up with new models?
With a repeatable evaluation harness and a gateway, so assessing and adopting a new release is a routine comparison and a configuration change rather than a rebuild.
05How long does deployment take?
Evaluation typically takes weeks; production serving follows once capacity is available, with infrastructure provisioning as the usual gating item.
Scoped in writing before you commit
Choose the model your evaluation supports.
Bring your tasks and volume. The scoping call returns an evaluation plan, a serving architecture, and a cost comparison.