Evaluating Frontier LLMs for Enterprises
Large language models are no longer research curiosities; they are critical backend primitives. When I evaluate a model for enterprise-grade workloads—regulatory workflows, multi-billion event pipelines, customer intelligence systems—I lean on a living playbook. Consider this page the current snapshot of that playbook.
Foundational Principles
A few truths continue to hold even as the leaderboard changes weekly:
- Architecture defines the envelope. Transformer variants, Mixture-of-Experts routing, retrieval augmentation, and compiler-level optimizations decide latency, cost, and observability.
- Scaling laws are stubborn. Parameter count, token count, and compute budget still obey power-law behavior. Any pitch that claims to skip those laws demands extraordinary proof.
- Alignment is contextual, not universal. Refusal policies, bias mitigations, and jailbreak resistance must be evaluated against your domain constraints—financial advice, clinical workflows, safety review, etc.
- Tooling equals leverage. Eval harnesses, tracing hooks, and fine-tuning APIs determine whether the model becomes an adaptive platform or a black box dependency.
Evaluation Rubric
I score each contender on a 1–5 scale. Anything below “3” earns a mitigation plan before deployment.
| Dimension | Key Questions | What “5” Looks Like |
|---|---|---|
| Task Fit | Can it solve our top five workflows with minimal prompt contortions? | Zero/few-shot competence, deterministic reasoning traces, grounded outputs. |
| Latency & Throughput | Does P95 stay < 1.5s under burst traffic? | Streaming tokens, GPU-aware autoscaling, graceful degradation under load. |
| Context & Retrieval | How does it behave with 100k+ tokens plus retrieval augmentation? | Stable citations, controllable memory, transparent truncation strategy. |
| Safety & Auditability | Are guardrails transparent? Can we inspect logs and refusals? | Configurable policies, red-team reports, instant access to raw telemetry. |
| Customization Surface | How much control do we have over fine-tuning, adapters, system prompts? | Native APIs, weight snapshots, eval suites that survive version bumps. |
| Total Cost | Does the business model make sense at production scale? | Predictable pricing, on-prem/VPC options, clear escape hatch to open models. |
Failure Modes I Monitor
- Specification gaming. Models that ace benchmarks yet crumble when prompts mix code, policy, and free-form reasoning.
- Latency cliffs. Systems that look fine at 5 TPS but spiral once retrieval adds 150k tokens or cold GPUs join the pool.
- Silent regressions. Vendors rotate weights without telling anyone; accuracy on tax/compliance flows drops double digits.
- Overzealous guardrails. Refusal policies block legitimate internal tools, forcing prompt hacks and shadow systems.
- Procurement lock-in. Credits expire, minimum commits balloon, or retention clauses prevent switching vendors.
Procurement & Vendor Diligence
Technical excellence still fails if commercial scaffolding is weak. My diligence checklist:
- Data governance proof. SOC 2 / ISO 27001, regional tenancy, explicit retention windows for inference and fine-tuning.
- Incident transparency. Documented SLAs, historical outage summaries, and escalation paths that reach humans quickly.
- Roadmap disclosure. Upcoming context expansions, multimodal plans, and pricing guardrails before they ship.
- Exit strategy. Contract clauses for exporting fine-tuned derivatives or rolling workloads onto open-source fallbacks.
- Usage analytics. Token-level reporting accessible to engineering, finance, and security from day one.
Operating the Decision
Shipping is the midpoint, not the finish line. Once a model is in production, I keep:
- Monthly regression suites combining production transcripts, adversarial prompts, and policy-sensitive cases.
- Shadow deployments that replay anonymized traffic through alternate models to quantify switching costs.
- Unified observability—latency, refusal rate, hallucination tags, and spend—inside the same dashboards as the rest of the backend.
- A “model council” with product, infra, and legal to trigger upgrades, rollbacks, or fine-tuning cycles.
Keeping This Evergreen
This page intentionally avoids timestamped claims. As new models, regulations, or architectures arrive, I update the relevant rubric and document the rationale. Treat it as an evolving architecture memo rather than a dated announcement.