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Comparison
Beag Labs vs GPT-4 API
Compare Beag Labs custom SLMs vs the GPT-4 API for domain-specific tasks in regulated industries. Cost, privacy, latency, customization, compliance, and model ownership.
Beag Labs Custom SLM
A purpose-built small language model (500M-5B parameters) trained on your domain data and deployed on-prem, air-gapped, or in your VPC. No external API calls, no data leakage, and full model ownership.
- ✓Fixed cost per inference — no per-token billing or usage surprises
- ✓Zero data egress; sensitive documents never leave your infrastructure
- ✓Sub-100ms inference latency on a single GPU for batch classification
- ✓Fine-tuned on your domain corpus for higher accuracy on niche tasks
- ✓Full model weights and artifacts delivered — you own the IP
- ✓Deployable in air-gapped environments for classified or regulated workloads
- ✕Upfront investment required for model training and validation
- ✕Requires GPU infrastructure (or a managed deployment partner)
- ✕General-knowledge breadth is lower than a 1T+ parameter frontier model
- ✕Iterating on the model requires a retraining cycle, not a prompt change
GPT-4 API
OpenAI's frontier model accessed via a hosted API endpoint. Zero infrastructure to manage, broad general knowledge, and rapid prototyping — but every inference sends data to a third-party server and bills per token.
- ✓Zero infrastructure — no GPUs to provision or maintain
- ✓Broad general knowledge across virtually any topic
- ✓Immediate iteration via prompt engineering, no retraining
- ✓Rapid prototyping and proof-of-concept development
- ✓Managed scaling and uptime by the provider
- ✕Per-token pricing scales unpredictably with high-volume workloads
- ✕Every inference transmits data to a third-party server
- ✕Incompatible with air-gapped or on-prem compliance requirements
- ✕Latency of 500ms-3s per call depending on load and output length
- ✕No model ownership — the provider can change capabilities or pricing at any time
- ✕Vendor lock-in: prompts and workflows are tightly coupled to one provider's API
Feature Comparison
| Feature | Beag Labs Custom SLM | GPT-4 API |
|---|---|---|
| Cost per inference | Fixed amortized cost — near-zero marginal cost at scale | Per-token billing; cost scales linearly with volume |
| Data privacy | Data never leaves your infrastructure | Data sent to third-party servers on every call |
| Deployment options | On-prem, air-gapped, or VPC — full control | Hosted API only; no on-prem or air-gapped option |
| Latency | Sub-100ms on a single GPU for classification tasks | 500ms-3s per call depending on load and output length |
| Customization | Fine-tuned on your domain data for task-specific accuracy | Prompt engineering only; no weight-level customization |
| Compliance | Meets air-gapped, FedRAMP, HIPAA, and SOC 2 requirements | Limited; depends on provider's compliance certifications |
| Model ownership | You receive full model weights and artifacts | No ownership — provider controls the model entirely |
| Vendor lock-in | No lock-in — standard PyTorch weights, portable anywhere | High lock-in — prompts and workflows coupled to one API |
Verdict
For regulated industries running high-volume, domain-specific workloads — legal document classification, clinical note extraction, financial filing analysis — Beag Labs custom SLMs win on every axis that matters operationally. The cost curve inverts at scale: while the GPT-4 API is cheaper for low-volume prototyping, per-token billing becomes prohibitive at millions of inferences per month. A custom SLM amortizes its training cost rapidly and then runs at near-zero marginal cost on hardware you already own or lease.
The privacy and compliance gap is even more decisive. In legal, healthcare, and government contexts, transmitting client or patient data to a third-party API is often a non-starter — not a preference but a regulatory hard line. Air-gapped deployment is not a feature you can add to an API; it is architecturally impossible by definition. Beag Labs models run inside your perimeter, period.
Where the GPT-4 API retains an advantage is breadth: open-ended reasoning, creative generation, and zero-shot tasks across unfamiliar domains. If your workload is exploratory, low-volume, or requires general-world knowledge, the API is the right tool. But once a task is well-defined, repeated at scale, and touches regulated data, a custom SLM is the correct production architecture.