Decision Comparison Matrix
On-Premise vs Hybrid vs API-Only: Trade-offs, Constraints, and What to Verify
⚠ Non-Advisory Content
This comparison matrix presents decision axes, trade-offs, and constraints for evaluation purposes only.
It does not constitute a recommendation, endorsement, or ranking of deployment models.
Your organization's specific requirements, regulatory context, and risk tolerance must drive your decision.
What this matrix does: Helps you reason about deployment options.
What this matrix does not do: Tell you which option to choose.
1. DATA LOCALITY / PRIVACY
ON-PREMISE ONLY
Trade-offs:
• Full control over data residency
• No external API exposure
• Requires strict internal access controls
• Easier to demonstrate compliance
Constraints:
• Must maintain physical security
• Network segmentation required
• Internal breach = total exposure
What to Verify:
• Data at rest encryption capability
• Access audit logs retention
• Physical datacenter certifications
HYBRID (ON-PREM + API)
Trade-offs:
• Can isolate sensitive data on-prem
• Non-sensitive workloads use API
• Requires data classification rigor
• Dual operational model complexity
Constraints:
• Misclassified data = exposure
• Egress filtering needed
• API vendor contract terms apply
What to Verify:
• Data classification policy exists
• Routing rules auditable
• API vendor SOC2/ISO27001 status
API-ONLY
Trade-offs:
• No infrastructure maintenance
• Data leaves organizational boundary
• Vendor terms define privacy posture
• Zero control over model training use
Constraints:
• May violate data residency laws
• Vendor breach = your exposure
• Subprocessor changes unilateral
What to Verify:
• DPA/BAA availability
• Subprocessor list frequency
• Data retention policy clarity
2. COST PREDICTABILITY
ON-PREMISE ONLY
Trade-offs:
• High upfront CapEx (hardware)
• Predictable monthly OpEx (power, staff)
• No per-token surprise bills
• Refresh cycles every 3-5 years
Constraints:
• Underutilization = sunk cost
• Sudden scale-up requires purchase
• Staff cost must be internalized
What to Verify:
• Total 3-year cost model exists
• Power/cooling budget confirmed
• Staff training budget allocated
HYBRID (ON-PREM + API)
Trade-offs:
• Base load on-prem (predictable)
• Burst to API (variable)
• Dual accounting complexity
• Can optimize per workload
Constraints:
• API cost can spiral unexpectedly
• Routing logic must be cost-aware
• Both models require budgeting
What to Verify:
• API rate limit / overage policy
• Cost monitoring tooling exists
• Chargeback model defined
API-ONLY
Trade-offs:
• Zero CapEx
• Pure OpEx (pay-per-use)
• Scales instantly
• Budget predictability = usage discipline
Constraints:
• Uncontrolled usage = unbounded cost
• Vendor pricing changes unilateral
• No hedge against token cost inflation
What to Verify:
• Usage cap controls available
• Pricing history volatility
• Cost per 1M tokens verified
3. LATENCY CONTROL
ON-PREMISE ONLY
Trade-offs:
• Sub-10ms network latency possible
• No internet dependency
• Local load = local degradation
• Edge/plant deployments viable
Constraints:
• Inference speed = hardware limit
• No external fallback for surges
• Must dimension for peak load
What to Verify:
• P95/P99 latency requirements defined
• Load testing completed
• Queueing behavior under load
HYBRID (ON-PREM + API)
Trade-offs:
• Low-latency on-prem for critical
• Tolerant workloads use API
• Routing adds decision latency
• Two latency SLAs to manage
Constraints:
• API latency = vendor SLA + internet
• Failover latency must be acceptable
• Mixed latency = user confusion risk
What to Verify:
• Workload latency tolerance defined
• API vendor SLA documented
• Fallback behavior tested
API-ONLY
Trade-offs:
• Latency = internet + vendor processing
• Vendor scales transparently
• No control over inference queue
• Geographic routing possible
Constraints:
• Internet outage = total failure
• Vendor degradation uncontrollable
• No sub-100ms guarantees typical
What to Verify:
• Vendor SLA uptime percentage
• Multi-region redundancy available
• Timeout policy defined
4. GOVERNANCE / AUDITABILITY
ON-PREMISE ONLY
Trade-offs:
• Full audit trail control
• Model versioning under your control
• Easier regulatory validation
• Must build observability yourself
Constraints:
• Log retention = your infrastructure
• Change management required
• Internal compliance burden
What to Verify:
• Logging framework supports audit reqs
• Model registry capability exists
• Change control process defined
HYBRID (ON-PREM + API)
Trade-offs:
• Dual logging systems required
• On-prem logs full, API logs partial
• Correlation across systems hard
• Separate compliance postures
Constraints:
• API vendor may not retain user logs
• Model version tracking fragmented
• Auditors must understand both
What to Verify:
• Unified logging ingestion possible
• API logs meet audit requirements
• Incident response covers both
API-ONLY
Trade-offs:
• Vendor provides logs (if any)
• Model updates = vendor schedule
• Less audit infrastructure
• Governance = trust vendor
Constraints:
• Log detail = vendor discretion
• Model versioning opaque
• Regulatory proof harder
What to Verify:
• Logs include prompt/response IDs
• Model version exposed via API
• Audit report frequency confirmed
5. OPERATIONAL COMPLEXITY
ON-PREMISE ONLY
Trade-offs:
• Requires ML/Ops expertise internally
• Hardware lifecycle management
• Full stack observability needed
• Model updates = planned maintenance
Constraints:
• 24/7 on-call may be required
• Patching cadence = your choice
• Scaling requires procurement lead time
What to Verify:
• Ops team skill matrix complete
• Runbook coverage > 80%
• Mean-time-to-repair acceptable
HYBRID (ON-PREM + API)
Trade-offs:
• Both on-prem ops AND API integration
• Routing logic = additional component
• Dual failure modes to monitor
• API reduces on-prem load complexity
Constraints:
• Teams must understand both systems
• Incidents harder to diagnose
• More integration points = more risk
What to Verify:
• Router component has HA plan
• Fallback logic tested end-to-end
• Alerting covers both paths
API-ONLY
Trade-offs:
• Minimal ops overhead
• Vendor handles infrastructure
• No model deployment complexity
• Integration code only
Constraints:
• Vendor downtime = your downtime
• No deep troubleshooting possible
• API changes = mandatory migration
What to Verify:
• Retry/timeout strategy defined
• Vendor status page monitored
• Graceful degradation mode exists
No deployment model is universally superior. Your decision must be driven by:
- Data sensitivity tier: What cannot leave your boundary?
- Latency tolerance: What response time kills your use case?
- Cost model preference: CapEx budget vs OpEx flexibility?
- Operational maturity: Can you run ML infrastructure 24/7?
- Regulatory constraints: What does your auditor require?
→ Use this matrix to map your constraints to each model's trade-offs. The "right" answer is constraint-dependent, not model-dependent.