A weighted scoring framework for comparing AI vendors and models objectively. Cut through marketing noise with 40+ evaluation criteria across capabilities, pricing, security, and strategic fit.
Most AI vendor decisions are made based on a demo, a blog post benchmark, or whichever provider the team's most vocal engineer prefers. This is how organizations end up locked into vendors that do not fit their actual needs.
The cost of getting this wrong is not just the monthly API bill. It is the 6 months of engineering work you will need to rewrite when you switch providers, the compliance gaps you discover during your first audit, and the production outages from rate limits you did not test against.
Average migration cost when switching AI providers is 3-6 months of engineering time. Provider-specific prompt formats, function calling schemas, and SDK patterns create invisible dependencies that compound over time.
New models launch weekly. Without a scoring framework, teams chase benchmarks instead of measuring what matters for their use case. A model that scores 2% higher on MMLU may be 3x slower for your workload.
Per-token pricing is only 40-60% of total cost. Egress fees, fine-tuning runs, support tiers, compliance audits, and engineering overhead are the invisible majority of your AI spend.
Evaluate every AI vendor across six dimensions. Weight each dimension based on your organization's priorities. A healthcare company will weight Security & Compliance at 30%+. A startup optimizing for speed may weight Technical Capabilities at 35%.
Head-to-head comparison of major LLM providers as of early 2026. Pricing changes frequently -- always verify current rates before making decisions.
Public benchmarks (MMLU, HumanEval, GPQA) measure general capability. Your production workload is not general. Always run a 1-2 week pilot with your actual prompts, data, and edge cases before committing. A model that ranks #3 on benchmarks may rank #1 for your specific domain.
Your vector database choice depends on scale, operational maturity, and whether you already have PostgreSQL infrastructure you want to leverage.
Already on PostgreSQL? Start with pgvector. You can migrate to a purpose-built vector DB later if you hit scale limits. Need zero-ops? Pinecone serverless. Need hybrid search? Weaviate. Need maximum performance? Qdrant. Building at massive scale from day one? Milvus/Zilliz.
Cloud AI platforms provide managed access to multiple models with enterprise integrations. Your choice often depends on which cloud provider you already use.
| Platform | Available Models | Pricing Model | Enterprise |
|---|---|---|---|
| AWS Bedrock | Anthropic, Meta, Mistral, Cohere, Stability, Amazon Titan | Pay-per-token, provisioned throughput, model customization | HIPAA, SOC, FedRAMP, data never leaves AWS account |
| Azure AI / OpenAI Service | OpenAI (exclusive), Mistral, Meta, Cohere, Phi | Pay-per-token, PTU for guaranteed throughput | Most compliance certs (HIPAA, FedRAMP High, IL5, SOX) |
| GCP Vertex AI | Gemini (exclusive), Anthropic, Meta, Mistral | Pay-per-token, provisioned throughput | ISO 27001, SOC, HIPAA, data residency controls |
| HuggingFace | 700K+ open models, Inference Endpoints, Spaces | Free tier, Inference Endpoints from $0.06/hr (CPU) | Enterprise Hub, private models, SSO, audit logs |
Consider abstracting your LLM calls behind a gateway (LiteLLM, Portkey, or a custom router) that can switch between providers. This lets you use Azure OpenAI as primary and Bedrock Claude as failover without rewriting application code. The abstraction cost is minimal; the vendor flexibility is invaluable.
Three proven methods for weighting criteria, from simplest to most rigorous. Choose based on the stakes of the decision and the time you have.
Fastest -- 30 minutes
Categorize each requirement as Must Have, Should Have, Could Have, or Won't Have. Any vendor failing a Must Have is eliminated immediately.
Moderate -- 1-2 hours
Compare every criterion pair and ask: "Which is more important?" Count wins to derive relative weights. Reduces bias from arbitrary percentage assignment.
Example for 4 criteria:
Security vs Pricing → Security wins
Security vs Latency → Security wins
Pricing vs Latency → Latency wins
Result: Security (2), Latency (1), Pricing (0)
Most rigorous -- half day
Assign weights (summing to 100%), score each vendor 1-10 per criterion, multiply score by weight. Highest total wins. See the worked example below.
Formula: Total = Sum(Weight_i x Score_i)
Score 1-10 where:
1-3 = Does not meet requirements
4-6 = Partially meets requirements
7-9 = Meets or exceeds requirements
10 = Exceptional fit
Here is a worked example comparing three hypothetical vendors for an EU-based enterprise RAG deployment. Adjust criteria and weights to match your priorities.
| Criterion | Weight | Vendor AUS hyperscaler | Vendor BCloud platform | Vendor CEU-native |
|---|---|---|---|---|
| Model quality (task-specific) | 25% | 9/10(22.5) | 8/10(20.0) | 7/10(17.5) |
| Latency P95 | 15% | 7/10(10.5) | 9/10(13.5) | 8/10(12.0) |
| Data residency (EU) | 20% | 5/10(10.0) | 7/10(14.0) | 10/10(20.0) |
| Pricing at 10M tokens/day | 15% | 6/10(9.0) | 8/10(12.0) | 9/10(13.5) |
| Fine-tuning support | 10% | 9/10(9.0) | 6/10(6.0) | 8/10(8.0) |
| Enterprise support SLA | 10% | 8/10(8.0) | 9/10(9.0) | 6/10(6.0) |
| Migration portability | 5% | 4/10(2.0) | 7/10(3.5) | 9/10(4.5) |
| Weighted Total | 100% | 71.0 | 78.0 | 81.5Winner |
In this scenario, the EU-native vendor (C) wins despite lower model quality and enterprise support scores. The heavy weight on data residency (20%) and competitive pricing made the difference. This is exactly why structured evaluation matters -- Vendor A would have won if we only looked at model quality.
Important: If two vendors are within 5 points of each other, consider running a parallel pilot. The matrix gives you a shortlist, not a final answer. Real-world performance on your data is the tiebreaker.
Any of these should trigger deeper investigation. Three or more in a single vendor should be a hard pass unless you have no alternatives.
If they hide downtime, you cannot assess reliability. Every serious vendor has a public status page.
If opting out of data training is not a simple API flag or account setting, your data is likely being used.
Hidden pricing means pricing varies by perceived ability to pay and creates budget unpredictability.
SOC 2 Type I is a point-in-time snapshot. Type II covers a period and shows sustained controls. Demand Type II.
Models you depend on should not disappear with less than 6 months notice. Check their deprecation policy.
If your data must stay in your environment and the vendor cannot accommodate that, it is a dealbreaker for regulated industries.
Quality SDKs handle retries, rate limits, streaming, and errors. A thin REST wrapper signals engineering immaturity.
You cannot architect systems around limits you cannot predict. Undocumented throttling causes production failures.
GDPR and many regulations require a DPA. No DPA means no serious enterprise compliance posture.
One region means one point of failure. For production workloads, demand multi-region or have a hot backup vendor.
Before signing any vendor contract, make sure you have verified: