A foundation model for generative AI is a large pretrained model adapted through prompting, tool usage or additional training to produce outputs like text, code or images.
While choosing a foundational model, you should weigh factors like expected outcomes, openness, modality support, cost, deployment requirements and an objective scorecard.
This matters because:
- In 2025, 84% of developers use AI tools, which makes disciplined selection even more important for you.
- IDC forecasts worldwide AI spending to reach about $632 billion by 2028, which signals continued change that you should plan to track.
Let’s dive deeper and check out the steps to choose the right foundation model for Gen AI.
Step 1: Define Outcomes, Risks and Constraints
Start by translating business goals into measurable evaluation criteria, so you can compare models with rigor. You should define acceptable error types, such as hallucination on facts, broken JSON or tool call failures, because each error harms different workflows.
Then convert those risks into metrics you can track, including accuracy on golden sets, structured output fidelity, latency at p95 and total cost per successful task. Since prompts are billed in tokens, you should convert typical documents into token sizes to size contexts and estimate spend early.
For Gemini models, 100 tokens roughly equal 60 to 80 English words, which helps you estimate request sizes with less guesswork.
Key Points:
- Define the exact outputs you need, then list risks up front.
- Note latency limits, structured data requirements, multilingual coverage and privacy constraints.
- Translate representative inputs into tokens, so you can size prompts, context reuse and cache strategy before any bake-off.
Step 2: Choose Your Path: Open-weight or Proprietary
Next, decide whether you will self-host open-weight models or consume proprietary models through an API. Open-weight families like Llama 3.1 and Mistral allow on-prem or VPC deployment with license terms that enable deeper control, which many teams prefer for data residency.
Proprietary options like Claude, GPT and Gemini provide managed uptime, integrated tooling and faster feature access, which reduces operational overhead for many organizations.
Therefore, match this choice to your compliance profile, operating model and team skills, since switching later creates additional migration work. Mistral’s Medium 3 announcement highlights cost efficiency claims with simplified enterprise deployment, which signals improving open-weight economics.
Step 3: Align Modalities and Context Windows
Before shortlisting, confirm the inputs and outputs your product requires, so you avoid expensive re-architecture later. If you need to process PDFs, charts or images, pick a multimodal model with reliable tool use and streaming output.
Long-context models reduce chunking complexity for you, yet they still require careful prompt design to preserve retrieval fidelity.
Google’s Gemini family documents models with million-token context options, while Claude Sonnet 4.5 lists a 200K context window with 64K output tokens for coding and planning. Select the smallest window that safely fits your real inputs to control cost and latency.
Key Points:
- Map required modalities, tool usage and streaming needs to specific models.
- If you summarize long PDFs or contracts, prioritize models offering tested long context on your data.
- Capture expected maximum tokens for inputs, tools and outputs in a sizing sheet.
Step 4: Evaluate Real-world Performance, Not Just Leaderboards
Public leaderboards help you compare families quickly, yet production success depends on your exact tasks and constraints. You should build a golden set from real tickets, documents and calls, so measurements reflect your user journeys.
Then test tool call accuracy, guardrail performance, p95 latency and cache hit rates across multiple runs to capture variance.
Community evaluations like LMSYS Chatbot Arena and the Hugging Face Open LLM Leaderboard are useful starting points, although they should not replace task-level tests. Anthropic reports substantial savings from prompt caching, which can change the total economics for you at scale.
Key Points:
- Run paired prompts across finalists using the same tools, retrieval stack and cache policy.
- Track failures that matter to your users, including structured output breaks and retrieval misses.
- Version your golden sets, so future retests remain comparable.
Step 5: Calculate Actual Costs, including Infra and Caching
Costing must include inputs, cached inputs, outputs and infrastructure, so you avoid surprises during rollout. You should compute a cost per successful task, not cost per token, because retries and failures drive real spend.
For example, OpenAI lists gpt-real-time-mini at $0.60 per million input tokens with lower rates for cached inputs, which changes the math when cache hits are high. Anthropic documents up to 90% input savings with prompt caching on Sonnet 4.5, which can materially lower recurring costs for stable instructions or long contexts.
If you run GPUs yourself in India, local cloud providers can provide up to eight H100 80 GB at about ₹16 lakh per month, which anchors infra budgeting in rupees. Always combine model pricing with infra, storage, networking and caching to estimate total cost per user flow.
Key Points:
- Build a calculator that multiplies your average inputs, cached inputs and outputs by each vendor’s rates.
- Add GPU, storage and egress if you self-host.
- Compare models by cost per completed workflow, so you favor reliable outputs over cheap tokens.
Step 6: Select Deployment Model, Residency and SLAs
Deployment choices affect security posture, data residency and uptime, so choose them as deliberately as model quality. You can consume models by public API, private link, VPC or full on-prem, depending on compliance and control needs.
Claude is available through Amazon Bedrock, which supports VPC endpoints using AWS PrivateLink for private connectivity.
If you need India residency with GPUs, providers advertise a 99.99%* uptime SLA and hosts DeepSeek in dedicated or shared environments with data residency in India. Align these options to your governance map, so data location and uptime targets are satisfied without custom workarounds.
Key Points:
- Document your data classes, retention rules and residency needs.
- Select API with PrivateLink for controlled connectivity, VPC for tighter isolation or on-prem for maximum control.
- Record SLAs, indemnities and support paths in your runbook.
Step 7: Build a Scoring Model to Compare Finalists
A clear scorecard helps you justify the decision to technical and business leaders. We recommend a simple utility score:
Utility = (Quality + Safety + Reliability) ÷ Cost, normalized to a 0 to 5 scale.
Here,
- Quality comes from your golden set accuracy.
- Safety reflects guardrail adherence and low-risk behaviors.
- Reliability covers p95 latency, tool success and cache stability.
- Cost includes model pricing and infra for your chosen deployment. Shortlist three finalists, then run a week-long bake-off using identical workloads, so results are comparable across options.
Pro Tip: Weight transparency, tool compatibility, latency and total cost of ownership. Use your production traffic shape to define value, so you do not chase leaderboard wins that do not translate to your users.
Step 8: Apply Decision Recipes to Common Use Cases
You can accelerate selection by starting with proven pairings, then validating your data.
- For budget-sensitive assistants, consider Mistral or a compact GPT variant with retrieval augmentation to improve accuracy on your corpus.
- For maximum accuracy, consider Claude Sonnet or a frontier-grade GPT model with careful tool use and caching.
- For low latency, consider Gemini Flash or Mixtral configurations that trade some accuracy for speed when responses must feel instant.
Pro Tip: Verify pricing, since vendors adjust rates often and prefer configurations that meet your error budget.
Mistral claims Medium 3 achieves SOTA (State-of-the-Art) performance at up to eight times lower cost compared with peers at published rates. Validate against your workload before adopting the claim.
Key Takeaways
There is no single best model, only the best fit for your workloads and constraints. Therefore, we suggest you combine real evaluations, deployment facts and current pricing, then revisit quarterly as models and rates change.
Need help with figuring out the right foundational model for Generative AI? Connect with our friendly cloud computing experts using free consultations and free trials. Get started with your AI/ML deployment in a matter of minutes!
Frequently Asked Questions:
You likely need it only when typical inputs exceed a few hundred thousand tokens, so test long context with Gemini before paying more.
For Gemini models, 100 tokens equal about 60 to 80 English words, which helps you size prompts and budgets.
Yes, for many tasks when retrieval and prompt discipline are strong, although you should validate accuracy on your data using a golden set.
Yes. AceCloud hosts DeepSeek with India residency and publishes a 99.99% SLA for its VPC service.
