Deploying generative AI into your business is neither a weekend project nor a six-month slog if you plan correctly. The timeline depends heavily on your use case—integrating a chatbot differs drastically from building a custom LLM pipeline—but understanding the phases helps you avoid nasty surprises and budget blowouts. Here's what to expect from planning through go-live.
Phase 1: Discovery and Requirements (2–4 weeks)
Before touching any code or vendor demos, define what you're actually trying to solve. Are you automating customer support, augmenting internal research, or generating product descriptions? The clearer your goal, the faster Phase 1 moves.
Work with your team to document:
- Specific use cases (e.g., "answer 70% of support tickets automatically")
- Data requirements (volume, format, sensitivity level)
- Integration points (which systems feed into or consume the AI output)
- Compliance constraints (HIPAA, GDPR, industry-specific regs)
- Budget ceiling (ongoing API costs, infrastructure, training)
This phase typically costs nothing if handled internally, but consulting specialized firms can run $5,000–$20,000 to audit your readiness.
Phase 2: Technology Selection and PoC (3–8 weeks)
Once you know what you need, evaluate whether to buy (SaaS APIs), build (fine-tuned models), or hybrid.
Off-the-shelf options (ChatGPT API, Claude, Gemini) get you running in days. Monthly costs range $100–$10,000+ depending on token consumption and scale. This route works for low-sensitivity tasks and quick launches.
Custom or fine-tuned models (using platforms like Hugging Face, Modal, or Replicate) take longer but give you control and can reduce per-query costs at scale. Budget 4–8 weeks for a proof of concept and $10,000–$50,000 in initial setup and compute.
Run a focused PoC during this phase. Integrate your actual data, test outputs against real workflows, and measure accuracy. This is where you catch fundamental misfits before full development.
If you're comparing multiple vendors or approaches, Mercoly helps you find and evaluate trusted Generative AI & LLM Integration providers side by side, making selection faster and more confident.
Phase 3: Development and Integration (6–16 weeks)
Scope matters enormously here. A simple chatbot connected to a knowledge base takes 6–8 weeks. A multi-step workflow that chains several LLM calls, handles streaming responses, and integrates with legacy databases can easily stretch to 16 weeks or beyond.
Key activities:
- Building API connectors or embedding libraries
- Setting up data pipelines (cleaning, chunking, vectorization for RAG)
- Developing fallback logic and guardrails
- Creating monitoring and cost-tracking infrastructure
- Writing integration tests with real data samples
Your team's familiarity with LLM tooling accelerates this phase. If you're hiring specialists, expect to spend $150,000–$400,000 depending on complexity and headcount.
Phase 4: Testing, Safety, and Compliance (2–6 weeks)
Don't rush this. Test adversarial inputs, bias, hallucinations, and edge cases specific to your industry. If you're handling regulated data, have your legal and compliance teams review outputs and logging practices.
Common checks:
- Accuracy benchmarking against ground truth
- Latency under production load
- Cost per query under typical volume
- Handling of out-of-scope or harmful requests
- Audit trail and data retention compliance
Budget $20,000–$60,000 for professional security and compliance audits if required.
Phase 5: Deployment and Monitoring (1–3 weeks)
Roll out to production gradually. Start with a small user cohort, monitor error rates and user feedback, then expand. Keep feedback loops tight—LLM outputs often need prompt tweaking or retraining after real usage.
Plan for ongoing maintenance: model updates, cost optimization, retraining schedules, and incident response.
Total Timeline Expectations
- Quick SaaS integration: 8–12 weeks
- Custom model with internal data: 14–20 weeks
- Enterprise-grade system with compliance: 20–32 weeks
Frequently Asked Questions
Q: Should we use a pre-built LLM API or fine-tune our own model? Use pre-built APIs if speed and low maintenance matter; fine-tuning is worth it if you have proprietary data, strict cost constraints at scale, or need offline capability. Most teams start with APIs and shift to custom models as volume grows.
Q: How much does it cost to run generative AI in production? API-based solutions typically cost $500–$5,000 monthly for small to medium usage; large enterprises paying for token consumption or self-hosted models can see $10,000+ monthly depending on query volume and model size.
Q: What's the biggest risk during integration? Underestimating data quality and fallback handling. Garbage in means garbage—or hallucinations—out, and your system needs clear guardrails for when the LLM can't answer confidently.
Ready to compare vendors and timelines for your specific project? Explore trusted providers on Mercoly to get realistic quotes and implementation schedules.