Estimate Monthly AI API Costs for a 5-Person Team
Start with team workflows, not a vague token target
A small team usually spends on AI through a few repeatable workflows: coding assistance, document summarization, support drafts, internal research, and automation. List each workflow, how many people use it, and how often it runs. This gives you a usable forecast before you choose models or commit to a provider.
For example, imagine a five-person product team:
- 2 engineers use an AI coding agent for 20 workdays each month.
- 1 support specialist summarizes 800 customer conversations monthly.
- 2 product staff create research and planning drafts.
- Automations classify 10,000 short feedback items per month.
Do not assume every task needs the largest model. A practical mix is usually a premium reasoning model for difficult code or analysis, a balanced model for writing and summaries, and a fast low-cost model for high-volume classification. This is the largest controllable cost lever.
Estimate input and output tokens for each task
API providers bill separately for input tokens (what you send) and output tokens (what the model returns). Start with a small production sample: log 20 to 50 representative requests, then calculate average input and output tokens. If logs are not available yet, use conservative assumptions and revisit them after the first week.
Here is a simple monthly estimate for the engineering workflow. Suppose each engineer makes 25 requests per day, with an average of 3,000 input tokens and 1,000 output tokens:
2 engineers x 20 days x 25 requests = 1,000 requests/month
Input: 1,000 x 3,000 = 3,000,000 tokens
Output: 1,000 x 1,000 = 1,000,000 tokens
Repeat this calculation for every workflow. For support summaries, an average conversation may contain 1,200 input tokens and produce a 250-token summary. For classification, use compact prompts and short labels; a request might need only 150 input tokens and 20 output tokens.
Turn token volume into a monthly budget
Use the current per-million-token prices for the selected model and calculate input and output separately. The general formula is:
Monthly cost = (input tokens / 1,000,000 x input rate) + (output tokens / 1,000,000 x output rate)
For a coding workflow, if your chosen model costs $3 per million input tokens and $15 per million output tokens, the estimate above is:
(3M x $3) + (1M x $15) = $24/month
That number is intentionally a worked example, not a quoted price. Check the live price page before setting a budget because model pricing can change. Build a spreadsheet with one row per workflow and columns for model, monthly requests, average input tokens, average output tokens, input rate, output rate, and monthly total.
Use one compatible endpoint to control model choice
59API is useful for cost-conscious teams because it provides pay-as-you-go access to native official-quality Claude models, including Opus, Sonnet, Haiku, and Fable, plus GPT models, without a model downgrade. Its relay is compatible with Claude Code, Codex, and OpenAI SDK integrations, so a team can route different jobs to appropriate models without rebuilding its application around multiple provider-specific clients.
For an OpenAI SDK-compatible application, keep the base URL and API key in environment variables:
export OPENAI_BASE_URL=https://api.59api.com
export OPENAI_API_KEY=your_59api_key
Then select a lower-cost model for routine work and reserve a stronger model for requests that demonstrably need it. In most teams, this policy reduces spending more reliably than trying to shorten every prompt by a few words.
Add a buffer and monitor the first month
Multiply your modeled total by 1.2 to 1.5 for the first month. The buffer covers experimentation, retries, longer-than-expected contexts, and adoption growth. After launch, review usage weekly and compare actual token counts with the assumptions in your sheet. Investigate workflows where output tokens are unexpectedly high; stricter response formats and lower maximum output limits often help.
- Set per-workflow monthly caps and alert thresholds.
- Store usage metadata such as feature, model, and customer tier.
- Use a cheaper model for first-pass routing, extraction, and classification.
- Test prompt changes against quality before changing production model tiers.
A small team does not need a perfect forecast. It needs a transparent model that can be updated from real usage. For low-cost, flexible access across Claude and GPT workflows, sign up for 59API, track the first month closely, and refine your model mix as usage data arrives. Its referral rebate can also provide an additional cost benefit for teams that share the service with other developers.