Keywords AI charges $9 per seat/month for its Pro plan, which includes 10,000 logs, making it one of the most accessible entry points in the AI gateway market. For a startup running a customer service bot processing 5,000 requests per day (150k/month), you'd pay the $9 base plus usage fees for the extra 140k logs. Assuming standard log ingestion rates (approx. $0.10/1k logs), your bill sits around $25/month. Compare this to Portkey's starting tier of $99 or building your own logging stack on AWS, and the value is obvious.
The platform feels like the "Datadog for LLMs." It unifies model routing, observability, and prompt management into a single, polished dashboard. You can route traffic to OpenAI, Anthropic, or 250+ other models just by swapping your base URL. The prompt management system is particularly strong, allowing product managers to iterate on prompts in a playground and deploy them without engineering pushes. It also includes built-in evaluations, letting you score model outputs against golden datasets automatically.
However, this convenience comes with a "physics tax." Because Keywords AI operates as a SaaS proxy, every request makes a round trip to their servers before hitting the LLM provider. This introduces a latency overhead—typically 50-150ms depending on your region. For a chatbot, this is negligible; for a real-time voice agent or high-frequency trading bot, it’s a dealbreaker. Additionally, unlike Helicone or LiteLLM, Keywords AI is proprietary. You cannot self-host it to keep data strictly within your VPC, which may disqualify it for highly regulated fintech or healthcare use cases.
Ultimately, Keywords AI is the best choice for product-focused teams who want a "battery-included" DevOps stack. If you are an indie hacker or a seed-stage startup, the $9/month Pro plan is a no-brainer to get world-class tooling. But if you are an infrastructure purist who needs sub-20ms overhead or absolute data sovereignty, you should look at self-hosting LiteLLM or Helicone.
Pricing
The Free plan is genuinely useful for development, offering 2,000 logs/month and unlimited prompts for up to 2 seats. The real value is the $9/seat Pro plan, which bumps you to 10,000 logs. This is significantly cheaper than Portkey ($99/mo) or LangSmith's paid tiers for small teams. The cost cliff is gentle; after your included logs, you pay usage-based fees (estimated ~$0.10-$0.25 per 1k logs), meaning you won't get a surprise $500 bill just for a minor traffic spike. However, keep an eye on the Team plan ($49/seat) if you need granular roles, as the jump from Pro is substantial.
Technical Verdict
Integration is trivial: standard OpenAI SDK compatibility means you just change the base_url and api_key. The platform handles the complexity of normalized schemas across providers well. Documentation is clean but occasionally sparse on edge cases like complex multi-modal routing. The primary technical trade-off is the SaaS proxy latency (50ms+), which is unavoidable without self-hosting. SDK support is strong for Python and TypeScript, with a focus on modern async patterns.
Quick Start
# pip install openai
from openai import OpenAI
client = OpenAI(
base_url="https://api.keywordsai.co/api/openai/v1",
api_key="YOUR_KEYWORD_AI_KEY"
)
response = client.chat.completions.create(
model="gpt-4o", # Routes to provider via Keywords
messages=[{"role": "user", "content": "Hello world"}]
)
print(response.choices[0].message.content)Watch Out
- It is a SaaS proxy, so you incur a 50-150ms network latency penalty on every request.
- Strictly proprietary; you cannot self-host the core platform if your compliance needs change.
- The $9 Pro plan is per-seat, so costs scale with team size, not just API volume.
- Data retention on lower plans may be limited compared to self-hosted logs.