Hey everyone, I've been using PromptLayer for a few months now to track my OpenAI and Anthropic calls. The dashboard and prompt versioning are super handy for my benchmark posts. However, I'm hitting some scaling cost concerns, and their team-based features feel a bit heavy for my solo experimentation.
I'm looking for alternatives, but with a specific constraint: **I do NOT want to self-host an open-source solution.** I've tried a few (like the LangSmith open-source beta) and the infra overhead just kills my vibe. I want a managed service.
My core needs:
* **Multi-provider logging** (OpenAI, Anthropic, maybe Gemini/Mistral)
* **Simple, searchable request tracing** to compare outputs.
* **Basic cost tracking** per project/model.
* An API/SDK that's as simple as PromptLayer's wrapper.
What I don't need (right now):
* Full-blown AI agent orchestration
* Extensive team RBAC
* Built-in eval suites (I roll my own)
I've glanced at:
* **LangSmith** (managed): Seems powerful but maybe overkill? The pricing is opaque.
* **Arize Phoenix**: More focused on evals and tracing, but can it do the simple logging?
* **Portkey**: Prompts are secondary; they seem more about gateway/load balancing.
Has anyone moved from PromptLayer to another *managed* service and been happy? Or found a lighter-weight hosted option? I'd love to see some code snippets of how the logging compares.
For example, here's my typical PromptLayer pattern:
```python
import promptlayer
openai = promptlayer.openai
response = openai.ChatCompletion.create(
model="gpt-4",
messages=[{"role": "user", "content": "Explain quantum tunneling."}],
pl_tags=["benchmark-round-3"]
)
```
What does the equivalent look like in your alternative?
--experiment
Prompt engineering is the new debugging.
Totally get the scaling cost thing - it sneaks up on you! Your list is a solid starting point. For your specific needs, I'd actually add **Lunary** (formerly PromptWatch) to the mix. It's a managed service that nails the multi-provider logging (OpenAI, Anthropic, Cohere, even some open models via LiteLLM) and has a really clean interface for tracing and comparing runs. Their pricing is per-logged event, which felt more predictable for my solo projects than some seat-based plans.
Re: LangSmith managed, I agree it's powerful but feels built for teams building complex agent chains. The learning curve is steeper than PromptLayer. Arize Phoenix can do the logging but its strength is definitely the eval side, and Portkey is indeed more gateway-first. You might also peek at **Weights & Biases Prompts** - they have a managed tier, good tracing, and the cost tracking is integrated with their experiment tracking which is nice for benchmarks. Good luck!
Integration Ian