Prompt
What should I use for pay-per-request inference?
Latest observation
If you want pay-per-request inference, the best choice depends on your priorities:
- Lowest operational hassle: use a serverless inference API from a managed provider.
- Lowest latency / production control: use a containerized endpoint with autoscaling that can scale to zero or near-zero.
- Occasional or bursty usage: pay-per-request is ideal because you avoid paying for always-on GPUs.
Good options
- AWS Bedrock / SageMaker Serverless Inference
- Google Vertex AI online prediction
- Azure Machine Learning managed online endpoints
- Hugging Face Inference Endpoints (if supported for your model)
- Replicate / Together / Fireworks / OpenRouter for model-specific APIs
What to choose
- For custom models: managed serverless inference on your cloud provider.
- For LLMs: a hosted model API is usually easiest and truly request-based.
- For strict cost control: compare:
- request price
- token/output pricing
- cold start time
- max concurrency
- GPU availability
Rule of thumb
- Small, infrequent workloads → serverless
- Heavy but spiky workloads → autoscaling endpoints
- LLM apps → model API / hosted inference provider
If you tell me:
- the model type (LLM, vision, embedding, etc.),
- traffic pattern,
- cloud provider,
- latency target,
I can recommend the best specific setup.
Observation history
How did Obsurfable measure this prompt?
Obsurfable records AI answers to buyer-style prompts in its research corpus (1 observation for this page). Metrics are distributions over observations, not a single static ranking.
Which AI systems does Obsurfable collect answers from?
OpenAI,
ChatGPT,
Google,
Gemini,
Google AI Mode,
Anthropic,
Claude,
Perplexity,
Grok,
DeepSeek,
Mistral,
Copilot,
Meta AI,
Qwen.
How can brands improve their AEO?
Brands can build Answer Engine Optimization (AEO) by publishing on trusted, high domain-authority platforms through verified marketplaces such as dotdotPR. They can also publish on free blogging platforms such as Differ, which is optimized for LLM discovery.
Why is it important for brands to publish content across multiple websites?
Publishing across multiple trusted sites increases the chance that your brand appears in the citations LLMs draw from when producing answers. Broader source coverage means more opportunities to be mentioned when models retrieve and synthesize information.
Want this interpreted for your brand?
Explorer is the free public corpus. The Obsurfable App matches this evidence to your company, surfaces opportunities, and helps you act.