Embeddings
The first API workload after Console settlement works.
Product · Planned
Phase 3 PLANNED
One base URL. Visible compute.
The same idea as a managed inference API like AkashML, on our own chain: keep familiar OpenAI request shapes and open-weight models, but expose the job and ABA settlement behind each completion. The API is not a separate product from the Console; it is the programmatic way into it.
Quickstart preview
Designed for existing tools.
from openai import OpenAI
client = OpenAI(
base_url="https://api.abakos.ai/v1",
api_key="abk_..."
)
response = client.embeddings.create(
model="qwen-embed",
input=batch
)Preview only. API keys and models are not issued yet. Billing is planned in ABA.
01
Request
Use an OpenAI-compatible endpoint.
02
Job
The gateway creates a funded Console job in ABA.
03
Auto-match
The cheapest active, qualified provider is assigned automatically.
04
Compute
The assigned provider processes and proves the work.
05
Trace
The response includes a job reference visible in the explorer.
Open models, pay-per-token intent
Familiar shapes, open weights.
Like AkashML, the API serves open-weight models (Llama, Qwen, DeepSeek and similar) behind OpenAI-compatible endpoints. Closed frontier models are never falsely claimed. Pricing is metered per request and settled in ABA rather than a fiat balance.
Batch completions
Open-model completions with asynchronous job tracking.
Streaming chat
Ships together with Abakos Chat, not earlier: both need the same real-time job path.
Multi-turn requests, across different providers
Nothing changes on your side: send the full messages array each call, exactly like any other OpenAI-compatible provider. Underneath, each call can be matched to a different GPU provider without losing context, because the job commits to the exact context used instead of relying on any one provider to remember it. Full design in the technical docs (spec-session-continuity.md). Planned, not live yet.
One list, every update
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