You are overpaying for inference.
We fix that.
Our inference gateway serves you frontier intelligence, but at a fraction of the cost.
Relax.
Model selection is no longer your job.
Auto-tune for your inference: quality held at the right level, cost brought down automatically and continually. No pickers, no routing rules, no retry trees.
Swap in the endpoint.
Compatible with the OpenAI, Anthropic, and Gemini APIs. New base URL, new key. Nothing else changes, in any stack, framework, or harness.
We learn your traffic.
The gateway analyzes your real usage: request classes, applications, token and latency profiles.
We construct evals from real workloads.
Built around your actual use cases, not generic benchmarks. Your traffic sets the quality bar.
Every request gets the right model.
When a cheaper model proves parity on your evals, it takes over. The workload is pinned to it behind the endpoint.
The endpoint improves over time.
New models are tested against your evals as they release. Quality stays above your floor; cost per token goes down.
The inference gateway
Quality pinned.
Cost falling.
We build evals off your workloads and choose the right model at a lower cost. You start saving immediately, with receipts to prove it.
A substitution is pinned in only after parity is confirmed on your own workloads. Then the saving is permanent.
Illustrative: a representative production workload across twelve months. Each marker is a model substitution pinned in after evals confirmed parity. Live workloads on the gateway today run 75 to 90% cheaper than before the switch.
Drop-in by design
One line to switch.
Call the di-fusion model alias, or keep the model ids you already send. Both just work.
Keep your SDKs.
Native OpenAI, Anthropic, and Gemini API compatibility: streaming, tool calls, structured output.
Keep your stack.
Works with any framework, agent harness, or gateway you already run. Nothing to install, nothing to operate.
Keep your model ids.
Send di-fusion or the model IDs you already use. Substitutions happen behind the endpoint either way. You never migrate or redeploy.
from openai import OpenAI client = OpenAI( base_url="https://api.directinference.com/di/v1", api_key=os.environ["DI_API_KEY"],) resp = client.chat.completions.create( model="di-fusion", # or keep your existing ids messages=[{"role": "user", "content": "..."}],)
That’s the whole migration. Everything else stays exactly as it is.
Results
lower inference cost on live production workloads
higher accuracy on customer evals, with some use cases more than 2×
saved by our largest customers, every month
Handles high-throughput production workloads.
State-of-the-art performance on public benchmarks.
And then, every month
Delivering inference that gets cheaper over time.
You never watch a leaderboard or rerun a migration. The gateway retests new models against your evals, pins the winners, and lowers your cost per token on its own, month after month, while your code stays untouched.
For the enterprise
Built for production.
Certified security and information-management controls.
Zero-data-retention policies tailored to your compliance requirements.
Custom and on-prem deployments. Same routing, same evals, same savings.
For sophisticated AI teams and enterprises, around the world.
Per-key and per-account limits that fail closed. No overspend.
Never used to train models. Encrypted in transit and at rest.
For
Builders
Start right away
- One endpoint for OpenAI, Anthropic, and Gemini workloads
- One key, one model:
di-fusion(ordi-saver/di-maxfor direct control) - Per-app keys, telemetry, and spend caps
- Effort-level tuning per request
For
CXOs
AI spend > $40k/month
- Managed inference optimization: software plus Forward Deployed AI Engineers
- Evals constructed around your production workflows
- Custom policies: quality floors, latency, privacy, ZDR, model allowlists
- Continuous endpoint updates; on-prem available
For
Builders
Start right away
- One endpoint for OpenAI, Anthropic, and Gemini workloads
- One key, one model:
di-fusion(ordi-saver/di-max) - Per-app keys, telemetry, and spend caps
- Effort-level tuning per request
For
CXOs
AI spend > $40k/month
- Managed inference optimization: software plus Forward Deployed AI Engineers
- Evals constructed around your production workflows
- Custom policies: quality floors, latency, privacy, ZDR, model allowlists
- Continuous endpoint updates; on-prem available