Production AI you can prove.
Run the top open models, or your own, on dedicated or on-prem capacity. Every output carries a cryptographic receipt, your data is never retained or trained on, all at a fraction of closed-API cost.
Verifiable, private, and a fraction of the cost.
Production AI is moving into codebases, contracts, compliance, and autonomous agents. In those systems, a hidden model swap or opaque execution is a business risk. Ambient removes it.
Private by default
Zero data retention, no training on your data. Verification is hash-based, so there's nothing on our side to retain in the first place.
Verifiable, not trust-me
Proof of Logits on every call: proof of exactly which model and config ran. It's how a decentralized network stays safe for production traffic.
Frontier coding, a fraction of the cost
GLM 5.2 beats GPT-5.5 on SWE-bench Pro at roughly a sixth of the output cost, and you can bring your own model too.
One line to switch.
Keep your existing SDKs and agent frameworks. Point your base URL at Ambient, authenticate with an Ambient key, and your stack gets verifiable inference, no rewrite required.
Deploy it your way.
Scale from a shared endpoint to dedicated fleets or your own environment, without changing your integration.
Pay per token across 15 open models. Drop-in OpenAI- and Anthropic-compatible endpoints.
Single-tenant GPUs reserved for you, with burst headroom for unexpected traffic spikes.
Run your fine-tuned or proprietary model on dedicated hardware. Your weights always stay private.
Need it in your environment? We provision dedicated GPUs in your VPC or scope a custom on-prem deployment.
| Tier | Best for |
|---|---|
| Shared API | Pay per token across 15 open models. Drop-in OpenAI- and Anthropic-compatible endpoints. |
| Dedicated capacity | Single-tenant GPUs reserved for you, with burst headroom for unexpected traffic spikes. |
| Bring your own model | Run your fine-tuned or proprietary model on dedicated hardware. Your weights always stay private. |
| On-prem & VPC | Need it in your environment? We provision dedicated GPUs in your VPC or scope a custom on-prem deployment. |
Built for the teams that get audited.
Zero data retention
Prompts, outputs, and intermediate reasoning are never stored.
No training on your data
We strictly prohibit training on customer API traffic.
Encrypted end to end
Encryption in transit and at rest across the network.
Compliance-ready
Architected for SOC 2 Type II, HIPAA, and data-residency needs. DPA, sub-processor list, and security reports available under NDA.
Built for production
Uptime SLA and dedicated support on reserved capacity, with a named solutions engineer.
Verifiable by design
Every output carries a cryptographic receipt, so you never have to trust an operator's word.
Frontier coding, benchmarked.
On SWE-bench Pro, GLM 5.2 beats GPT-5.5 at roughly a sixth of the output cost, through the same drop-in OpenAI- and Anthropic-compatible API.
Coding Capability
SWE-bench Pro %Output Cost
$ per 1M tokens* Kimi K2.7 Code has no SWE-bench Pro score: 62.0 on Kimi Code Bench v2 (GPT-5.5 69.0, Opus 4.8 67.4). SWE-bench Pro coding scores via third-party aggregates (llm-stats / morphllm); prices per provider; June 2026.
Tell us your workload.
No calculator to fight with. Share your latency targets, context sizes, monthly volume, and confidentiality needs, and we tune the deployment and return an architecture and price. Month-to-month, no minimum-volume traps.