Inference for faster, cheaper & smarter SRE agents
Balance log volume and data protection with one API for open and fine-tuned models, serverless and in your own cloud.
- GLiNER2.5 Decide
- Qwen3 Reranker 4B
- GLiNER PII
- Your fine-tuned model
- GLM-5.3 Flash
- DeepSeek V4.1 Flash
- Gemma 4 31B
The leading engineering teams already run on open models
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datadoghq.comOct 15, 2026
Introducing Scout 1.0: A new SRE model powering Bits AI with frontier performance at a fraction of the cost
Scout powers Bits Investigation, our AI SRE… We built Scout 1.0 on GLM-5.3 Flash and post-trained it using reinforcement learning in investigation simulations.
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blog.sentry.ioJun 12, 2026
Better, faster, less wrong: Enhancing issue grouping
AI-powered issue grouping is enabled by default and live for all Sentry customers… We then trained lightonai/modernbert-embed-large on these labeled stacktrace pairs.
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splunk.comNov 24, 2025
Introducing the Cisco Time Series Model: Unlock Machine Data Insights with Gen AI
…the Cisco Time Series Model, an open-weight time series foundation model that can be easily deployed for reliable forecasting across observability and security operations…
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engineering.fb.comJun 24, 2024
Leveraging AI for efficient incident response
The biggest lever to achieving 42% accuracy was fine-tuning a Llama 2 (7B) model using historical investigations for which we knew the underlying root cause.
Keep investigating when every alert fires at once
Each job runs on a model sized for it, next to your app. When an outage sets off an alert storm, you add GPUs to your own cluster instead of waiting on a provider’s quota.
Run a month of incident work for 91% less
Task-sized open models do each job for a fraction of a frontier API’s price. Each line is priced from its task page, at an illustrative month’s volume.
- Route alerts to the owning team
$1,197$175 Labelled correctly: SIE 87.5% · GPT-6 93.8% - Rerank runbooks and past incidents
$12,500$744 Open model more accurate - Turn incident timelines into reports
$1,536$145 More values exactly right - Redact customer data before an LLM reads it
$1,757$364 Less personal data exposed
Tuned on your task, an open model beats the frontier APIs at it
How each result was measured
- Business questions to chart specs
- Superlinked’s gd-1, August 2026: Gemma 4 31B tuned on generated questions, then scored once on a BI assistant’s 875-question benchmark, counting the answers that return the reference data. GPT-5.6 Sol at maximum reasoning was the strongest API. The training questions came from the benchmark’s own generator, which is the home advantage tuning on your own task gives.
- Possible fraud in bank messages
- BANKING77: 3,076 customer messages to a bank, 240 of them reporting a payment they don’t recognise or a card or phone lost, stolen or compromised, none among the 9,993 the model was tuned on. The share of those reports caught while 95% of the messages flagged are reports, each model’s threshold set on the scored messages; GPT-6 Luna at low effort.
- Contract-clause review
- LexGLUE UNFAIR-ToS: 3,882 Terms of Service clauses labelled by lawyers, none of them among the 5,532 the model was tuned on. F1 on the unfair class; each API at its best setting, with the lawyers’ annotation rulebook in its prompt.
Meet the vendor requirements of your engineering customers
| What the rule asks | SIE Serverless | SIE in your cloud |
|---|---|---|
Use only sub-processors your customer authorised EU GDPR Article 28“The processor shall not engage another processor without prior specific or general written authorisation of the controller.” | Superlinked is a sub-processor Your customers authorise us like any vendor | No new sub-processor Superlinked has no access unless you grant it |
State where logs are processed and stored EU DORA Article 30, from Jan 2025A financial customer’s contract must state “the locations, namely the regions or countries, where the contracted or subcontracted functions and ICT services are to be provided and where data is to be processed, including the storage location”. | US or EU region Requests stay in the region you choose | Inside your network Your cloud account, in your region |
Assess the risk of each vendor AICPA Trust Services Criteria, CC9.2“The entity assesses and manages risks associated with vendors and business partners.” | SOC2 Type 2 report For your customers’ vendor reviews | Code and weights to inspect Open source, on versions you pin |
Limit lock-in to any one supplier EU NIS2 rules, Oct 2024Supplier criteria must include “the ability of the relevant entities to diversify sources of supply and limit vendor lock-in, where applicable”. | Same API in your cloud The same models and calls move in-house | Free to self-host Apache 2.0 engine, runs without us |
Start on SIE Serverless, or run it under your control
SIE Serverless
We host the models; you call one API.
- No idle GPUs, pay for what you use
- Fits your stack: SDK, API, CLI, MCP
- Self-host the same open-source engine
- SOC 2 Type 2, US or EU data residency
- Create an account to try Serverless
SIE Cluster
Kubernetes in your own cloud, backed by our engineers.
- Terraform modules for your cloud
- Apache-2.0, same engine as Serverless
- GPU workers scale to zero between jobs
- Separate worker pools for each tenant
Deploy SIE to our AWS account with the superlinked/sie/aws Terraform module. Docs: superlinked.com/docs/deployment.Deploy SIE to our Google Cloud project with the superlinked/sie/google Terraform module. Docs: superlinked.com/docs/deployment.Deploy SIE to our Azure subscription with the superlinked/sie/azure Terraform module. Docs: superlinked.com/docs/deployment.Deploy SIE to our Alibaba Cloud account with the superlinked/sie/alicloud Terraform module. Docs: superlinked.com/docs/deployment. SIE Local
The open-source server on your own machine.
- Runs on your NVIDIA GPU
- One container, no cluster
- Process your data fully offline
- Same SDK and model IDs as Serverless
docker run --gpus all -p 8080:8080 ghcr.io/superlinked/sie-server:latest-cuda12-default Start from your task or your industry
Search and RAG
Documents
Text and LLM apps
- Chat Open LLM, OpenAI-style API
- Coding agents Tool calls for your coding agent
- Structured output Schema-valid JSON
- Classify text Your labels, no training
- Yes/no decisions Automate workflow checks
- Route requests Cheap or strong LLM, per request
- Extract names and dates People, orgs, places
- Knowledge graph Entities and their relations
- Translate text English into many languages