jurniti vs Progress AI Observability: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of jurniti and Progress AI Observability — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
jurniti
jurniti
Managed 24/7 hosting for coding agents, each running in its own Firecracker microVM with your own model keys and no token markup.
Key features
- Firecracker microVM Isolation: Every agent runs in its own KVM-backed virtual machine with hardware-enforced tenant isolation instead of a shared-kernel container.
- Bring Your Own Key: OpenRouter, OpenAI or Anthropic keys live only inside the customer's VM — jurniti never sees them, never proxies calls and never marks up model spend.
- Multi-Harness Support: Runs Claude Code, Codex CLI, OpenClaw, Hermes, OpenCode, Devin CLI, Mastra and Pi, each in its own dedicated microVM.
- Fleet CLI: A jurniti command-line tool to boot agents, list fleet status, dispatch work and copy results back, so the whole fleet is managed from a terminal.
- Swarm Runtime: Boots dozens of isolated microVM workers at once and dispatches the same brief to every worker, with results collected in a single command.
- Flat Per-VM or Hourly Billing: A flat monthly or annual price per agent VM, or per-second metered On-Demand and Spot pricing for bursty workloads, with prepaid credits.
- Automated Provisioning: Payment triggers a magic-link sign-in and an auto-provisioner that has a live microVM running in about three minutes with no human in the loop.
- Custom Subdomain and Sidecars: Pro tiers add a custom subdomain, alongside separate microVM services for multi-agent communication and long-term agent memory.
Best for
- Always-On Coding Agents: Keeping a Claude Code or Codex agent working on a backlog overnight without leaving a laptop running.
- Secure Key Handling: Running agents for a team that cannot let model API keys leave its own infrastructure boundary.
- Parallel Agent Fleets: Dispatching one brief to fifty isolated workers to compare approaches or parallelize a large refactor.
- Bursty Batch Work: Using per-second Spot or On-Demand VMs for agents that only run a few hours a day, paying only for active runtime.
- Self-Hosting Alternative: Replacing hand-rolled VPS setups for open-source agent harnesses like Hermes, OpenClaw or OpenCode.
- Long-Running Agent Memory: Pairing an agent VM with a dedicated memory microVM so knowledge persists between sessions.
Progress AI Observability
Progress Software (Telerik)
Progress AI Observability traces, debugs, cost-tracks and evaluates AI agents in production for .NET, Python and JavaScript.
Key features
- AI Trace Explorer: Capture every span across prompts, model calls, tool calls and retrieval steps, with latency, tokens and outputs.
- Workflow Debugging: Diagnose failed spans, skipped tools, retries and cascading failures with agent-specific debugging context.
- Cost Analysis: Attribute LLM spend to specific models, providers, agents and workflows so teams can optimize before it scales.
- LLM-as-a-Judge Evaluations: Run quality, usefulness and policy-alignment scoring on captured traces and compare prompt/model changes.
- Multi-Language SDK: Instrument .NET, Python and JavaScript apps with a few lines of code — first trace in under 5 minutes.
- Datasets & Experiments: Curate real traces into datasets and run repeatable experiments against new prompts or models.
- Enterprise Governance: SSO, retention controls, data residency options and audit trails for regulated teams.
Best for
- Agent Failure Debugging: Cut root-cause analysis from hours to minutes by tracing where a run broke across prompts, retrieval and tools.
- LLM Cost Governance: Identify token-hungry patterns, expensive models and retry loops so finance and engineering can budget accurately.
- Quality Regression Testing: Score outputs with LLM judges before and after prompt/model changes to catch quality drops pre-release.
- RAG Pipeline Tuning: Spot bad retrieval or stale context inside multi-step RAG workflows and iterate with real production evidence.
- Enterprise AI Governance: Maintain trace history, evaluation records and access controls needed to scale AI to regulated business lines.
- Multi-Agent Observability: Compare behavior, cost and quality across agents, environments and providers from a single dashboard.